Mask Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pyttsx3\n",
    "import 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": [
    "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\")\n",
    "        engine.runAndWait()\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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}