ai_analysis/live_analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from live_connection import Live_connection \n",
    "from IoT.IoT import Fan_Control,Light_Control,Buzzer_Control\n",
    "from audioplayer import AudioPlayer\n",
    "import threading\n",
    "import torch\n",
    "from PIL import Image\n",
    "from io import BytesIO, StringIO\n",
    "import math\n",
    "import time\n",
    "import numpy as np\n",
    "from IPython.display import display,update_display,Audio , Image as ipyImage\n",
    "import ipywidgets as widgets\n",
    "from loguru import logger\n",
    "from collections import deque\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup stuffs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup logger\n",
    "\n",
    "> output from live_connection.py are not iterpreted by the logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "LogStream = StringIO()\n",
    "logger.add(\"logs/file_{time}.log\", format=\"{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}\", rotation=\"50MB\")\n",
    "logger.add(LogStream)\n",
    "logdeque = deque(maxlen=10)\n",
    "posLogStream = 0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and setup model\n",
    "![img/yolo_model_graph.png](img/yolo_model_graph.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Tommy AU/.cache\\torch\\hub\\ultralytics_yolov5_master\n",
      "YOLOv5  2023-2-1 Python-3.10.9 torch-1.12.1 CPU\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5x6 summary: 574 layers, 140730220 parameters, 0 gradients\n",
      "Adding AutoShape... \n"
     ]
    }
   ],
   "source": [
    "# you can change to other yolo model(they are not tested,but less cpu usage)\n",
    "# you may take reference to the graph\n",
    "model = torch.hub.load('ultralytics/yolov5', 'yolov5x6', pretrained=True) # default yolov5x6\n",
    "model.classes = [0,59,63,67]# person, bed, laptop(as some phone can be detact by laptop), cell phone \n",
    "torch.set_num_interop_threads(8)# improve performance, you may change according to your cpu\n",
    "torch.set_num_threads(8)\n",
    "#model.cpu() , if you want to use cpu;model.cuda() if you want to use gpu"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🚩 Here is the place to start the program\n",
    "## Start and Setup socket(Connect camera) and Other IoT devices\n",
    "## And Call main()\n",
    "<br>\n",
    "\n",
    "> #### ⚠  There maybe exeption message pop up when you restart the socket, ignore it will do the job<br>But if it this error from system os it is recommend to use another port\n",
    "\n",
    "\n",
    "> #### ⚠  There maybe error message pop up when you restart the socket, ignore it will do the job<br>\n",
    "> Examples error<br>\n",
    "    frameid = splited[1]<br>\n",
    "IndexError: list index out of range<br>\n",
    "\n",
    "> #### \"WARNING NMS time limit 0.550s exceeded\" can be ignore\n",
    "\n",
    "> #### 🎥 The image will have a high latency then the actuall latency,<br>   As the render of image use >10 sec.<br>Stuff is still working on normal latency, only the image preview is slow.\n",
    "\n",
    "> #### ⚠  If none of the above work try to restart kernel or even restart pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binding to 192.168.0.210 7778\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:39:43.519 | INFO     | __main__:main_analysis:2 - Main starts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=480x640>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('192.168.0.193', 44028) connected\n",
      "accept?(y/n)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:39:50.133 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:51.270 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:52.441 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:53.626 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:54.783 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:56.278 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:57.397 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:59.033 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:00.756 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:02.326 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:04.333 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:05.957 | INFO     | __main__:main_analysis:10 - new frame\n",
      "WARNING  NMS time limit 0.550s exceeded\n",
      "2023-06-23 20:40:07.329 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.814 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-893 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:39:54.783 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:56.278 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:57.397 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:59.033 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:00.756 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:02.326 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:04.333 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:05.957 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.329 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.814 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "2023-06-23 20:40:08.253 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "WARNING  NMS time limit 0.550s exceeded\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-898 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2023-06-23 20:39:54.783 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:56.278 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:57.397 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:59.033 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:00.756 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:02.326 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:04.333 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:05.957 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.329 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.814 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "2023-06-23 20:40:09.249 | INFO     | __main__:main_analysis:10 - new frame\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:39:57.397 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:39:59.033 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:00.756 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:02.326 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:04.333 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:05.957 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.329 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.814 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:08.253 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:09.249 | INFO     | __main__:main_analysis:10 - new frame\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:09.575 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread WARNING  NMS time limit 0.550s exceeded\n",
