kalman_filter_lane-destection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from collections import defaultdict\n",
    "from scipy.linalg import block_diag\n",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LaneTracker:\n",
    "    def __init__(self, n_lanes, proc_noise_scale, meas_noise_scale, process_cov_parallel=0, proc_noise_type='white'):\n",
    "        self.n_lanes = n_lanes\n",
    "        self.meas_size = 4 * self.n_lanes\n",
    "        self.state_size = self.meas_size * 2\n",
    "        self.contr_size = 0\n",
    "\n",
    "        self.kf = cv2.KalmanFilter(self.state_size, self.meas_size, self.contr_size)\n",
    "        self.kf.transitionMatrix = np.eye(self.state_size, dtype=np.float32)\n",
    "        self.kf.measurementMatrix = np.zeros((self.meas_size, self.state_size), np.float32)\n",
    "        for i in range(self.meas_size):\n",
    "            self.kf.measurementMatrix[i, i*2] = 1\n",
    "\n",
    "        if proc_noise_type == 'white':\n",
    "            block = np.matrix([[0.25, 0.5],\n",
    "                               [0.5, 1.]], dtype=np.float32)\n",
    "            self.kf.processNoiseCov = block_diag(*([block] * self.meas_size)) * proc_noise_scale\n",
    "        if proc_noise_type == 'identity':\n",
    "            self.kf.processNoiseCov = np.eye(self.state_size, dtype=np.float32) * proc_noise_scale\n",
    "        for i in range(0, self.meas_size, 2):\n",
    "            for j in range(1, self.n_lanes):\n",
    "                self.kf.processNoiseCov[i, i+(j*8)] = process_cov_parallel\n",
    "                self.kf.processNoiseCov[i+(j*8), i] = process_cov_parallel\n",
    "\n",
    "        self.kf.measurementNoiseCov = np.eye(self.meas_size, dtype=np.float32) * meas_noise_scale\n",
    "\n",
    "        self.kf.errorCovPre = np.eye(self.state_size)\n",
    "\n",
    "        self.meas = np.zeros((self.meas_size, 1), np.float32)\n",
    "        self.state = np.zeros((self.state_size, 1), np.float32)\n",
    "\n",
    "        self.first_detected = False\n",
    "\n",
    "    def _update_dt(self, dt):\n",
    "        for i in range(0, self.state_size, 2):\n",
    "            self.kf.transitionMatrix[i, i+1] = dt\n",
    "\n",
    "    def _first_detect(self, lanes):\n",
    "        for l, i in zip(lanes, range(0, self.state_size, 8)):\n",
    "            self.state[i:i+8:2, 0] = l\n",
    "        self.kf.statePost = self.state\n",
    "        self.first_detected = True\n",
    "\n",
    "    def update(self, lanes):\n",
    "        if self.first_detected:\n",
    "            for l, i in zip(lanes, range(0, self.meas_size, 4)):\n",
    "                if l is not None:\n",
    "                    self.meas[i:i+4, 0] = l\n",
    "            self.kf.correct(self.meas)\n",
    "        else:\n",
    "            if lanes.count(None) == 0:\n",
    "                self._first_detect(lanes)\n",
    "\n",
    "    def predict(self, dt):\n",
    "        if self.first_detected:\n",
    "            self._update_dt(dt)\n",
    "            state = self.kf.predict()\n",
    "            lanes = []\n",
    "            for i in range(0, len(state), 8):\n",
    "                lanes.append((state[i], state[i+2], state[i+4], state[i+6]))\n",
    "                print(lanes)\n",
    "            return lanes\n",
    "        else:\n",
    "            return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hsv_filter(image, min_val_y, max_val_y,  min_val_w, max_val_w):\n",
    "    \"\"\"\n",
    "    A function returning a mask for pixels within min_val - max_val range\n",
    "    Inputs:\n",
    "    - image - a BGR image you want to apply function on\n",
    "    - min_val_y - array of shape (3,) giving minumum HSV values for yellow color\n",
    "    - max_val_y - array of shape (3,) giving maximum HSV values for yellow color\n",
    "    - min_val_w - array of shape (3,) giving minumum HSV values for white color\n",
    "    - max_val_w - array of shape (3,) giving maximum HSV values for white color\n",
    "    Returns:\n",
    "    - img_filtered - image of pixels being in given threshold\n",
    "    \n",
    " gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    " mask = cv2.inRange(frame,lower_color_bounds,upper_color_bounds )\n",
    " mask_rgb = cv2.cvtColor(mask,cv2.COLOR_GRAY2BGR)\n",
    " frame = frame & mask_rgb\n",
    "\n",
    "    \"\"\"\n",
