Lecture 6.2 - Face & Eye Detection.ipynb

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   "source": [
    "## Face & Eye Detection using HAAR Cascade Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
    "\n",
    "image = cv2.imread('images/Trump.jpg')\n",
    "gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "\n",
    "if faces is ():\n",
    "    print 'no faces'\n",
    "\n",
    "for (x,y,w,h) in faces:\n",
    "    cv2.rectangle(image, (x,y), (x+w, y+h), (100,100,100), 2)\n",
    "    cv2.imshow('image', image)\n",
    "    cv2.waitKey(0)\n",
    "    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's combine face and eye detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "C:\\build\\2_4_winpack-bindings-win64-vc14-static\\opencv\\modules\\highgui\\src\\window.cpp:261: error: (-215) size.width>0 && size.height>0 in function cv::imshow\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-c508bb40a77e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Our Face Extractor'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mface_detector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m13\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m#13 is the Enter Key\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: C:\\build\\2_4_winpack-bindings-win64-vc14-static\\opencv\\modules\\highgui\\src\\window.cpp:261: error: (-215) size.width>0 && size.height>0 in function cv::imshow\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
    "eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')\n",
    "\n",
    "def face_detector(img, size=0.5):\n",
    "    # Convert image to grayscale\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "    if faces is ():\n",
    "        return img\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        x = x - 50\n",
    "        w = w + 50\n",
    "        y = y - 50\n",
    "        h = h + 50\n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "        roi_gray = gray[y:y+h, x:x+w]\n",
    "        roi_color = img[y:y+h, x:x+w]\n",
    "        eyes = eye_classifier.detectMultiScale(roi_gray)\n",
    "        \n",
    "        for (ex,ey,ew,eh) in eyes:\n",
    "            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,0,255),2) \n",
    "            \n",
    "    roi_color = cv2.flip(roi_color,1)\n",
    "    return roi_color\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    cv2.imshow('Our Face Extractor', face_detector(frame))\n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's make a live face & eye detection, keeping the face inview at all times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
    "eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')\n",
    "\n",
    "def face_detector(img, size=0.5):\n",
    "    # Convert image to grayscale\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "    img = cv2.flip(img, 1)\n",
    "\n",
    "    if faces is ():\n",
    "        return img\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        x = x - 50\n",
    "        w = w + 50\n",
    "        y = y - 50\n",
    "        h = h + 50\n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "        \n",
    "        roi_gray = gray[y:y+h, x:x+w]\n",
    "\n",
    "        eyes = eye_classifier.detectMultiScale(roi_gray,1.1,5)\n",
    "        \n",
    "        for (ex,ey,ew,eh) in eyes:\n",
    "            cv2.rectangle(img,(x+ex,y+ey),(x+ex+ew,y+ey+eh),(0,0,255),2) \n",
    "            \n",
    "            \n",
    "    return img\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    cv2.imshow('Our Face Extractor', face_detector(frame))\n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()      "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Tuning Cascade Classifiers\n",
    "\n",
    "*ourClassifier*.**detectMultiScale**(input image, **Scale Factor** , **Min Neighbors**)\n",
    "\n",
    "- **Scale Factor**\n",
    "Specifies how much we reduce the image size each time we scale. E.g. in face detection we typically use 1.3. This means we reduce the image by 30% each time it’s scaled. Smaller values, like 1.05 will take longer to compute, but will increase the rate of detection.\n",
    "\n",
    "\n",
    "\n",
    "- **Min Neighbors**\n",
    "Specifies the number of neighbors each potential window should have in order to consider it a positive detection. Typically set between 3-6. \n",
    "It acts as sensitivity setting, low values will sometimes detect multiples faces over a single face. High values will ensure less false positives, but you may miss some faces.  \n"
   ]
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