3. DETECTING MASKS.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING LIBRARIES\n",
    "\n",
    "from keras.models import load_model\n",
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
     ]
    }
   ],
   "source": [
    "# LOADING DATA AND CASCADE CLASSIFIER \n",
    "\n",
    "model = load_model('model-010.model')\n",
    "face_clsfr=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "source=cv2.VideoCapture(0)\n",
    "labels_dict={1:'MASK',0:'NO MASK'}\n",
    "color_dict={1:(0,255,0),0:(0,0,255)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# READING FRAME BY FRAME FROM VIDEO OR CAMERA \n",
    "# DRAWING RECTANGLES AFTER CONVERTING IMAGE TO GRAYSCLAE AND RESIZING IT\n",
    "# ADDING TEXT ACCORDING TO LABEL WHETHER IT IS 0 OR 1\n",
    "# SHOWING EACH FRAME AND WAITING FOR ESCAPE KEY TO TERMINATE\n",
    "\n",
    "while(True):\n",
    "    ret,img=source.read()\n",
    "    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces=face_clsfr.detectMultiScale(gray,1.3,5)  \n",
    "    for x,y,w,h in faces:\n",
    "        face_img=gray[y:y+w,x:x+w]\n",
    "        resized=cv2.resize(face_img,(100,100))\n",
    "        normalized=resized/255.0\n",
    "        reshaped=np.reshape(normalized,(1,100,100,1))\n",
    "        result=model.predict(reshaped)\n",
    "        label=np.argmax(result,axis=1)[0]\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,0.8,(255,255,255),2)\n",
    "    cv2.imshow('LIVE',img)\n",
    "    key=cv2.waitKey(1)\n",
    "    if(key==27):\n",
    "        break\n",
    "        \n",
    "        \n",
    "cv2.destroyAllWindows()\n",
    "source.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "withMask = image.load_img('withMask.png',target_size=(440,440))\n",
    "plt.imshow(withMask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "withoutMask = image.load_img('withoutMask.png',target_size=(640,640))\n",
    "plt.imshow(withoutMask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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