openCV_imp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import model_from_json\n",
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "#classifierLoad = tf.keras.models.load_model('model/modeltest.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "json_file = open(\"model.json\",\"r\")\n",
    "loaded_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_json)\n",
    "# loading the weights\n",
    "loaded_model.load_weights(\"model.h5\")\n",
    "print(\"Loaded model from disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model.compile(optimizer = 'adam', loss='binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for identifying and classifying faces we use haarcascade_frontalface_defaul\n",
    "# you can download it from https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml\n",
    "face_clsfr=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "source=cv2.VideoCapture(0)\n",
    "\n",
    "labels_dict={0:'MASK',1:'NO MASK'}\n",
    "color_dict={0:(0,255,0),1:(0,0,255)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "while(True):\n",
    "\n",
    "    ret,img=source.read()\n",
    "    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces=face_clsfr.detectMultiScale(gray,1.3,5)  \n",
    "\n",
    "    for (x,y,w,h) in faces:\n",
    "    \n",
    "        face_img=gray[y:y+w,x:x+w]\n",
    "        resized=cv2.resize(face_img,(100,100))\n",
    "        resized = np.stack((resized,)*3,axis = -1)\n",
    "        normalized=resized/255.0\n",
    "        reshaped=np.reshape(normalized,(1,100,100,3))\n",
    "        result=loaded_model.predict(reshaped)\n",
    "\n",
    "        label=np.argmax(result,axis=1)[0]\n",
    "      \n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),color_dict[label],2)\n",
    "        cv2.rectangle(img,(x,y-40),(x+w,y),color_dict[label],-1)\n",
    "        cv2.putText(img, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,(255,255,255),2)\n",
    "        \n",
    "        \n",
    "    cv2.imshow('LIVE',img)\n",
    "    key=cv2.waitKey(1)\n",
    "    \n",
    "    if(key==27):\n",
    "        break\n",
    "        \n",
    "cv2.destroyAllWindows()\n",
    "source.release()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1"
   ]
  },
  {
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}