Issue#5_IIT2019092.ipynb

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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "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",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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(0)\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",
    "    color_dict={0:(0,255,0),1:(0,0,255)}\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) #predict.classes() will return a numpy array of class predictions.\n",
    "        #print(pred)\n",
    "        _, accuracy=mymodel.predict(face_img)[0]\n",
    "        \n",
    "        \n",
    "        if  (pred[0]==0 and accuracy<0.5):\n",
    "            cv2.putText(img,'MASK',(250,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),4) \n",
    "            cv2.putText(img,f'Accuracy (%): {(1-accuracy)*100}',(250,100),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),4)\n",
    "            cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2) #Display a green rectangular frame of thickness 2. \n",
    "        else:\n",
    "            cv2.putText(img,'NO MASK',(250,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),4)\n",
    "            cv2.putText(img,f'Accuracy (%): {accuracy*100}',(250,100),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),4)\n",
    "            cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2) #Display a red rectangular frame of thickness 2.\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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