      "Thread-910 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "2023-06-23 20:40:10.399 | INFO     | __main__:main_analysis:10 - new frame\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:10.693 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:11.291 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-922 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "2023-06-23 20:40:11.810 | INFO     | __main__:main_analysis:10 - new frame\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:04.333 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:05.957 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.329 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.814 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:08.253 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:09.249 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:09.575 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:10.399 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:10.693 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:11.291 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:12.815 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-935 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:07.329 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:07.814 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:08.253 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:09.249 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:09.575 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:10.399 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:10.693 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:11.291 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:11.810 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:12.815 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:13.875 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:14.277 | INFO     | __main__:detectsleep:51 - Sleeping\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:15.129 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-947 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "2023-06-23 20:40:15.273 | INFO     | __main__:main_analysis:10 - new frame\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [0], Bed temp: None\n",
      "2023-06-23 20:40:09.249 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:09.575 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:10.399 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:10.693 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:11.291 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:11.810 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:12.815 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:13.875 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:14.277 | INFO     | __main__:detectsleep:51 - Sleeping\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:15.757 | INFO     | __main__:OnLyingBed:22 - Aircon on\n",
      "2023-06-23 20:40:16.073 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-960 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:11.291 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:11.810 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:12.815 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:13.875 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:14.277 | INFO     | __main__:detectsleep:51 - Sleeping\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n",
      "2023-06-23 20:40:15.129 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:15.273 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:15.757 | INFO     | __main__:OnLyingBed:22 - Aircon on\n",
      "2023-06-23 20:40:16.073 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "2023-06-23 20:40:16.545 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-969 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:11.810 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:12.815 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:13.875 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:14.277 | INFO     | __main__:detectsleep:51 - Sleeping\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n",
      "2023-06-23 20:40:15.129 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:15.273 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:15.757 | INFO     | __main__:OnLyingBed:22 - Aircon on\n",
      "2023-06-23 20:40:16.073 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:16.545 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:18.274 | INFO     | __main__:detectmoving:39 - Not moving for 7.164562225341797 seconds\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "Exception in thread Thread-979 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:14.277 | INFO     | __main__:detectsleep:51 - Sleeping\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n",
      "2023-06-23 20:40:15.129 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:15.273 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:15.757 | INFO     | __main__:OnLyingBed:22 - Aircon on\n",
      "2023-06-23 20:40:16.073 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:16.545 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.274 | INFO     | __main__:detectmoving:39 - Not moving for 7.164562225341797 seconds\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-989 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n",
      "2023-06-23 20:40:15.129 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:15.273 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:15.757 | INFO     | __main__:OnLyingBed:22 - Aircon on\n",
      "2023-06-23 20:40:16.073 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:16.545 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.274 | INFO     | __main__:detectmoving:39 - Not moving for 7.164562225341797 seconds\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:20.106 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:14.563 | INFO     | __main__:OnLyingBed:8 - playing music\n",
      "2023-06-23 20:40:15.129 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:15.273 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:15.757 | INFO     | __main__:OnLyingBed:22 - Aircon on\n",
      "2023-06-23 20:40:16.073 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:16.545 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.274 | INFO     | __main__:detectmoving:39 - Not moving for 7.164562225341797 seconds\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-1001 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:20.505 | INFO     | __main__:detections:97 - Lying on bed for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.625 | INFO     | __main__:detectsleep:55 - Sleeping for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1011 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:16.545 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.274 | INFO     | __main__:detectmoving:39 - Not moving for 7.164562225341797 seconds\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.106 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.505 | INFO     | __main__:detections:97 - Lying on bed for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.625 | INFO     | __main__:detectsleep:55 - Sleeping for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1022 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:18.274 | INFO     | __main__:detectmoving:39 - Not moving for 7.164562225341797 seconds\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.106 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.505 | INFO     | __main__:detections:97 - Lying on bed for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.625 | INFO     | __main__:detectsleep:55 - Sleeping for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:22.934 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1033 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:23.394 | INFO     | __main__:main_analysis:10 - new