Issue#8_IIT2019044.ipynb

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      "displayName": "ARPIT AGARWAL",
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    "id": "-vUDkKhNUqDB"
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   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "\n",
    "import keras\n",
    "from keras.models import load_model\n",
    "from keras.models import model_from_json\n",
    "\n",
    "\n",
    "import cv2\n",
    "import datetime\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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     "status": "error",
     "timestamp": 1595709048750,
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      "displayName": "ARPIT AGARWAL",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1011 02:54:31.305886 31820 deprecation.py:323] From <ipython-input-4-6177a902927d>:82: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    }
   ],
   "source": [
    "# IMPLEMENTING LIVE DETECTION OF FACE MASK\n",
    "\n",
    "# Importing the saved model from the IPython notebook\n",
    "mymodel=load_model('../train_model.h5')\n",
    "\n",
    "# Importing the Face Classifier XML file containing all features of the face\n",
    "face_classifier=cv2.CascadeClassifier('../haarcascade_frontalface_default.xml')\n",
    "\n",
    "filename = 'video.avi'\n",
    "frames_per_second = 24.0\n",
    "res = '480p'\n",
    "\n",
    "# Set resolution for the video capture\n",
    "# Function adapted from https://kirr.co/0l6qmh\n",
    "def change_res(cap, width, height):\n",
    "    cap.set(3, width)\n",
    "    cap.set(4, height)\n",
    "\n",
    "# Standard Video Dimensions Sizes\n",
    "STD_DIMENSIONS =  {\n",
    "    \"480p\": (640, 480),\n",
    "    \"720p\": (1280, 720),\n",
    "    \"1080p\": (1920, 1080),\n",
    "    \"4k\": (3840, 2160),\n",
    "}\n",
    "\n",
    "\n",
    "# grab resolution dimensions and set video capture to it.\n",
    "def get_dims(cap, res='720p'):\n",
    "    width, height = STD_DIMENSIONS[\"480p\"]\n",
    "    if res in STD_DIMENSIONS:\n",
    "        width,height = STD_DIMENSIONS[res]\n",
    "    ## change the current caputre device\n",
    "    ## to the resulting resolution\n",
    "    change_res(cap, width, height)\n",
    "    return width, height\n",
    "\n",
    "# Video Encoding, might require additional installs\n",
    "# Types of Codes: http://www.fourcc.org/codecs.php\n",
    "VIDEO_TYPE = {\n",
    "    'avi': cv2.VideoWriter_fourcc(*'XVID'),\n",
    "    #'mp4': cv2.VideoWriter_fourcc(*'H264'),\n",
    "    'mp4': cv2.VideoWriter_fourcc(*'XVID'),\n",
    "}\n",
    "\n",
    "def get_video_type(filename):\n",
    "    filename, ext = os.path.splitext(filename)\n",
    "    if ext in VIDEO_TYPE:\n",
    "      return  VIDEO_TYPE[ext]\n",
    "    return VIDEO_TYPE['avi']\n",
    "\n",
    "\n",
    "# To open a video via link to be inserted in the () of VideoCapture()\n",
    "# To open the web cam connected to your laptop/PC, write '0' (without quotes) in the () of VideoCapture()\n",
    "cap=cv2.VideoCapture(1)\n",
    "\n",
    "out = cv2.VideoWriter(filename, get_video_type(filename), 25, get_dims(cap, res))\n",
    "\n",
    "# fourcc = cv2.VideoWriter_fourcc(*'XVID') \n",
    "# out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))\n",
    "\n",
    "while cap.isOpened():\n",
    "    _,img=cap.read()\n",
    "\n",
    "    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    # detect MultiScale / faces\n",
    "    faces = face_classifier.detectMultiScale(rgb, 1.3, 5)\n",
    "\n",
    "    # Draw rectangles around each face\n",
    "    for (x, y, w, h) in faces:\n",
    "\n",
    "        #Save just the rectangle faces in SubRecFaces\n",
    "        face_img = rgb[y:y+w, x:x+w]\n",
    "\n",
    "        face_img=cv2.resize(face_img,(224,224))\n",
    "        face_img=face_img/255.0\n",
    "        face_img=np.reshape(face_img,(224,224,3))\n",
    "        face_img=np.expand_dims(face_img,axis=0)\n",
    "\n",
    "\n",
    "        pred=mymodel.predict_classes(face_img)\n",
    "        \n",
    "        _, accuracy = mymodel.predict(face_img)[0]\n",
    "        \n",
    "        if pred[0]==0:\n",
    "\n",
    "            cv2.putText(img,f'MASK: {accuracy*100}',(100,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),4)\n",
    "        else:\n",
    "\n",
    "            cv2.putText(img,f'NO MASK: {accuracy*100}',(100,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),4)\n",
    "            \n",
    "        datet=str(datetime.datetime.now())\n",
    "        cv2.putText(img,datet,(400,450),cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,255,255),1)\n",
    "\n",
    "    \n",
    "    out.write(img)\n",
    "    \n",
    "    # Show the image\n",
    "    cv2.imshow('LIVE DETECTION',img)\n",
    "    \n",
    "    # if key 'q' is press then break out of the loop\n",
    "    if cv2.waitKey(1)==ord('q'):\n",
    "        break\n",
    "    \n",
    "# Stop video\n",
    "cap.release()\n",
    "\n",
    "out.release()\n",
    "\n",
    "# Close all started windows\n",
    "cv2.destroyAllWindows()"
   ]
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