    "    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
    "    mask_yellow = cv2.inRange(hsv, min_val_y, max_val_y)\n",
    "    mask_white = cv2.inRange(hsv, min_val_w, max_val_w)\n",
    "    mask = cv2.bitwise_or(mask_yellow, mask_white)\n",
    "    img_filtered = cv2.bitwise_and(image, image, mask=mask)\n",
    "    \n",
    "    return img_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LaneDetector:\n",
    "    def __init__(self, road_horizon, prob_hough=True):\n",
    "        self.prob_hough = prob_hough\n",
    "        self.vote = 50\n",
    "        self.roi_theta = 0.59\n",
    "        self.road_horizon = road_horizon\n",
    "\n",
    "    def _standard_hough(self, img, init_vote):\n",
    "        # Hough transform wrapper to return a list of points like PHough does\n",
    "        lines = cv2.HoughLines(img, 1, np.pi/180, init_vote)\n",
    "        points = [[]]\n",
    "        for l in lines:\n",
    "            for rho, theta in l:\n",
    "                a = np.cos(theta)\n",
    "                b = np.sin(theta)\n",
    "                x0 = a*rho\n",
    "                y0 = b*rho\n",
    "                x1 = int(x0 + 1000*(-b))\n",
    "                y1 = int(y0 + 1000*a)\n",
    "                x2 = int(x0 - 1000*(-b))\n",
    "                y2 = int(y0 - 1000*a)\n",
    "                points[0].append((x1, y1, x2, y2))\n",
    "                print(points)\n",
    "        return points\n",
    "\n",
    "    def _base_distance(self, x1, y1, x2, y2, width):\n",
    "        # compute the point where the give line crosses the base of the frame\n",
    "        # return distance of that point from center of the frame\n",
    "        if x2 == x1:\n",
    "            return (width*0.5) - x1\n",
    "        m = (y2-y1)/(x2-x1)\n",
    "        c = y1 - m*x1\n",
    "        base_cross = -c/m\n",
    "        return (width*0.5) - base_cross\n",
    "\n",
    "    def _scale_line(self, x1, y1, x2, y2, frame_height):\n",
    "        # scale the farthest point of the segment to be on the drawing horizon\n",
    "        if x1 == x2:\n",
    "            if y1 < y2:\n",
    "                y1 = self.road_horizon\n",
    "                y2 = frame_height\n",
    "                return x1, y1, x2, y2\n",
    "            else:\n",
    "                y2 = self.road_horizon\n",
    "                y1 = frame_height\n",
    "                return x1, y1, x2, y2\n",
    "        if y1 < y2:\n",
    "            m = (y1-y2)/(x1-x2)\n",
    "            x1 = ((self.road_horizon-y1)/m) + x1\n",
    "            y1 = self.road_horizon\n",
    "            x2 = ((frame_height-y2)/m) + x2\n",
    "            y2 = frame_height\n",
    "        else:\n",
    "            m = (y2-y1)/(x2-x1)\n",
    "            x2 = ((self.road_horizon-y2)/m) + x2\n",
    "            y2 = self.road_horizon\n",
    "            x1 = ((frame_height-y1)/m) + x1\n",
    "            y1 = frame_height\n",
    "        return x1, y1, x2, y2\n",
    "\n",
    "    def detect(self, frame):\n",
    "        \n",
    "        img= frame\n",
    "        \n",
    "        min_val_y = np.array([15,80,190])\n",
    "        max_val_y = np.array([30,255,255])\n",
    "        min_val_w = np.array([0,0,120])\n",
    "        max_val_w = np.array([255, 255, 255])\n",
    "        #defining color thresholds\n",
    "        '''min_val_y = np.array([50,120,130])\n",
    "        max_val_y = np.array([20,230,230])\n",
    "        min_val_w = np.array([0,0,150])\n",
    "        max_val_w = np.array([240,240,240])'''\n",
    "\n",
    "\n",
    "        roiy_end = frame.shape[0]\n",
    "        roix_end = frame.shape[1]\n",
    "        roi = img[self.road_horizon:roiy_end-20, 20:roix_end-20]\n",
    "        #blur = cv2.medianBlur(roi, 5)\n",
    "        blur = cv2.bilateralFilter(roi, 9, 21, 21)\n",
    "        hsv = hsv_filter(blur, min_val_y, max_val_y,  min_val_w, max_val_w)\n",
    "        cv2.imshow('hsv', hsv)\n",
    "   \n",
    "\n",
    "        \n",
    "        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n",
    "        #roiy_end = frame.shape[0]\n",
    "        #roix_end = frame.shape[1]\n",
    "        roi_gray = gray[self.road_horizon:roiy_end-20, 20:roix_end-20]\n",
    "        blur_gray = cv2.bilateralFilter(gray, 4, 17, 17)#cv2.medianBlur(roi, 11)\n",