frame\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:18.671 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:18.742 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.106 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.505 | INFO     | __main__:detections:97 - Lying on bed for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.625 | INFO     | __main__:detectsleep:55 - Sleeping for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:22.934 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:24.163 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1045 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:24.589 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:18.962 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.106 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:20.505 | INFO     | __main__:detections:97 - Lying on bed for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.625 | INFO     | __main__:detectsleep:55 - Sleeping for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:22.934 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:23.394 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:24.163 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:24.596 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "Exception in thread Thread-1056 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1065 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:20.625 | INFO     | __main__:detectsleep:55 - Sleeping for 11.098241090774536 seconds\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:22.934 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:23.394 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:24.163 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.589 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.596 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:25.219 | INFO     | __main__:main_analysis:10 - new frame\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:20.676 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:22.934 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:23.394 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:24.163 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.589 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.596 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:25.219 | INFO     | __main__:main_analysis:10 - new frame\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:25.476 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1076 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:20.729 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:21.337 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:22.934 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:23.394 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:24.163 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.589 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.596 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:25.219 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:25.476 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:26.708 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:27.885 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1089 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "2023-06-23 20:40:28.034 | INFO     | __main__:main_analysis:10 - new frame\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:23.394 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:24.163 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.589 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.596 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:25.219 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:25.476 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:26.708 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:27.885 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:28.034 | INFO     | __main__:main_analysis:10 - new frame\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:28.327 | INFO     | __main__:detectmoving:39 - Not moving for 10.250167846679688 seconds\n",
      "2023-06-23 20:40:28.431 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1100 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:24.589 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:24.596 | INFO     | __main__:detectmoving:31 - Not moving\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:25.219 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:25.476 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:26.708 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:27.885 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:28.034 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:28.327 | INFO     | __main__:detectmoving:39 - Not moving for 10.250167846679688 seconds\n",
      "2023-06-23 20:40:28.431 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:29.803 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:31.277 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1113 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:31.485 | INFO     | __main__:main_analysis:10 - new frame\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:24.852 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:25.219 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:25.476 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:26.708 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:27.885 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:28.034 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:28.327 | INFO     | __main__:detectmoving:39 - Not moving for 10.250167846679688 seconds\n",
      "2023-06-23 20:40:28.431 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:29.803 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:31.277 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:33.059 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.168 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1125 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:26.708 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:27.885 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:28.034 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:28.327 | INFO     | __main__:detectmoving:39 - Not moving for 10.250167846679688 seconds\n",
      "2023-06-23 20:40:28.431 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:29.803 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:31.277 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:31.485 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.059 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.168 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:34.494 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:34.562 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1137 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:28.034 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:28.327 | INFO     | __main__:detectmoving:39 - Not moving for 10.250167846679688 seconds\n",
      "2023-06-23 20:40:28.431 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:29.803 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:31.277 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:31.485 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.059 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.168 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:34.494 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:34.562 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:35.697 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread 2023-06-23 20:40:35.972 | INFO     | __main__:main_analysis:10 - new frame\n",
