emotion_detector_from_webcam.ipynb

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   "source": [
    "import cv2\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from classify import Fer,IMAGE_SIZE\n",
    "from mtcnn.mtcnn import MTCNN\n",
    "\n",
    "# Initialise the fer model\n",
    "model = './output'\n",
    "fer = Fer(model)\n",
    "fer.build()\n",
    "\n",
    "# Create face detector\n",
    "detector = MTCNN()\n",
    "video = cv2.VideoCapture(0)\n",
    "while True:\n",
    "    # Capture frames\n",
    "    _, frame = video.read()\n",
    "    # Use MTCNN for detecting faces\n",
    "    result = detector.detect_faces(frame)\n",
    "    if result != []:\n",
    "        for person in result:\n",
    "            expression=''\n",
    "            bounding_box = person['box']            \n",
    "            x = bounding_box[0]\n",
    "            y = bounding_box[1]\n",
    "            w = bounding_box[0]+bounding_box[2]\n",
    "            h = bounding_box[1]+bounding_box[3]\n",
    "            \n",
    "            face_image = frame[y:h, x:w]\n",
    "            face_image = cv2.resize(face_image,(IMAGE_SIZE,IMAGE_SIZE),interpolation = cv2.INTER_AREA)\n",
    "            face_image = np.array([face_image],dtype=np.float32)\n",
    "            results = fer.predict(face_image)\n",
    "            if len(results) > 0:\n",
    "                expression = results[0]\n",
    "            \n",
    "            # display resulting frame\n",
    "            cv2.rectangle(frame,(x,y),(w,h),(0,155,155),2)\n",
    "            cv2.putText(frame,expression,(x,y),cv2.FONT_HERSHEY_SIMPLEX,1.0,(0,155,155))\n",
    "            cv2.imshow('frame',frame)\n",
    "            if cv2.waitKey(1) &0xFF == ord('q'):\n",
    "                break\n",
    "video.release()\n",
    "cv2.destroyAllWindows()        "
   ]
  }
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