Project_4.0-mask-with-arduino.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4.0 cropping added\n",
    "import h5py\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pyttsx3\n",
    "import serial, time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading pretrained model\n",
    "model = keras.models.load_model('maskDetector.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Arduino Configuration\n",
    "port = '/dev/cu.usbmodem14101'\n",
    "arduino = serial.Serial(port, 9600, timeout=.1)\n",
    "time.sleep(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Process which should be done if mask is detected\n",
    "def found():\n",
    "    data = \"1\"\n",
    "    arduino.write(data.encode())\n",
    "    \n",
    "    time.sleep(4) #meanwhile arduino will detect hand and will spray sanitizer\n",
    "                \n",
    "    incoming = arduino.readline()\n",
    "    incoming = incoming.decode()\n",
    "    \n",
    "    if incoming != \"done\":\n",
    "        engine = pyttsx3.init();\n",
    "        engine.say(\"Please sanitize your hands\");\n",
    "        engine.runAndWait() ;\n",
    "    elif incoming == \"done\":\n",
    "        engine = pyttsx3.init();\n",
    "        engine.say(\"Good to go!\");\n",
    "        engine.runAndWait() ;\n",
    "        time.sleep(3) #Halt for some time to let go one person and wait for other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "status = ['Mask','Without Mask']\n",
    "#Function for reading images and predicting\n",
    "def detect(img):\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = cv2.resize(img, (50, 50))\n",
    "\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    output = model.predict(x)\n",
    "    output = output[0][0]\n",
    "    tag = int(output)    \n",
    "        \n",
    "    if(status[tag]==\"Mask\"):      #Here mask is found and going for further process\n",
    "        engine = pyttsx3.init();\n",
    "        engine.say(\"Mask found proceed for hand sanitizer\")\n",
    "        engine.runAndWait()\n",
    "        found()\n",
    "    else:\n",
    "        engine = pyttsx3.init();\n",
    "        engine.say(\"Mask not found\")\n",
    "        engine.runAndWait()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') \n",
    "while True:\n",
    "    ret, img = cap.read()\n",
    "    img = cv2.flip( img, 1 )\n",
    "    show = img\n",
    "    img = cv2.resize(img, (0,0), fx=0.5, fy=0.5) \n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "   \n",
    "    for (x,y,w,h) in faces:\n",
    "        send = img[y:y+h, x:x+w]\n",
    "        detect(send)\n",
    "    time.sleep(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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