      "Thread-1149 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:28.327 | INFO     | __main__:detectmoving:39 - Not moving for 10.250167846679688 seconds\n",
      "2023-06-23 20:40:28.431 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:29.803 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:31.277 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:31.485 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.059 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.168 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:34.494 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:34.562 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.697 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-943 (OnLyingBed):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connection.py\", line 174, in _new_conn\n",
      "    conn = connection.create_connection(\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\util\\connection.py\", line 95, in create_connection\n",
      "    raise err\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\util\\connection.py\", line 85, in create_connection\n",
      "    sock.connect(sa)\n",
      "TimeoutError: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connectionpool.py\", line 703, in urlopen\n",
      "    httplib_response = self._make_request(\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connectionpool.py\", line 398, in _make_request\n",
      "    conn.request(method, url, **httplib_request_kw)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connection.py\", line 239, in request\n",
      "    super(HTTPConnection, self).request(method, url, body=body, headers=headers)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\http\\client.py\", line 1282, in request\n",
      "    self._send_request(method, url, body, headers, encode_chunked)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\http\\client.py\", line 1328, in _send_request\n",
      "    self.endheaders(body, encode_chunked=encode_chunked)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\http\\client.py\", line 1277, in endheaders\n",
      "    self._send_output(message_body, encode_chunked=encode_chunked)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\http\\client.py\", line 1037, in _send_output\n",
      "2023-06-23 20:40:37.451 | INFO     | __main__:main_analysis:10 - new frame\n",
      "    self.send(msg)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\http\\client.py\", line 975, in send\n",
      "    self.connect()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connection.py\", line 205, in connect\n",
      "    conn = self._new_conn()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connection.py\", line 179, in _new_conn\n",
      "    raise ConnectTimeoutError(\n",
      "urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPConnection object at 0x000001E7B602C250>, 'Connection to 192.168.43.151 timed out. (connect timeout=None)')\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\requests\\adapters.py\", line 489, in send\n",
      "    resp = conn.urlopen(\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\connectionpool.py\", line 787, in urlopen\n",
      "    retries = retries.increment(\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\urllib3\\util\\retry.py\", line 592, in increment\n",
      "    raise MaxRetryError(_pool, url, error or ResponseError(cause))\n",
      "urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='192.168.43.151', port=7777): Max retries exceeded with url: /controller?fan=On (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x000001E7B602C250>, 'Connection to 192.168.43.151 timed out. (connect timeout=None)'))\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\172981643.py\", line 23, in OnLyingBed\n",
      "  File \"d:\\my programs\\python\\Aiot\\aiot_bedroom\\ai_analysis\\IoT\\IoT.py\", line 12, in set_fan_state\n",
      "    result = requests.get(url = self.url,params=params)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\requests\\api.py\", line 73, in get\n",
      "    return request(\"get\", url, params=params, **kwargs)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\requests\\api.py\", line 59, in request\n",
      "    return session.request(method=method, url=url, **kwargs)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\requests\\sessions.py\", line 587, in request\n",
      "    resp = self.send(prep, **send_kwargs)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\requests\\sessions.py\", line 701, in send\n",
      "    r = adapter.send(request, **kwargs)\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\site-packages\\requests\\adapters.py\", line 553, in send\n",
      "    raise ConnectTimeout(e, request=request)\n",
      "requests.exceptions.ConnectTimeout: HTTPConnectionPool(host='192.168.43.151', port=7777): Max retries exceeded with url: /controller?fan=On (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x000001E7B602C250>, 'Connection to 192.168.43.151 timed out. (connect timeout=None)'))\n",
      "2023-06-23 20:40:38.074 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1163 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:31.277 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:31.485 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.059 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.168 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:34.494 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:34.562 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.697 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.972 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:37.451 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:38.074 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:39.030 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:39.967 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1175 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:33.059 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:33.168 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:34.494 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:34.562 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.697 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.972 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:37.451 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:38.074 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:39.030 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:39.967 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:40.683 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:42.111 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1187 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "2023-06-23 20:40:42.268 | INFO     | __main__:main_analysis:10 - new frame\n",