    "        th, thld = cv2.threshold(blur_gray, 130, 255, cv2.THRESH_BINARY)# binary threshold\n",
    "        cv2.imshow('thld', thld)\n",
    "        \n",
    "        contours = cv2.Canny(blur, 60, 120)#canny edges\n",
    "        cv2.imshow('canny', contours)\n",
    "        \n",
    "\n",
    "        \n",
    "        if self.prob_hough:\n",
    "            lines = cv2.HoughLinesP(contours, 1, np.pi/180, self.vote, minLineLength=30, maxLineGap=100)\n",
    "        else:\n",
    "            lines = self.standard_hough(contours, self.vote)\n",
    "\n",
    "        if lines is not None:\n",
    "            # find nearest lines to center\n",
    "            lines = lines+np.array([0, self.road_horizon, 0, self.road_horizon]).reshape((1, 1, 4))  # scale points from ROI coordinates to full frame coordinates\n",
    "            left_bound = None\n",
    "            right_bound = None\n",
    "            for l in lines:\n",
    "                # find the rightmost line of the left half of the frame and the leftmost line of the right half\n",
    "                for x1, y1, x2, y2 in l:\n",
    "                    theta = np.abs(np.arctan2((y2-y1), (x2-x1)))  # line angle WRT horizon\n",
    "                    if theta > self.roi_theta:  # ignore lines with a small angle WRT horizon\n",
    "                        dist = self._base_distance(x1, y1, x2, y2, frame.shape[1])\n",
    "                        if left_bound is None and dist < 0:\n",
    "                            left_bound = (x1, y1, x2, y2)\n",
    "                            left_dist = dist\n",
    "                        elif right_bound is None and dist > 0:\n",
    "                            right_bound = (x1, y1, x2, y2)\n",
    "                            right_dist = dist\n",
    "                        elif left_bound is not None and 0 > dist > left_dist:\n",
    "                            left_bound = (x1, y1, x2, y2)\n",
    "                            left_dist = dist\n",
    "                        elif right_bound is not None and 0 < dist < right_dist:\n",
    "                            right_bound = (x1, y1, x2, y2)\n",
    "                            right_dist = dist\n",
    "            if left_bound is not None:\n",
    "                left_bound = self._scale_line(left_bound[0], left_bound[1], left_bound[2], left_bound[3], frame.shape[0])\n",
    "            if right_bound is not None:\n",
    "                right_bound = self._scale_line(right_bound[0], right_bound[1], right_bound[2], right_bound[3], frame.shape[0])\n",
    "\n",
    "            return [left_bound, right_bound]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gamma_correction_auto(RGBimage,equalizeHist = False): #0.35\n",
    "    originalFile = RGBimage.copy()\n",
    "    red = RGBimage[:,:,2]\n",
    "    green = RGBimage[:,:,1]\n",
    "    blue = RGBimage[:,:,0]\n",
    "    \n",
    "    forLuminance = cv2.cvtColor(originalFile,cv2.COLOR_BGR2YUV)\n",
    "    Y = forLuminance[:,:,0]\n",
    "    totalPix = vidsize[0]* vidsize[1]\n",
    "    summ = np.sum(Y[:,:])\n",
    "    Yaverage = np.divide(totalPix,summ)\n",
    "    #Yclipped = np.clip(Yaverage,0,1)\n",
    "    epsilon = 1.19209e-007\n",
    "    correct_param = np.divide(-0.3,np.log10([Yaverage + epsilon]))\n",
    "    correct_param = 0.7 - correct_param \n",
    "\n",
    "    red = red/255.0\n",
    "    red = cv2.pow(red, correct_param)\n",
    "    red = np.uint8(red*255)\n",
    "    if equalizeHist:\n",
    "        red = cv2.equalizeHist(red)\n",
    "    \n",
    "    green = green/255.0\n",
    "    green = cv2.pow(green, correct_param)\n",
    "    green = np.uint8(green*255)\n",
    "    if equalizeHist:\n",
    "        green = cv2.equalizeHist(green)\n",
    "        \n",
    "    \n",
    "    blue = blue/255.0\n",
    "    blue = cv2.pow(blue, correct_param)\n",
    "    blue = np.uint8(blue*255)\n",
    "    if equalizeHist:\n",
    "        blue = cv2.equalizeHist(blue)\n",
    "    \n",
    "\n",
    "    output = cv2.merge((blue,green,red))\n",
    "    #print(correct_param)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#capturing video\n",
    "cap = cv2.VideoCapture('C:/Users/DarkLord/Downloads/giscle internship/projects/Lane detection_Giscle/input/videod.mp4')# it will capture videos from camara\n",
    "\n",
    "vidsize = (640,480,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def region_of_interest(img, vertices):\n",
