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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize dataset\n",
"\n",
"Utilities to visualize episodes from a dataset. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import animation\n",
"import pathlib\n",
"from IPython.display import Video\n",
"import numpy as np\n",
"import os\n",
"\n",
"dataset_path = pathlib.Path(os.path.abspath('')).parent / 'data/stickman_example'\n",
"\n",
"directory = dataset_path.expanduser()\n",
"filenames = sorted(directory.glob('*.npz'))\n",
"if len(filenames) == 0:\n",
" raise ValueError(\"Empty directory (or no episodes)\")\n",
"\n",
"try:\n",
" filenames_dict = { int(str(f).replace(str(dataset_path), \"\").split(\"-\")[0][1:]) : f for f in filenames}\n",
"except Exception as e:\n",
" print(\"Error:\", e)\n",
"\n",
"print(directory)\n",
"print(len(filenames))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ep_num = next(iter(filenames_dict))\n",
"\n",
"filename = filenames_dict[ep_num]\n",
"with filename.open('rb') as f:\n",
" episode = np.load(f)\n",
" episode = {k: episode[k] for k in episode.keys()}\n",
"\n",
"# Show reward on top with red/green bar\n",
"pix_rew_max = np.round(episode['reward'] / 2 * 64)\n",
"for ob, pix_n in zip(episode['observation'], pix_rew_max):\n",
" if pix_n < 0:\n",
" pix_n = abs(pix_n)\n",
" ob[:, 0, :int(pix_n+1)] = np.array([255,0,0]).reshape(3,1)\n",
" else:\n",
" ob[:, 0, :int(pix_n+1)] = np.array([0,255,0]).reshape(3,1)\n",
"\n",
"# # np array with shape (frames, height, width, channels)\n",
"video = np.transpose(episode['observation'], axes=[0,2,3,1])\n",
"\n",
"fig = plt.figure(frameon=False)\n",
"ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
"ax.set_axis_off()\n",
"fig.add_axes(ax)\n",
"fig.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
"fig.set_size_inches(2,2)\n",
"im = ax.imshow(video[0,:,:,:])\n",
"plt.close() # this is required to not display the generated image\n",
"\n",
"def init():\n",
" im.set_data(video[0,:,:,:])\n",
"\n",
"def animate(i):\n",
" im.set_data(video[i,:,:,:])\n",
" return im\n",
"\n",
"print('Episode reward', np.sum(episode['reward']))\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init, frames=video.shape[0],interval=45)\n",
"file_path = str(pathlib.Path(os.path.abspath('')) / 'videos/temp.mp4')\n",
"anim.save(file_path)\n",
"print('Video file', file_path)\n",
"Video(file_path)"
]
}
],
"metadata": {
"interpreter": {
"hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf"
},
"kernelspec": {
"display_name": "Python 3.8.10 ('base')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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