      "    "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:34.562 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.697 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.972 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:37.451 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:38.074 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:39.030 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:39.967 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:40.683 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:42.111 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:42.268 | INFO     | __main__:main_analysis:10 - new frame\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:43.033 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1199 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:35.697 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:35.972 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:37.451 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:38.074 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:39.030 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:39.967 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:40.683 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:42.111 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:42.268 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:43.033 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n",
      "2023-06-23 20:40:44.091 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:45.279 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1211 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1d9abb74ec4addbf782caaebaa1c04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Stop', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body temp: [], Bed temp: None\n",
      "2023-06-23 20:40:37.451 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:38.074 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:39.030 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:39.967 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:40.683 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:42.111 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:42.268 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:43.033 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "2023-06-23 20:40:44.091 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:45.279 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-23 20:40:45.784 | INFO     | __main__:main_analysis:10 - new frame\n",
      "2023-06-23 20:40:46.187 | INFO     | __main__:OnTemperature:6 - tempPerson: [], tempBed: None\n",
      "Exception in thread Thread-1222 (OnTemperature):\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 1016, in _bootstrap_inner\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[28], line 31\u001b[0m\n\u001b[0;32m     29\u001b[0m button_download\u001b[39m.\u001b[39mon_click(onclick)\n\u001b[0;32m     30\u001b[0m display(button_download)\n\u001b[1;32m---> 31\u001b[0m main_analysis()\n",
      "Cell \u001b[1;32mIn[26], line 7\u001b[0m, in \u001b[0;36mmain_analysis\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m display(imagedisplay,display_id\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m display(button_download)\n\u001b[1;32m----> 7\u001b[0m \u001b[39mwhile\u001b[39;00m term:\n\u001b[0;32m      8\u001b[0m     \u001b[39m#get data\u001b[39;00m\n\u001b[0;32m      9\u001b[0m     \u001b[39mif\u001b[39;00m live_connection\u001b[39m.\u001b[39mnew_frame_avaliable:\n\u001b[0;32m     10\u001b[0m         logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mnew frame\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    self.run()\n",
      "  File \"c:\\Users\\Tommy AU\\.conda\\envs\\myenv\\lib\\threading.py\", line 953, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"C:\\Users\\Tommy AU\\AppData\\Local\\Temp\\ipykernel_27868\\1052446600.py\", line 22, in OnTemperature\n",
      "TypeError: '<' not supported between instances of 'NoneType' and 'int'\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "#URl of cohntroller\n",
    "# addr = \"http://192.168.43.151:7777/controller\"\n",
    "# x = input(\"Address to bind(enter for default):\")\n",
    "# if x != \"\":\n",
    "#     addr = x\n",
    "# # Connect IoT devices\n",
    "# fan = Fan_Control(addr)\n",
    "# light = Light_Control(addr)\n",
    "# buzzer = Buzzer_Control(addr)\n",
    "# aircon = fan.get_fan_state() # use fan for aircon temporarily\n",
    "# lights = light.get_light_state()\n",
    "addr = \"192.168.0.210\"## use ipconfig to find the ip address of the computer\n",
    "x = input(\"Address to bind(enter for default):\")\n",
    "if x != \"\":\n",
    "    addr = x\n",
    "try:\n",
    "    if live_connection != None and not live_connection.died:\n",
    "        live_connection.terminate()\n",
    "except NameError:\n",
    "    pass\n",
    "live_connection = None\n",
    "live_connection = Live_connection(addr,7778)    # 7777 is the default port, use other port if the port is blocked\n",
    "\n",
    "t1 = threading.Thread(target=live_connection.start_connection,kwargs={\"nolog\":True},group=None)\n",
    "# nolog = True\n",
    "t1.start()\n",
    "term = True\n",
    "button_download = widgets.Button(description = 'Stop')   \n",
    "button_download.on_click(onclick)\n",
    "display(button_download)\n",
    "main_analysis()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Force Stop\n",
    "> Press start and stop the below cell multiple times to kill the program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def onclick(arg):\n",
    "    term = False\n",
    "    live_connection.terminate()\n",
    "    t1.terminate()\n",
    "    live_connection = None"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Close socket if needed\n",
    "> or reset some var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# live_connection.ss.close()\n",
    "# live_connection = None"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable of current state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#global tolerance\n",
    "tolerance = 10\n",
    "## Person exist\n",
    "personexist = False\n",
    "## lying bed\n",
    "lyingonbed = False\n",
    "startlyingtime = 0\n",
    "notlyingtimestart = 0\n",
    "chancelying = 3\n",
    "## touching phone\n",
    "Touchingphone = False\n",
    "starttouchingtime = 0\n",
    "nottouchingtimestart = 0\n",
    "chanceTouching = 3\n",
    "## sleeping\n",
    "sleeping = False\n",
    "startsleepingtime = 0\n",
    "notsleepingtimestart = 0\n",
    "## not moving\n",
    "notmoving = False\n",
    "lastposition = [0,0,0,0]\n",
    "toleranceOfNotMoving = 10\n",
    "startnotmovingtime = 0\n",
    "movingtimestart = 0\n",
    "chancenotmoving = 2\n",
    "## temperatures\n",
    "tempPerson = []\n",
    "### core temp requires face recongnition\n",
    "tempBed = -1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Condition for automation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Person detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isPersonAvailable(pandas)-> bool:\n",
    "    return 0 in pandas['class'].array"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed lying detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isLyinOnBed(pandas,k:int)->bool:\n",
    "    #pandasDataFrame is the output of model(pandas.DataFrame)\n",
    "    #return true if the person is lying on the bed\n",
    "    #return false if the person is not lying on the bed\n",
    "\n",
    "    # Check if the person is on bed     \n",
    "    if not(0 in pandas['class'].array):\n",
    "        return False\n",
    "    # Calculate the length ratio of person on the bed\n",
    "    personxlen = pandas[pandas['class'] == 0]['xmax'].array[k] - pandas[pandas['class'] == 0]['xmin'].array[k]\n",