    "    # Define a blank matrix that matches the image height/width.\n",
    "    mask = np.zeros_like(img)\n",
    "    \n",
    "    # Retrieve the number of color channels of the image.\n",
    "    #channel_count = img.shape[2]\n",
    "    \n",
    "    # color used to fill polygon\n",
    "    match_mask_color = 255\n",
    "      \n",
    "    # Fill the polygon with white\n",
    "    cv2.fillPoly(mask, vertices, (255,255,255))\n",
    "    \n",
    "    # Returning the image only where mask pixels match\n",
    "    masked_image = cv2.bitwise_and(img, mask)\n",
    "    \n",
    "    return masked_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "480 640\n",
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      "480 640\n",
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      "480 640\n",
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      "480 640\n",
      "[(array([ 84.88583374], dtype=float32), array([ 480.], dtype=float32), array([ 210.45973206], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 90.26305389], dtype=float32), array([ 480.], dtype=float32), array([ 215.92802429], dtype=float32), array([ 330.], dtype=float32))]\n",
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     "output_type": "stream",
     "text": [
      "480 640\n",
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      "480 640\n",
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      "480 640\n",
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      "480 640\n",
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      "480 640\n",
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      "480 640\n",
      "[(array([ 123.34339142], dtype=float32), array([ 480.], dtype=float32), array([ 258.65725708], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 125.54804993], dtype=float32), array([ 480.], dtype=float32), array([ 262.53930664], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 128.095047], dtype=float32), array([ 480.], dtype=float32), array([ 266.45559692], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 130.53320312], dtype=float32), array([ 480.], dtype=float32), array([ 270.19290161], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 132.93966675], dtype=float32), array([ 480.], dtype=float32), array([ 273.80133057], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 135.05134583], dtype=float32), array([ 480.], dtype=float32), array([ 277.11340332], dtype=float32), array([ 330.], dtype=float32))]\n",
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      "480 640\n",
      "[(array([ 136.96302795], dtype=float32), array([ 480.], dtype=float32), array([ 280.20504761], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 136.96302795], dtype=float32), array([ 480.], dtype=float32), array([ 280.20504761], dtype=float32), array([ 330.], dtype=float32)), (array([ 185.34921265], dtype=float32), array([ 330.00109863], dtype=float32), array([ 378.34100342], dtype=float32), array([ 480.00158691], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 138.82063293], dtype=float32), array([ 480.], dtype=float32), array([ 283.20608521], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 138.82063293], dtype=float32), array([ 480.], dtype=float32), array([ 283.20608521], dtype=float32), array([ 330.], dtype=float32)), (array([ 194.771698], dtype=float32), array([ 330.00097656], dtype=float32), array([ 388.02145386], dtype=float32), array([ 480.00146484], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 140.45291138], dtype=float32), array([ 480.], dtype=float32), array([ 285.96072388], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 140.45291138], dtype=float32), array([ 480.], dtype=float32), array([ 285.96072388], dtype=float32), array([ 330.], dtype=float32)), (array([ 203.58772278], dtype=float32), array([ 330.00085449], dtype=float32), array([ 397.08105469], dtype=float32), array([ 480.00134277], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 142.24139404], dtype=float32), array([ 480.], dtype=float32), array([ 288.82318115], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 142.24139404], dtype=float32), array([ 480.], dtype=float32), array([ 288.82318115], dtype=float32), array([ 330.], dtype=float32)), (array([ 212.5496521], dtype=float32), array([ 330.00076294], dtype=float32), array([ 406.32388306], dtype=float32), array([ 