location.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import serial\n",
    "import time\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import json\n",
    "\n",
    "from IPython.display import IFrame\n",
    "import serial.tools.list_ports\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "from pyargus.directionEstimation import *\n",
    "\n",
    "import math\n",
    "from collections import deque\n",
    "from scipy.spatial import distance\n",
    "\n",
    "from IPython.display import display, clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Localizer():\n",
    "    \n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Localizer, self).__init__()\n",
    "        \n",
    "        self.baud_rate = 115200\n",
    "        self.timeout = 1\n",
    "        \n",
    "        self.devices = []\n",
    "        self.coords = []\n",
    "        \n",
    "        self.width = [-2,10]\n",
    "        self.height = [-2,14]\n",
    "        \n",
    "        self.spacing = 0.1\n",
    "        \n",
    "        self.anchors = [\n",
    "            {\"anchor\": \"A\",\n",
    "            \"coord\": [0,0]},\n",
    "            {\"anchor\": \"B\",\n",
    "            \"coord\": [8,0]},\n",
    "            {\"anchor\": \"C\",\n",
    "            \"coord\": [8,12]},\n",
    "            {\"anchor\": \"A\",\n",
    "            \"coord\": [0,12]},\n",
    "            ]\n",
    "\n",
    "        #manufacturer given\n",
    "        self.tag_id = {\n",
    "            \" 5817\": \"Tag 1\",\n",
    "            \" 58AD\": \"Tag 2\",\n",
    "            \" 123B\": \"Tag 3\"\n",
    "        }\n",
    "        self.tag_color = {\n",
    "            \" 5817\": \"Green\",\n",
    "            \" 58AD\": \"Blue\",\n",
    "            \" 123B\": \"Black\"\n",
    "        }\n",
    "        \n",
    "    def connect_serial(self):\n",
    "        port = self.get_port()\n",
    "        self.uart = serial.Serial(port, baudrate=self.baud_rate, timeout = self.timeout)\n",
    "    \n",
    "    def disconnect_serial(self):\n",
    "        self.uart.close()\n",
    "        \n",
    "    def get_port(self):\n",
    "        ports = serial.tools.list_ports.comports()\n",
    "\n",
    "        for port, desc, hwid in sorted(ports):\n",
    "\n",
    "            if \"J-Link\" in desc:\n",
    "                return port\n",
    "\n",
    "        return null\n",
    "\n",
    "    def get_serial_data(self):\n",
    "\n",
    "        if self.uart.is_open:\n",
    "\n",
    "            received_data = self.uart.inWaiting()\n",
    "            if received_data:\n",
    "                data = self.uart.read(received_data)\n",
    "                data = data.decode('windows-1252')\n",
    "                \n",
    "                return data\n",
    "            else:\n",
    "                time.sleep(0.15)\n",
    "\n",
    "        else:\n",
    "            return(\"Uart not open\")\n",
    "\n",
    "\n",
    "    def compute_coordinates(self, data):\n",
    "        df = re.split('\\r|\\n', data)\n",
    "        df = pd.DataFrame(df, columns=['data'])\n",
    "        df = df[[\")\" in x for x in df['data']]]\n",
    "\n",
    "        self.coords = []\n",
    "        \n",
    "        for idx, row in df.iterrows():\n",
    "            row = re.findall(\"[^()]+\", row['data'])[1]\n",
    "            row = re.split(\"\\[(.*?)\\]\", row)\n",
    "\n",
    "            id = row[0]\n",
    "\n",
    "            row = re.split(',', row[1])\n",
    "\n",
    "            coord = {\n",
    "                \"id\": id,\n",
    "                \"x\": float(row[0]),\n",
    "                \"y\": float(row[1]),\n",
    "                \"z\": float(row[2])\n",
    "            }\n",
    "\n",
    "            if not any(coord[\"id\"]==d[\"id\"] for d in self.coords): #don't add duplicates\n",
    "                if not (np.isnan(coord['x']) or np.isnan(coord['y']) or np.isnan(coord['z'])): #don't add nans\n",
    "                    self.coords.append(coord)\n",
    "\n",
    "    def sanitize_coordinates(self):\n",
    "        \n",
    "        #initialize\n",
    "        for coord in self.coords:\n",
    "            if not any(coord[\"id\"] == d[\"id\"] for d in self.devices): #if coord[id] is not in device\n",
    "                    self.devices.append(coord)\n",
    "        \n",
    "        #copy old coords from device if doesn't exist in current coords\n",
    "        for device in self.devices:\n",
    "            if not any(device[\"id\"] == c[\"id\"] for c in self.coords): # if device is not in coords\n",
    "                self.coords.append(device)\n",
    "        \n",
    "        #update device\n",
    "        self.devices = self.coords\n",
    "        \n",
    "\n",
    "    def visualize_tags(self):\n",