    "    personylen = pandas[pandas['class'] == 0]['ymax'].array[k] - pandas[pandas['class'] == 0]['ymin'].array[k]\n",
    "    if not(59 in pandas['class'].array):\n",
    "        if personylen>personxlen:\n",
    "            if(personylen/personxlen>3 and personylen/personxlen<5):# acoording to web data, the ratio of length to  shoulder is 4:1\n",
    "                return True\n",
    "            else:\n",
    "                return False\n",
    "    bedxlen = pandas[pandas['class'] == 59]['xmax'].array[0] - pandas[pandas['class'] == 59]['xmin'].array[0]\n",
    "    bedylen = pandas[pandas['class'] == 59]['ymax'].array[0] - pandas[pandas['class'] == 59]['ymin'].array[0]\n",
    "\n",
    "    if bedxlen > bedylen :\n",
    "            return personxlen/bedxlen > 0.6\n",
    "    else:\n",
    "            return personylen/bedylen > 0.6\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Touching phone detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isTouchingPhone(pandas,person)->bool:\n",
    "    #pandasDataFrame is the output of model(pandas.DataFrame)\n",
    "    #return true if the person is touching the phone\n",
    "    #return false if the person is not touching the phone\n",
    "    #return None if the person is not in the frame\n",
    "    # Check if the person is on bed\n",
    "    phone = []\n",
    "    if 63 in pandas['class'].array:\n",
    "        phone.append(pandas[pandas['class'] == 63])\n",
    "    if 67 in pandas['class'].array:\n",
    "        phone.append(pandas[pandas['class'] == 67])\n",
    "    if len(phone) == 0:\n",
    "        return False\n",
    "    # see if the person touches the phone\n",
    "    overlapArea = 0\n",
    "    people = pandas[pandas['class'] == 0]\n",
    "    for x in person:\n",
    "        for y in phone:\n",
    "            for z in range(len(y)):\n",
    "                x_overlap = max(0, min(y['xmax'].array[z], people['xmax'].array[x]) - max(y['xmin'].array[z], people['xmin'].array[x]));\n",
    "                y_overlap = max(0, min(y['ymax'].array[z], people['ymax'].array[x]) - max(y['ymin'].array[z], people['ymin'].array[x]));\n",
    "                overlapArea = max(overlapArea,x_overlap * y_overlap);\n",
    "                areaphone = (y['xmax'].array[z] - y['xmin'].array[z])*(y['ymax'].array[z] - y['ymin'].array[z])\n",
    "                time.sleep(1)\n",
    "                if overlapArea/areaphone > 0.3:\n",
    "                    return True\n",
    "    return False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Human movement detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isnotmoving(pandas)->bool:\n",
    "    #pandasDataFrame is the output of model(pandas.DataFrame)\n",
    "    #return true if the person is not moving\n",
    "    #return false if the person is moving\n",
    "    #return None if the person is not in the frame\n",
    "    # Check if the person is on bed\n",
    "    person = pandas[pandas['class'] == 0]\n",
    "    if len(person) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        x = person['xmin'].array[0]\n",
    "        y = person['ymin'].array[0]\n",
    "        x2 = person['xmax'].array[0]\n",
    "        y2 = person['ymax'].array[0]\n",
    "        if abs(x - lastposition[0]) < toleranceOfNotMoving and abs(y - lastposition[1]) < toleranceOfNotMoving and abs(x2 - lastposition[2]) < toleranceOfNotMoving and abs(y2 - lastposition[3]) < toleranceOfNotMoving:\n",
    "            return True\n",
    "        else:\n",
    "            lastposition[0] = x\n",
    "            lastposition[1] = y\n",
    "            lastposition[2] = x2\n",
    "            lastposition[3] = y2\n",
    "            return False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insideBed(x,y,bedlist):\n",
    "    return x >= bedlist[0] and x <= bedlist[2] and y >= bedlist[1] and y <= bedlist[3]\n",
    "def insideOtherObject(x,y,lst):\n",
    "    for i in range(len(lst)):\n",
    "        if x >= lst['xmin'].array[i] and x <= lst['xmax'].array[i] and y >= lst['ymin'].array[i] and y <= lst['ymax'].array[i]:\n",
    "            return True\n",
    "    return False\n",
    "def bedtemperature(pandas,thermal,size):\n",
    "    ## calculate the temperature of the bed by calculate the average temperature of the bed exclude the person\n",
    "    ## return the temperature of the bed\n",
    "    if(59 not in pandas['class'].array):\n",
    "        return None\n",
    "    temp = 0\n",
    "    count = 0\n",
    "    sy = len(thermal)\n",
    "    sx = len(thermal[0])\n",
    "    #caches\n",
    "    bedlist = np.array([pandas[pandas['class'] == 59]['xmin'].array[0],pandas[pandas['class'] == 59]['ymin'].array[0],pandas[pandas['class'] == 59]['xmax'].array[0],pandas[pandas['class'] == 59]['ymax'].array[0]])\n",
    "    otherobjects = pandas[pandas['class']!=59]\n",
    "    for y,t in enumerate(thermal):\n",
    "        for x,val in enumerate(t):\n",
    "            # exclude persons and the extra objects\n",
    "            # only count the temperature of the bed\n",
    "            ix = x/sx*size[0]\n",
    "            iy = y/sy*size[1]\n",
    "            if insideBed(ix,iy,bedlist) and not insideOtherObject(ix,iy,otherobjects):\n",
    "                val = val/100 - 273\n",
    "                count+=1\n",
    "                temp = temp + (val - temp)/count\n",
    "    return temp "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Body Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bodytemperture(pandas,thermal,size,k:int):\n",
    "    ## calculate the temperature of the person by calculate the average temperature of the person\n",
    "    ## return the temperature of the person\n",
    "    ## reduce the insignificant temperature(extermely low or high,ie >40, <30)\n",
    "    temp = 0\n",
    "    count = 0\n",
    "    sx = len(thermal[0])\n",
    "    sy = len(thermal)\n",
    "    xmax = pandas[pandas['class'] == 0]['xmax'].array[k]\n",
    "    xmin = pandas[pandas['class'] == 0]['xmin'].array[k]\n",
    "    ymax = pandas[pandas['class'] == 0]['ymax'].array[k]\n",
    "    ymin = pandas[pandas['class'] == 0]['ymin'].array[k]\n",
    "    for y,t in enumerate(thermal):\n",
    "        for x,val in enumerate(t):\n",
    "            ix = x/sx*size[0]\n",
    "            iy = y/sy*size[1]\n",
    "            val = val/100 - 273\n",
    "            ## check if the point is in the person\n",
    "            if ( xmax <= ix and ix >= xmin ) \\\n",
    "            and                                                                                                                     \\\n",
    "               ( ymax <= iy and iy >= ymin ) \\\n",
    "            and val < 40 and val > 30:\n",
    "                 count+=1\n",
    "                 temp = temp + (val - temp)/count\n",
    "    return temp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detection Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## touching phone\n",
    "def detecttouch(pandas,person,timenow):\n",
    "    global nottouchingtimestart,starttouchingtime,Touchingphone,chanceTouching\n",
    "    if isTouchingPhone(pandas,person):\n",
    "        chanceTouching = 0\n",
    "        if not Touchingphone:\n",
    "            Touchingphone = True\n",
    "            starttouchingtime = timenow\n",
    "            nottouchingtimestart = 0\n",
    "            logger.info(\"Touching phone\")\n",