480.0012207], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 143.73834229], dtype=float32), array([ 480.], dtype=float32), array([ 291.38827515], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 143.73834229], dtype=float32), array([ 480.], dtype=float32), array([ 291.38827515], dtype=float32), array([ 330.], dtype=float32)), (array([ 220.93829346], dtype=float32), array([ 330.00067139], dtype=float32), array([ 414.6831665], dtype=float32), array([ 480.00109863], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 144.95671082], dtype=float32), array([ 480.], dtype=float32), array([ 293.97790527], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 144.95671082], dtype=float32), array([ 480.], dtype=float32), array([ 293.97790527], dtype=float32), array([ 330.], dtype=float32)), (array([ 229.09545898], dtype=float32), array([ 330.00057983], dtype=float32), array([ 422.84106445], dtype=float32), array([ 480.00097656], dtype=float32))]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 640\n",
      "[(array([ 146.06089783], dtype=float32), array([ 480.], dtype=float32), array([ 296.71209717], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 146.06089783], dtype=float32), array([ 480.], dtype=float32), array([ 296.71209717], dtype=float32), array([ 330.], dtype=float32)), (array([ 237.13389587], dtype=float32), array([ 330.00048828], dtype=float32), array([ 430.7545166], dtype=float32), array([ 480.00085449], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 147.17062378], dtype=float32), array([ 480.], dtype=float32), array([ 299.13659668], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 147.17062378], dtype=float32), array([ 480.], dtype=float32), array([ 299.13659668], dtype=float32), array([ 330.], dtype=float32)), (array([ 244.93132019], dtype=float32), array([ 330.00039673], dtype=float32), array([ 438.42681885], dtype=float32), array([ 480.00073242], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 148.29457092], dtype=float32), array([ 480.], dtype=float32), array([ 301.54501343], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 148.29457092], dtype=float32), array([ 480.], dtype=float32), array([ 301.54501343], dtype=float32), array([ 330.], dtype=float32)), (array([ 252.62055969], dtype=float32), array([ 330.00030518], dtype=float32), array([ 446.05160522], dtype=float32), array([ 480.00061035], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 149.11146545], dtype=float32), array([ 480.], dtype=float32), array([ 303.61227417], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 149.11146545], dtype=float32), array([ 480.], dtype=float32), array([ 303.61227417], dtype=float32), array([ 330.], dtype=float32)), (array([ 259.58868408], dtype=float32), array([ 330.00024414], dtype=float32), array([ 452.59280396], dtype=float32), array([ 480.00048828], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 150.21182251], dtype=float32), array([ 480.], dtype=float32), array([ 305.94082642], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 150.21182251], dtype=float32), array([ 480.], dtype=float32), array([ 305.94082642], dtype=float32), array([ 330.], dtype=float32)), (array([ 267.00119019], dtype=float32), array([ 330.00018311], dtype=float32), array([ 460.00570679], dtype=float32), array([ 480.00036621], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 150.98931885], dtype=float32), array([ 480.], dtype=float32), array([ 307.91030884], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 150.98931885], dtype=float32), array([ 480.], dtype=float32), array([ 307.91030884], dtype=float32), array([ 330.], dtype=float32)), (array([ 273.51712036], dtype=float32), array([ 330.00012207], dtype=float32), array([ 466.0958252], dtype=float32), array([ 480.00024414], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 151.74937439], dtype=float32), array([ 480.], dtype=float32), array([ 309.83355713], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 151.74937439], dtype=float32), array([ 480.], dtype=float32), array([ 309.83355713], dtype=float32), array([ 330.], dtype=float32)), (array([ 280.05444336], dtype=float32), array([ 330.00006104], dtype=float32), array([ 471.89642334], dtype=float32), array([ 480.00015259], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 152.6834259], dtype=float32), array([ 480.], dtype=float32), array([ 311.91983032], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 