MV-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce915992",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob as g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d95aefd",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = g('np_outputs/points*npy')\n",
    "len(files)\n",
    "\n",
    "list_lane0 = []\n",
    "list_lane1 = []\n",
    "list_lane2 = []\n",
    "list_lane3 = []\n",
    "for file in files:\n",
    "    arr = np.load(file, allow_pickle = True)\n",
    "\n",
    "    lane1 = np.array(arr[1])\n",
    "    lane2 = np.array(arr[2])\n",
    "    lane0 = np.array(arr[0])\n",
    "    lane3 = np.array(arr[3])\n",
    "    arr = np.load(file, allow_pickle = True)\n",
    "    \n",
    "    list_lane0.append(lane0)\n",
    "    list_lane1.append(lane1)\n",
    "    list_lane2.append(lane2)\n",
    "    list_lane3.append(lane3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caf6177b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_avg(new_arr, old_arr, new_weight = 0.05):\n",
    "    return new_arr * new_weight + old_arr * (1-new_weight)\n",
    "\n",
    "\n",
    "class MvArray():\n",
    "    def __init__(self, initial_array, alpha):\n",
    "        self.array = initial_array\n",
    "        self.cnt = 0\n",
    "        self.alpha = alpha\n",
    "    \n",
    "    def update(self, new_array):\n",
    "        c = max(self.alpha, 1 / (self.cnt + 1))\n",
    "        self.array = moving_avg(new_array, self.array, new_weight= c)\n",
    "        self.cnt += 1\n",
    "        return self.array\n",
    "\n",
    "class Line():\n",
    "    def __init__(self, lines_alpha = 0.05, limits_alpha = 0.05, n_degrees = 3):\n",
    "        self.n_degrees = n_degrees\n",
    "        self.cnt = 0\n",
    "        self.limits_alpha = limits_alpha\n",
    "        self.lines_alpha = lines_alpha\n",
    "        \n",
    "        \n",
    "    def update(self, new_line):\n",
    "        if self.cnt == 0 :\n",
    "            \n",
    "            if len(new_line):\n",
    "            \n",
    "                p = np.polyfit(new_line[:,0], new_line[:,1], self.n_degrees)\n",
    "                (x_min, _), (x_max, _) = line1.min(0), line1.max(0)\n",
    "\n",
    "                self.limits = MvArray(np.array([x_min, x_max]), self.limits_alpha)\n",
    "                x_min, x_max = self.limits.array\n",
    "\n",
    "                self.axis = np.linspace(x_min, x_max, 10)\n",
    "                npred_line = np.polyval(p, self.axis) #predicted line\n",
    "                pred_line = npred_line\n",
    "\n",
    "                self.predicted_line = MvArray(pred_line, self.lines_alpha)\n",
    "\n",
    "                self.cnt += 1\n",
    "        \n",
    "        else:\n",
    "            if len(new_line):\n",
    "                p = np.polyfit(new_line[:,0], new_line[:,1], self.n_degrees)\n",
    "                (nx_min, _), (nx_max, _) = new_line.min(0), new_line.max(0)\n",
    "\n",
    "                self.limits.update(np.array([nx_min, nx_max]))\n",
    "                x_min, x_max = self.limits.array\n",
    "\n",
    "                self.axis = np.linspace(x_min, x_max, 10)\n",
    "                npred_line = np.polyval(p, self.axis)\n",
    "\n",
    "                self.predicted_line.update(npred_line)\n",
    "                self.cnt += 1\n",
    "            \n",
    "    def get_line(self):\n",
    "        try:\n",
    "            return np.array([self.axis, self.predicted_line.array]).T\n",
    "        except:\n",
    "            return np.array(np.zeros((10,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21e86128",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "\n",
    "# create an instance for each line\n",
    "myline0 = Line(lines_alpha = 0.05, limits_alpha = 0.05, n_degrees = 3)\n",
    "myline1=  Line(lines_alpha = 0.05, limits_alpha = 0.05, n_degrees = 3)\n",
    "myline2=  Line(lines_alpha = 0.05, limits_alpha = 0.05, n_degrees = 3)\n",
    "myline3 = Line(lines_alpha = 0.05, limits_alpha = 0.05, n_degrees = 3)\n",
    "\n",
    "for idx , (line0, line1,line2, line3) in enumerate(zip(list_lane0, list_lane1, list_lane2, list_lane3)): \n",
    "    \n",
    "    myline1.update(line1) # for each frame, update the line point values\n",
    "    myline2.update(line2) # for each frame, update the line point values\n",
    "    myline3.update(line3) # for each frame, update the line point values\n",
    "    myline0.update(line0) # for each frame, update the line point values\n",
    "    \n",
    "    pline1 = myline1.get_line() #this code gets the line points from the Line object\n",
    "    pline2 = myline2.get_line()\n",
    "    pline0 = myline0.get_line()\n",
    "    pline3 = myline3.get_line()\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Below is only for plotting using matplotlib #\n",
    "    #            You do not need these            #\n",
    "    ###############################################\n",
    "    if len(line3) == 0:\n",
    "        line3 = np.array(np.zeros((10,2)))\n",
    "    if len(line0) == 0:\n",
    "        line0 = np.array(np.zeros((10,2)))\n",
    "    \n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.xlim(200,1200) \n",
    "    plt.ylim(800, 200)\n",
    "    \n",
    "    plt.plot(line1[:,0], line1[:,1], label = \"actual\")\n",
    "    plt.plot(pline1[:,0], pline1[:,1], label = \"mv\")\n",
    "    \n",
    "    plt.plot(line2[:,0], line2[:,1], label = \"actual\")\n",
    "    plt.plot(pline2[:,0], pline2[:,1], label = \"mv\")\n",
    "    \n",
    "    plt.plot(line3[:,0], line3[:,1], label = \"actual\")\n",
    "    plt.plot(pline3[:,0], pline3[:,1], label = \"mv\")\n",
    "    \n",
    "    plt.plot(line0[:,0], line0[:,1], label = \"actual\")\n",
    "    plt.plot(pline0[:,0], pline0[:,1], label = \"mv\")\n",
    "    \n",
    "    \n",
    "    plt.pause(0.1) #is necessary for the plot to update for some reason\n",
    "    \n",
    "    if idx>2:\n",
    "        plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878dec3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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