    "            # print(\"Touching phone\")\n",
    "    else:\n",
    "        if Touchingphone:\n",
    "            chanceTouching += 1\n",
    "            if chanceTouching > 3:\n",
    "                nottouchingtimestart = timenow\n",
    "                Touchingphone = False\n",
    "                logger.info(\"Touching phone for {time} seconds\".format(time=timenow-starttouchingtime))\n",
    "                # print(\"Touching phone for\",timenow-starttouchingtime,\"seconds\")\n",
    "                starttouchingtime = 0\n",
    "\n",
    "## is not moving\n",
    "def detectmoving(pandas,timenow):\n",
    "    global notmoving,startnotmovingtime,movingtimestart,chancenotmoving\n",
    "    if isnotmoving(pandas):\n",
    "        chancenotmoving = 0\n",
    "        if not notmoving:\n",
    "            notmoving = True\n",
    "            startnotmovingtime = timenow\n",
    "            movingtimestart = 0\n",
    "            logger.info(\"Not moving\")\n",
    "            # print(\"Not moving\")\n",
    "    else:\n",
    "        if notmoving:\n",
    "            chancenotmoving += 1\n",
    "            if chancenotmoving > 2:\n",
    "                movingtimestart = timenow\n",
    "                notmoving = False\n",
    "                logger.info(\"Not moving for {time} seconds\".format(time=timenow-startnotmovingtime))\n",
    "                # print(\"Not moving for\",timenow-startnotmovingtime,\"seconds\")\n",
    "                startnotmovingtime = 0\n",
    "    ### sleeping or in sleep mood\n",
    "# if lyingfor bed for 10 seconds and not touching phone for 10 seconds, then sleep \n",
    "def detectsleep(timenow):  \n",
    "    global sleeping,startsleepingtime,notsleepingtimestart \n",
    "    if lyingonbed and not Touchingphone:\n",
    "        if not sleeping:\n",
    "            startsleepingtime = timenow\n",
    "            sleeping = True\n",
    "            notsleepingtimestart = 0\n",
    "            logger.info(\"Sleeping\")\n",
    "            # print(\"Sleeping\")\n",
    "    else:# no need tolerance as thee is already tolerance in lying on bed and touching phone\n",
    "        if sleeping:\n",
    "            logger.info(\"Sleeping for {time} seconds\".format(time=timenow-startsleepingtime))\n",
    "            # print(\"Sleeping for\",timenow-startsleepingtime,\"seconds\")\n",
    "            startsleepingtime = 0\n",
    "            sleeping = False\n",
    "            notsleepingtimestart = timenow\n",
    "## calculate the temperature of the bed\n",
    "def detecttemperature(pandas,thermal,person,size):\n",
    "    global tempBed,tempPerson\n",
    "    tempBed = bedtemperature(pandas,thermal,size)\n",
    "    if lyingonbed:\n",
    "        tempPerson = [bodytemperture(pandas,thermal,size,k) for k in person] \n",
    "\n",
    "def detections(pandas,thermal,size,timenow):\n",
    "    global personexist\n",
    "    noperson = len(pandas[pandas['class'] == 0])\n",
    "    if(isPersonAvailable(pandas)):\n",
    "            personexist = True\n",
    "            pass # can add time automation\n",
    "    else:\n",
    "        personexist = False\n",
    "        pass # can add time automation\n",
    "\n",
    "    ## lying on bed\n",
    "    re = False\n",
    "    person = []\n",
    "    global lyingonbed,notlyingtimestart,startlyingtime,chancelying\n",
    "    for k in range(noperson):\n",
    "        if(isLyinOnBed(pandas,k)):\n",
    "            re = True\n",
    "            person.append(k)\n",
    "    if re:\n",
    "        chancelying = 0\n",
    "        if not lyingonbed:\n",
    "            lyingonbed = True\n",
    "            startlyingtime = timenow\n",
    "            notlyingtimestart = 0\n",
    "    else:\n",
    "        if lyingonbed:\n",
    "            chancelying += 1\n",
    "            if chancelying > 3:\n",
    "                notlyingtimestart = timenow\n",
    "                lyingonbed = False\n",
    "                logger.info(\"Lying on bed for {time} seconds\".format(time=timenow-startlyingtime))\n",
    "                # print(\"Lying on bed for\",timenow-startlyingtime,\"seconds\")\n",
    "                startlyingtime = 0\n",
    "    \n",
    "    t1 = threading.Thread(target=detecttouch,args=(pandas,person,timenow))\n",
    "    t2 = threading.Thread(target=detectmoving,args=(pandas,timenow))\n",
    "    t3 = threading.Thread(target=detectsleep,args=(timenow,))\n",
    "    t4 = threading.Thread(target=detecttemperature,args=(pandas,thermal,person,size))\n",
    "    t1.start()\n",
    "    t2.start()\n",
    "    t3.start()\n",
    "    t4.start()\n",
    "\n",
    "\n",
    "            "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> All action are customizable, are highly extensible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dummy variable for example\n",
    "playingmusic = False \n",
    "lights = False \n",
    "aircon = False\n",
    "aircontemp = 25\n",
    "lastairconchange = 0\n",
    "callambulance = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When lying on bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OnLyingBed(lying:bool,startlyingtime:float,stoplyingtime:float):\n",
    "    # Here are some examples\n",
    "    ## if the person is lying on the bed for more than 5 seconds, play music\n",
    "    global playingmusic,aircon\n",
    "    if lying and time.time()-startlyingtime >5 and not playingmusic:\n",
    "        #play music\n",
    "        playingmusic = True\n",
    "        logger.info(\"playing music\")\n",
    "        play_music()\n",
    "        # print(\"playing music\")\n",
    "    elif lying and stoplyingtime-time.time() >5 and playingmusic:\n",
    "        #stop music\n",
    "        playingmusic = False\n",
    "        logger.info(\"stop music\")\n",
    "        stop_music()\n",
    "        # print(\"stop music\")\n",
    "    \n",
    "    ## air con control\n",
    "    if lying and time.time()-startlyingtime >10 and not aircon:\n",
    "        #play music\n",
    "        aircon = True\n",
    "        logger.info(\"Aircon on\")\n",
    "        fan.set_fan_state(aircon)\n",
    "\n",
    "        # print(\"Aircon on\")\n",
    "    elif lying and stoplyingtime-time.time() >30 and aircon:\n",
    "        #stop music\n",
    "        playingmusic = False\n",
    "        logger.info(\"Aircon off\")\n",
    "        fan.set_fan_state(aircon)\n",
    "        # print(\"Aircon off\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When using(touching) phone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OnTouchingPhone(touching:bool,starttouchingtime:float):\n",
    "    # Here are some examples\n",
    "    ## if the person is touching the phone for more than 15 seconds, turn on the lights\n",
    "    global lights\n",
    "    if touching and time.time() - starttouchingtime >15 and not lights:\n",
    "        #turn on lights\n",
    "        lights = True\n",
    "        logger.info(\"turn on lights\")\n",
    "        light.set_light_state(lights)\n",
    "\n",
    "        # print(\"turn on lights\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When sleeping(on sleep mood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OnSleeping(sleeping:bool,startsleepingtime:float):\n",
    "    # Here are some examples\n",
    "    ## if the person is in sleep mood for 5 seconds, turn off the lights\n",
