Lecture 7.4 - Mini Project # 8 - Yawn Detector and Counting.ipynb

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    "# Mini Project # 8 – Yawn Detector and Counting"
   ]
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     "text": [
      "C:\\ProgramData\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:57: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import dlib\n",
    "import numpy as np\n",
    "\n",
    "PREDICTOR_PATH = \"shape_predictor_68_face_landmarks.dat\"\n",
    "predictor = dlib.shape_predictor(PREDICTOR_PATH)\n",
    "#cascade_path='haarcascade_frontalface_default.xml'\n",
    "#cascade = cv2.CascadeClassifier(cascade_path)\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "\n",
    "\n",
    "def get_landmarks(im):\n",
    "    rects = detector(im, 1)\n",
    "\n",
    "    if len(rects) > 1:\n",
    "        return \"error\"\n",
    "    if len(rects) == 0:\n",
    "        return \"error\"\n",
    "    return np.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])\n",
    "\n",
    "\n",
    "def annotate_landmarks(im, landmarks):\n",
    "    im = im.copy()\n",
    "    for idx, point in enumerate(landmarks):\n",
    "        pos = (point[0, 0], point[0, 1])\n",
    "        cv2.putText(im, str(idx), pos,\n",
    "                    fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,\n",
    "                    fontScale=0.4,\n",
    "                    color=(0, 0, 255))\n",
    "        cv2.circle(im, pos, 3, color=(0, 255, 255))\n",
    "    return im\n",
    "\n",
    "def top_lip(landmarks):\n",
    "    top_lip_pts = []\n",
    "    for i in range(50,53):\n",
    "        top_lip_pts.append(landmarks[i])\n",
    "    for i in range(61,64):\n",
    "        top_lip_pts.append(landmarks[i])\n",
    "        \n",
    "    top_lip_all_pts = np.squeeze(np.asarray(top_lip_pts))\n",
    "    top_lip_mean = np.mean(top_lip_pts, axis=0)\n",
    "    return int(top_lip_mean[:,1])\n",
    "\n",
    "def bottom_lip(landmarks):\n",
    "    bottom_lip_pts = []\n",
    "    for i in range(65, 68):\n",
    "        bottom_lip_pts.append(landmarks[i])\n",
    "    for i in range(56, 59):\n",
    "        bottom_lip_pts.append(landmarks[i])\n",
    "    bottom_lip_all_pts = np.squeeze(np.asarray(bottom_lip_pts))\n",
    "    bottom_lip_mean = np.mean(bottom_lip_pts, axis=0)\n",
    "    return int(bottom_lip_mean[:,1])\n",
    "\n",
    "def mouth_open(image):\n",
    "    landmarks = get_landmarks(image)\n",
    "    \n",
    "    if landmarks == \"error\":\n",
    "        return image, 0\n",
    "    \n",
    "    image_with_landmarks = annotate_landmarks(image, landmarks)\n",
    "    top_lip_center = top_lip(landmarks)\n",
    "    bottom_lip_center = bottom_lip(landmarks)\n",
    "    lip_distance = abs(top_lip_center - bottom_lip_center)\n",
    "    return image_with_landmarks, lip_distance\n",
    "\n",
    "\n",
    "    #cv2.imshow('Result', image_with_landmarks)\n",
    "    #cv2.imwrite('image_with_landmarks.jpg',image_with_landmarks)\n",
    "    #cv2.waitKey(0)\n",
    "    #cv2.destroyAllWindows()\n",
    "    \n",
    "cap = cv2.VideoCapture(0)\n",
    "yawns = 0\n",
    "yawn_status = False\n",
    "\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "    image_landmarks, lip_distance = mouth_open(frame)\n",
    "    \n",
    "    prev_yawn_status = yawn_status\n",
    "    \n",
    "    if lip_distance > 25:\n",
    "        yawn_status = True\n",
    "        \n",
    "        cv2.putText(frame, \"Subject is Yawning\", (50, 450), \n",
    "                   cv2.FONT_HERSHEY_COMPLEX, 1, (0,  0, 255), 2)\n",
    "        \n",
    "        output_text = \"Yawn Count: \" + str(yawns + 1)\n",
    "        cv2.putText(frame, output_text, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 127), 2)\n",
    "        \n",
    "    else:\n",
    "        yawn_status = False\n",
    "        \n",
    "    if prev_yawn_status == True and yawn_status==False:\n",
    "        yawns += 1\n",
    "        \n",
    "    cv2.imshow('Live Landmarks', image_landmarks)\n",
    "    cv2.imshow('Yawn Detection', frame)\n",
    "    \n",
    "    if cv2.waitKey(1) == 13:\n",
    "        break\n",
    "        \n",
    "    \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    "
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