Import_video_and_run_detector_in_frames.ipynb

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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Imports a video, acquires some frames and \n",
    "# performs (face) detection in those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "source": [
    "from os.path import isdir, isfile\n",
    "from functools import partial\n",
    "from numpy.random import randint\n",
    "import menpo.io as mio\n",
    "from menpo.visualize import print_progress\n",
    "from menpodetect.dlib import load_dlib_frontal_face_detector\n",
    "\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    from menpowidgets import visualize_images\n",
    "except NameError:\n",
    "    print('Maybe this is a terminal.')\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parse the video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = '/vol/atlas/homes/grigoris/videos_external/5_2016_kollias_dimitris/part_c/450_500/mp4/453BeatDropVines3Reaction.avi'\n",
    "assert isfile(p)\n",
    "v = mio.import_video(p, landmark_resolver=None, normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import video frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_frames = 20\n",
    "ims = []\n",
    "for i in range(min(max_frames, len(v))):\n",
    "    idx = randint(0, high=len(v))\n",
    "    ims.append(v[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detect and visualise the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "detector = partial(load_dlib_frontal_face_detector())\n",
    "for im in print_progress(ims):\n",
    "    detector(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "visualize_images(ims)"
   ]
  }
 ],
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