    "    global light,playmusic\n",
    "    if sleeping and startsleepingtime-time.time() >5 and lights:\n",
    "        #turn off lights\n",
    "        lights = False\n",
    "        playmusic = False\n",
    "        logger.info(\"turn off lights\")\n",
    "        light.set_light_state(lights)\n",
    "\n",
    "        # print(\"turn off lights\")\n",
    "        # print(\"stop music\")\n",
    "    ## air con control"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When not moving/moving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OnNotMoving(notmoving:bool,startnotmovingtime:float,haveperson:bool):\n",
    "    # Here are some examples\n",
    "    ## if person is not lying on bed and not moving for 20 seconds, call for the ambulance\n",
    "    global lyingonbed,callambulance\n",
    "    if notmoving and startnotmovingtime-time.time() >20 and not lyingonbed and haveperson:\n",
    "        #call for ambulance\n",
    "        callambulance = True\n",
    "        logger.info(\"call for ambulance\")\n",
    "        ambulance_update()\n",
    "        # print(\"call for ambulance\")\n",
    "    else:\n",
    "        callambulance = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temperature handle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "countOfDangerTemp = 0\n",
    "def OnTemperature(tempPerson:list[float],tempBed):\n",
    "    # Here are some examples\n",
    "    ## if temperature of the person is more than 40 degrees or lower than 30 degrees, call for the ambulance\n",
    "    global callambulance,countOfDangerTemp,aircon,aircontemp,lastairconchange\n",
    "    logger.info(\"tempPerson: {tempPerson}, tempBed: {tempBed}\".format(tempPerson=tempPerson,tempBed=tempBed))\n",
    "    if  len(tempPerson) > 0 and (max(tempPerson) > 40 or min(tempPerson) < 30):\n",
    "        countOfDangerTemp +=1\n",
    "        if countOfDangerTemp > 5:\n",
    "            #call for ambulance\n",
    "            callambulance = True\n",
    "            logger.info(\"call for ambulance\")\n",
    "            ambulance_update()\n",
    "            # print(\"call for ambulance\")\n",
    "    else:\n",
    "        callambulance = False\n",
    "        ambulance_update()\n",
    "        countOfDangerTemp = 0\n",
    "    # ideal bed temp is betweeen 27 - 31\n",
    "    # body temp is 36\n",
    "    timenow = time.time()\n",
    "    if tempBed <0:\n",
    "        return\n",
    "    if len(tempPerson) > 0 and tempBed < 27:\n",
    "        if not aircon:\n",
    "            aircon = True\n",
    "            logger.info(\"Aircon on\")\n",
    "            fan.set_fan_state(aircon)\n",
    "\n",
    "            # print(\"Aircon on\")\n",
    "\n",
    "        if lastairconchange - timenow > 300:#i.e. 5min\n",
    "            aircontemp +=1\n",
    "            lastairconchange = timenow\n",
    "    elif len(tempPerson) > 0 and tempBed  > 31:\n",
    "        if not aircon:\n",
    "            aircon = True\n",
    "            logger.info(\"Aircon on\")\n",
    "            # print(\"Aircon on\")\n",
    "\n",
    "        if lastairconchange - timenow > 300:#i.e. 5min\n",
    "            aircontemp +=1\n",
    "            lastairconchange = timenow\n",
    "            \n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add more if you like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Action main\n",
    "def actions():\n",
    "    t1 = threading.Thread(target=OnLyingBed,args=(lyingonbed,startlyingtime,notlyingtimestart))\n",
    "    t2 = threading.Thread(target=OnTouchingPhone,args=(Touchingphone,starttouchingtime))\n",
    "    t3 = threading.Thread(target=OnSleeping,args=(sleeping,startsleepingtime))\n",
    "    t4 = threading.Thread(target=OnNotMoving,args=(notmoving,startnotmovingtime,personexist))\n",
    "    t5 = threading.Thread(target=OnTemperature,args=(tempPerson,tempBed))\n",
    "    ## Add more if you like\n",
    "    t1.start()\n",
    "    t2.start()\n",
    "    t3.start()\n",
    "    t4.start()\n",
    "    t5.start()\n",
    "    \n",
    "def ambulance_update():\n",
    "    if callambulance:\n",
    "        threading.Thread(target=call_ambulance).start()\n",
    "    else:\n",
    "        halt_ambulance()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play amublance sound with buzzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "playing_lock = False\n",
    "term_ambulance = False\n",
    "def call_ambulance():\n",
    "    global playing_lock\n",
    "    if playing_lock:\n",
    "        return\n",
    "    playing_lock = True\n",
    "    flutuator = False\n",
    "    while not term_ambulance:\n",
    "        if flutuator:\n",
    "            flutuator = False\n",
    "        else:\n",
    "            flutuator = True\n",
    "        buzzer.send_buzzer_command(1480 if flutuator else 1407, 1000)\n",
    "        time.sleep(1)\n",
    "    playing_lock = False\n",
    "def halt_ambulance():\n",
    "   global term_ambulance\n",
    "   term_ambulance = True"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play music"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "music_player = AudioPlayer('music/Chopin_Nocturne_E_Flat_Major_Op9_No2.mp3')\n",
    "def play_music():\n",
    "    music_player.play(block=False)\n",
    "def stop_music():\n",
    "    music_player.stop()   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main_analysis():\n",
    "    logger.info(\"Main starts\")\n",
    "    # print(\"Main starts\")\n",
    "    imagedisplay = widgets.Image(width=480, height=640)\n",
    "    display(imagedisplay,display_id=\"1\")\n",
    "    display(button_download)\n",
    "    while term:\n",
    "        #get data\n",
    "        if live_connection.new_frame_avaliable:\n",
    "            logger.info(\"new frame\")\n",
    "            (frame,thermaldata)= live_connection.getcurrentframe()\n",
    "            \n",
    "            ## get into threading\n",
    "            t2 = threading.Thread(target=worker,args=(frame,thermaldata))\n",
    "            t2.start()\n",
    "\n",
    "\n",
    "def worker(frame,thermaldata):\n",
    "    ## convert bytes to image\n",
    "    timenow = time.time()\n",
    "    image = Image.open(BytesIO(frame))\n",
    "    ## make prediction\n",
    "    results = model(image)\n",
    "    ## display the image\n",
    "    t3 = threading.Thread(target=SetDisplay,args=(results,))\n",
    "    t3.start()\n",
    "    ## get the result\n",
    "    pandas = results.pandas().xyxy[0]\n",
    "    size = results.pandas().ims[0].shape\n",
    "    # DETECTIONS\n",
    "    detections(pandas,thermaldata,size,timenow)\n",
    "    # ACTIONS\n",
    "    actions()\n",
    "    \n",
    "def SetDisplay(results):\n",
    "    global posLogStream,logdeque\n",
    "    results.render()    \n",
    "    update_display(Image.fromarray(results.ims[0]),display_id=\"1\")\n",
    "    display(button_download)\n",
    "    # print body temp and bed temp\n",
    "    print(\"Body temp: {tempPerson}, Bed temp: {tempBed}\".format(tempPerson=tempPerson,tempBed=tempBed))\n",
    "    # print last 10 lines of log\n",
    "    LogStream.seek(posLogStream)\n",
    "    for lines in LogStream.readlines():\n",
    "        logdeque.append(lines)\n",
    "    posLogStream = LogStream.tell()\n",
    "    print(''.join(logdeque))\n",
    "\n",
    "            "
   ]
  }
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