drownsiness_detection.ipynb

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    "# Drowsiness Detection OpenCV\n",
    "\n",
    "\n",
    "This code can detect your eyes and alert when the user is drowsy.\n",
    "\n",
    "## Applications\n",
    "This can be used by riders who tend to drive for a longer period of time that may lead to accidents.\n",
    "\n",
    "### Algorithm\n",
    "\n",
    "Each eye is represented by 6 (x, y)-coordinates, starting at the left-corner of the eye (as if you were looking at the person), and then working clockwise around the eye:.\n",
    "\n",
    "<img src=\"eye1.jpg\">\n",
    "\n",
    "### Condition\n",
    "\n",
    "It checks 20 consecutive frames and if the Eye Aspect ratio is lesst than 0.25, Alert is generated.\n",
    "\n",
    "#### Relationship\n",
    "\n",
    "<img src=\"eye2.png\">\n",
    "\n",
    "#### Summing up\n",
    "\n",
    "<img src=\"eye3.jpg\">\n",
    "\n",
    "jupyter kernel by Manuel Romero (mrm8488@gmail.com or @mrm8488)"
   ]
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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "from scipy.spatial import distance\n",
    "from imutils import face_utils\n",
    "import imutils\n",
    "import dlib\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eye_aspect_ratio(eye):\n",
    "\tA = distance.euclidean(eye[1], eye[5])\n",
    "\tB = distance.euclidean(eye[2], eye[4])\n",
    "\tC = distance.euclidean(eye[0], eye[3])\n",
    "\tear = (A + B) / (2.0 * C)\n",
    "\treturn ear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = 0.25\n",
    "frame_check = 20\n",
    "detect = dlib.get_frontal_face_detector()\n",
    "predict = dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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   "source": [
    "(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"left_eye\"]\n",
    "(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"right_eye\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap=cv2.VideoCapture(0)\n",
    "flag=0\n",
    "while True:\n",
    "\tret, frame=cap.read()\n",
    "\tframe = imutils.resize(frame, width=450)\n",
    "\tgray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\tsubjects = detect(gray, 0)\n",
    "\tfor subject in subjects:\n",
    "\t\tshape = predict(gray, subject)\n",
    "\t\tshape = face_utils.shape_to_np(shape)#converting to NumPy Array\n",
    "\t\tleftEye = shape[lStart:lEnd]\n",
    "\t\trightEye = shape[rStart:rEnd]\n",
    "\t\tleftEAR = eye_aspect_ratio(leftEye)\n",
    "\t\trightEAR = eye_aspect_ratio(rightEye)\n",
    "\t\tear = (leftEAR + rightEAR) / 2.0\n",
    "\t\tleftEyeHull = cv2.convexHull(leftEye)\n",
    "\t\trightEyeHull = cv2.convexHull(rightEye)\n",
    "\t\tcv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)\n",
    "\t\tcv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)\n",
    "\t\tif ear < thresh:\n",
    "\t\t\tflag += 1\n",
    "\t\t\t#print (flag)\n",
    "\t\t\tif flag >= frame_check:\n",
    "\t\t\t\tcv2.putText(frame, \"****************ALERT!****************\", (10, 30),\n",
    "\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "\t\t\t\tcv2.putText(frame, \"****************ALERT!****************\", (10,325),\n",
    "\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "\t\telse:\n",
    "\t\t\tflag = 0\n",
    "\tcv2.imshow(\"Frame\", frame)\n",
    "\tkey = cv2.waitKey(1) & 0xFF\n",
    "\tif key == ord(\"q\"):\n",
    "\t\tcv2.destroyAllWindows()\n",
    "\t\tcap.release()\n",
    "\t\tbreak\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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