152.6834259], dtype=float32), array([ 480.], dtype=float32), array([ 311.91983032], dtype=float32), array([ 330.], dtype=float32)), (array([ 286.7868042], dtype=float32), array([ 330.], dtype=float32), array([ 478.02606201], dtype=float32), array([ 480.00006104], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 153.62643433], dtype=float32), array([ 480.], dtype=float32), array([ 314.00085449], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 153.62643433], dtype=float32), array([ 480.], dtype=float32), array([ 314.00085449], dtype=float32), array([ 330.], dtype=float32)), (array([ 293.42211914], dtype=float32), array([ 329.99993896], dtype=float32), array([ 484.05078125], dtype=float32), array([ 479.99996948], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 153.37811279], dtype=float32), array([ 480.], dtype=float32), array([ 315.45220947], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 153.37811279], dtype=float32), array([ 480.], dtype=float32), array([ 315.45220947], dtype=float32), array([ 330.], dtype=float32)), (array([ 298.94152832], dtype=float32), array([ 329.99987793], dtype=float32), array([ 488.61672974], dtype=float32), array([ 479.99987793], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 152.19532776], dtype=float32), array([ 480.], dtype=float32), array([ 316.93197632], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 152.19532776], dtype=float32), array([ 480.], dtype=float32), array([ 316.93197632], dtype=float32), array([ 330.], dtype=float32)), (array([ 303.56713867], dtype=float32), array([ 329.99981689], dtype=float32), array([ 492.54049683], dtype=float32), array([ 479.99981689], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 151.72494507], dtype=float32), array([ 480.], dtype=float32), array([ 318.0539856], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 151.72494507], dtype=float32), array([ 480.], dtype=float32), array([ 318.0539856], dtype=float32), array([ 330.], dtype=float32)), (array([ 308.74838257], dtype=float32), array([ 329.99975586], dtype=float32), array([ 496.72918701], dtype=float32), array([ 479.99972534], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 150.9344635], dtype=float32), array([ 480.], dtype=float32), array([ 318.78659058], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 150.9344635], dtype=float32), array([ 480.], dtype=float32), array([ 318.78659058], dtype=float32), array([ 330.], dtype=float32)), (array([ 312.67501831], dtype=float32), array([ 329.99969482], dtype=float32), array([ 500.03277588], dtype=float32), array([ 479.99966431], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 150.26734924], dtype=float32), array([ 480.], dtype=float32), array([ 319.61331177], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 150.26734924], dtype=float32), array([ 480.], dtype=float32), array([ 319.61331177], dtype=float32), array([ 330.], dtype=float32)), (array([ 317.2479248], dtype=float32), array([ 329.99963379], dtype=float32), array([ 503.41983032], dtype=float32), array([ 479.99960327], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 149.52246094], dtype=float32), array([ 480.], dtype=float32), array([ 320.29855347], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 149.52246094], dtype=float32), array([ 480.], dtype=float32), array([ 320.29855347], dtype=float32), array([ 330.], dtype=float32)), (array([ 321.16217041], dtype=float32), array([ 329.99960327], dtype=float32), array([ 506.51528931], dtype=float32), array([ 479.99957275], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 148.83668518], dtype=float32), array([ 480.], dtype=float32), array([ 321.0098877], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 148.83668518], dtype=float32), array([ 480.], dtype=float32), array([ 321.0098877], dtype=float32), array([ 330.], dtype=float32)), (array([ 325.28088379], dtype=float32), array([ 329.99957275], dtype=float32), array([ 509.0045166], dtype=float32), array([ 479.99951172], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 148.11972046], dtype=float32), array([ 480.], dtype=float32), array([ 321.63879395], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 148.11972046], dtype=float32), array([ 480.], dtype=float32), array([ 321.63879395], dtype=float32), array([ 330.], dtype=float32)), (array([ 329.12225342], dtype=float32), array([ 329.99954224], dtype=float32), array([ 511.52102661], dtype=float32), array([ 479.9994812], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 146.60765076], dtype=float32), array([ 480.], dtype=float32), array([ 322.08673096], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 146.60765076], dtype=float32), array([ 480.], dtype=float32), array([ 322.08673096], dtype=float32), array([ 330.], dtype=float32)), (array([ 332.84915161], dtype=float32), array([ 329.99951172], dtype=float32), array([ 513.69952393], dtype=float32), array([ 479.99945068], dtype=float32))]\n",
