{
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "from mmdet3d.apis import LidarDet3DInferencer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize inferencer\n",
    "inferencer = LidarDet3DInferencer('pointpillars_kitti-3class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# inference\n",
    "inputs = dict(points='./data/kitti/000008.bin')\n",
    "inferencer(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inference and visualize\n",
    "# NOTE: use the `Esc` key to exit Open3D window in Jupyter Notebook Environment\n",
    "inferencer(inputs, show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If your operating environment does not have a display device,\n",
    "# (e.g. a remote server), you can save the predictions and visualize\n",
    "# them in local devices.\n",
    "inferencer(inputs, show=False, out_dir='./remote_outputs')\n",
    "\n",
    "# Simulate the migration process\n",
    "%mv ./remote_outputs ./local_outputs\n",
    "\n",
    "# Visualize the predictions from the saved files\n",
    "# NOTE: use the `Esc` key to exit Open3D window in Jupyter Notebook Environment\n",
    "local_inferencer = LidarDet3DInferencer('pointpillars_kitti-3class')\n",
    "inputs = local_inferencer._inputs_to_list(inputs)\n",
    "local_inferencer.visualize_preds_fromfile(inputs, ['local_outputs/preds/000008.json'], show=True)"
   ]
  }
 ],
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  "pycharm": {
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    "cell_type": "raw",
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