demo.ipynb

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   "execution_count": 1,
   "metadata": {
    "pycharm": {
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   "outputs": [],
   "source": [
    "from mmaction.apis import init_recognizer, inference_recognizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
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   "source": [
    "config_file = '../configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py'\n",
    "# download the checkpoint from model zoo and put it in `checkpoints/`\n",
    "checkpoint_file = '../checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "# build the model from a config file and a checkpoint file\n",
    "model = init_recognizer(config_file, checkpoint_file, device='cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
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   "outputs": [],
   "source": [
    "# test a single video and show the result:\n",
    "video = 'demo.mp4'\n",
    "label = '../../tools/data/kinetics/label_map_k400.txt'\n",
    "results = inference_recognizer(model, video)\n",
    "\n",
    "labels = open(label).readlines()\n",
    "labels = [x.strip() for x in labels]\n",
    "results = [(labels[k[0]], k[1]) for k in results]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arm wrestling:  29.61644\n",
      "rock scissors paper:  10.754839\n",
      "shaking hands:  9.9084\n",
      "clapping:  9.189912\n",
      "massaging feet:  8.305307\n"
     ]
    }
   ],
   "source": [
    "# show the results\n",
    "for result in results:\n",
    "    print(f'{result[0]}: ', result[1])"
   ]
  }
 ],
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