      "480 640\n",
      "[(array([ 145.2505188], dtype=float32), array([ 480.], dtype=float32), array([ 322.60366821], dtype=float32), array([ 330.], dtype=float32))]\n",
      "[(array([ 145.2505188], dtype=float32), array([ 480.], dtype=float32), array([ 322.60366821], dtype=float32), array([ 330.], dtype=float32)), (array([ 336.6293335], dtype=float32), array([ 329.9994812], dtype=float32), array([ 515.70092773], dtype=float32), array([ 479.99942017], dtype=float32))]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "#defining corners for ROI\n",
    "height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n",
    "width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)\n",
    "\n",
    "h1 = int(height)\n",
    "w1 = int(width)\n",
    "\n",
    "topLeftPt = (width*(2/5), height*(3.0/5))\n",
    "topRightPt = (width*(3/5), height*(3.0/5))\n",
    "\n",
    "region_of_interest_points = [\n",
    "(0, height),\n",
    "#(0, height*(4.0/5)),\n",
    "topLeftPt,\n",
    "topRightPt,\n",
    "#(width, height*(4.0/5)),\n",
    "(width, height),\n",
    "]\n",
    "\n",
    "#defining color thresholds\n",
    "min_val_y = np.array([50, 120, 130])\n",
    "max_val_y = np.array([20, 230, 230])\n",
    "min_val_w = np.array([150, 150, 150])\n",
    "max_val_w = np.array([240, 240, 240])\n",
    "\n",
    "ticks = 0\n",
    "\n",
    "lt = LaneTracker(2, 0.1, 1500)\n",
    "ld = LaneDetector(330)\n",
    "\n",
    "\n",
    "\n",
    "# Define the codec and create VideoWriter object\n",
    "#fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
    "#out = cv2.VideoWriter('output/result.avi',fourcc, 20.0, (int(cap.get(3)),(int(cap.get(4)))))\n",
    "\n",
    "while True:\n",
    "    precTick = ticks\n",
    "    ticks = cv2.getTickCount()\n",
    "    dt = (ticks - precTick) / cv2.getTickFrequency()\n",
    "    ret, frame = cap.read()\n",
    "    if ret:\n",
    "        #cv2.imshow('original',frame)\n",
    "        #gamma = gamma_correction_auto(frame,equalizeHist = False) #0.2\n",
    "        #cv2.imshow('gamma', gamma)\n",
    "        #cropped = region_of_interest(frame, np.array([region_of_interest_points], np.int32))\n",
    "        #cv2.imshow('cropped', cropped)\n",
    "        #bilateral = cv2.bilateralFilter(cropped, 9, 80, 80)\n",
    "        #cv2.imshow('bilateral', bilateral)\n",
    "        #hsv = hsv_filter(cropped, min_val_y, max_val_y,  min_val_w, max_val_w)\n",
    "        print(h1,w1)   \n",
    "        predicted = lt.predict(dt)\n",
    "\n",
    "        lanes = ld.detect(frame)\n",
    "        \n",
    "        if predicted is not None:\n",
    "            cv2.line(frame, (predicted[0][0], predicted[0][1]), (predicted[0][2], predicted[0][3]), (0, 0, 255), 2)\n",
    "            cv2.line(frame, (predicted[1][0], predicted[1][1]), (predicted[1][2], predicted[1][3]), (0, 0, 255), 2)\n",
    "           \n",
    "        lt.update(lanes)\n",
    "        \n",
    "        #cv2.imshow('hsv', hsv)\n",
    "        #canny = cv2.Canny(hsv, 80, 255) #100\n",
    "        #cv2.imshow('canny', canny)\n",
    "        #hough, lines = hough_transform(frame, canny, 11, discard_horizontal = 0.7) #14 0.4\n",
    "        #cv2.imshow('hough', hough)\n",
    "        #_, frame = cap.read()\n",
    "        #final = clustering(lines, frame, np.array([region_of_interest_points], np.int32), eps = 0.5, min_samples = 4)\n",
    "        \n",
    "       \n",
    "        #out.write(frame)# write frames in the disk\n",
    "        final = cv2.rectangle(frame,(20,315),(w1-20,h1),(0,255,0),2)\n",
    "        cv2.imshow('final', final)\n",
    "        \n",
    "\n",
    "        \n",
    "        k = cv2.waitKey(29) & 0xFF\n",
    "        if k == ord('q'):\n",
    "            break\n",
    "            \n",
    "cap.release()\n",
    "#out.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
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