    "\n",
    "        clear_output(wait=True)\n",
    "        \n",
    "        plt.figure(figsize=(12, 6)) \n",
    "        \n",
    "        ax = plt.subplot(1,2,1)\n",
    "        self.plot_anchors()\n",
    "\n",
    "        for coord in self.coords:\n",
    "            plt.plot(coord['x'], coord['y'], 'g^', markersize=8, \n",
    "                    mfc=self.tag_color[coord[\"id\"]], mec = 'None',\n",
    "                    label = self.tag_id[coord[\"id\"]])\n",
    "            \n",
    "        #ax.legend(loc='center left', ncol=3, bbox_to_anchor=(0, -0.1))\n",
    "        \n",
    "        plt.xlim(self.width)\n",
    "        plt.ylim(self.height)\n",
    "\n",
    "    \n",
    "    def visualize_prediction_box(self):\n",
    "\n",
    "        ax = plt.subplot(1,2,2)\n",
    "        \n",
    "        x_range = np.arange(self.width[0], self.width[1], self.spacing)\n",
    "        y_range = np.arange(self.height[0], self.height[1], self.spacing)\n",
    "        \n",
    "        for i in x_range:\n",
    "            plt.axvline(x = i, color = 'b', linewidth=0.1)\n",
    "        \n",
    "        for i in y_range:\n",
    "            plt.axhline(y = i, color = 'b', linewidth=0.1)\n",
    "        \n",
    "        \n",
    "        for coord in self.coords:\n",
    "        \n",
    "            if np.isnan(coord['x']) or np.isnan(coord['y']):\n",
    "                continue\n",
    "        \n",
    "            rect_x = self.search_insert_position(x_range, coord['x'])\n",
    "            rect_y = self.search_insert_position(y_range, coord['y'])\n",
    "\n",
    "            rect = Rectangle((x_range[rect_x], y_range[rect_y]),\n",
    "                                self.spacing, self.spacing,\n",
    "                                color = self.tag_color[coord[\"id\"]],\n",
    "                                label=self.tag_id[coord[\"id\"]])\n",
    "\n",
    "            ax.add_patch(rect)\n",
    "        \n",
    "        #ax.legend(loc='center left', ncol=3, bbox_to_anchor=(0, -0.1))\n",
    "        \n",
    "        plt.xlim(self.width)\n",
    "        plt.ylim(self.height)\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_anchors(self):\n",
    "        x= [a['coord'][0] for a in self.anchors]\n",
    "        y = [a['coord'][1] for a in self.anchors]\n",
    "        \n",
    "        plt.plot(x,y, marker=\"^\", markersize=8, markerfacecolor=\"None\", \n",
    "                 markeredgecolor = \"red\", linestyle = 'None', label = \"Anchor\")\n",
    "        \n",
    "    \n",
    "    def search_insert_position(self, nums, target):\n",
    "        left = 0\n",
    "        right = len(nums) - 1\n",
    "        \n",
    "        while left <= right:\n",
    "            mid = (left + right) // 2\n",
    "            \n",
    "            if nums[mid] == target:\n",
    "                return mid\n",
    "            elif target > nums[mid]:\n",
    "                left = mid + 1\n",
    "            else:\n",
    "                right = mid - 1\n",
    "        \n",
    "        return right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/dev/ttyACM0\n"
     ]
    }
   ],
   "source": [
    "# ports = serial.tools.list_ports.comports()\n",
    "\n",
    "# for port, desc, hwid in sorted(ports):\n",
    "\n",
    "#     if \"J-Link\" in desc:\n",
    "#         print(port)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize live data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# localizer = Localizer()\n",
    "# localizer.connect_serial()\n",
    "\n",
    "# to_save = []\n",
    "\n",
    "# localizer.spacing= 0.5\n",
    "# for i in range(50):\n",
    "#     try:\n",
    "#         data = localizer.get_serial_data()\n",
    "#         localizer.compute_coordinates(data)\n",
    "#         localizer.sanitize_coordinates()\n",
    "#         localizer.visualize_tags()\n",
    "#         localizer.visualize_prediction_box()\n",
    "#         to_save.append(localizer.coords)\n",
    "#     except:\n",
    "#         continue\n",
    "        \n",
    "# localizer.disconnect_serial()\n",
    "\n",
    "# with open('data/saved_result_001', 'w') as fout:\n",
    "#     json.dump(to_save, fout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize saved data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# flamingo = Flamingo()\n",
    "\n",
    "# f = open('data/saved_result')\n",
    "# data = json.load(f)\n",
    "\n",
    "# itr = 0\n",
    "# flamingo.spacing= 0.2\n",
    "# for d in data:\n",
    "    \n",
    "#     flamingo.coords = d\n",
    "#     flamingo.visualize_tags()\n",
    "#     flamingo.visualize_prediction_box()\n",
    "    \n",
    "#     itr +=1\n",
    "    \n",
    "#     if(itr==500):\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}