demo/vis_skeleton.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/trungct/miniconda3/envs/pyskl/lib/python3.7/site-packages/requests/__init__.py:114: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (5.1.0)/charset_normalizer (3.1.0) doesn't match a supported version!\n",
      "  RequestsDependencyWarning,\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "from pyskl.smp import *\n",
    "from pyskl.utils.visualize import Vis3DPose, Vis2DPose\n",
    "from mmcv import load, dump\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../tools/data/label_map/nturgbd_120.txt', 'r') as file:\n",
    "    labels = [label.strip() for label in file.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download annotations\n",
    "download_file('http://download.openmmlab.com/mmaction/pyskl/demo/annotations/ntu60_samples_hrnet.pkl', 'ntu60_2d.pkl')\n",
    "download_file('http://download.openmmlab.com/mmaction/pyskl/demo/annotations/ntu60_samples_3danno.pkl', 'ntu60_3d.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: reading\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 108/108 [00:00<00:00, 3359.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Building video trung.mp4.\n",
      "Moviepy - Writing video trung.mp4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                               "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Done !\n",
      "Moviepy - video ready trung.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Visualize 2D Skeletons without video\n",
    "annotations = load('../data/nturgbd/ntu60_hrnet.pkl')['annotations']\n",
    "annotations = [anno for anno in annotations if labels[anno['label']] == \"reading\"]\n",
    "index = 5\n",
    "anno = annotations[index]\n",
    "print(f\"Label: {labels[anno['label']]}\")\n",
    "vid = Vis2DPose(anno, thre=0.2, out_shape=(540, 960), layout='coco', fps=30, video=None)\n",
    "# vid.ipython_display()\n",
    "# vid.write_videofile('trung.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "put on headphone\n",
      "take off headphone\n",
      "shoot at the basket\n",
      "bounce ball\n",
      "tennis bat swing\n",
      "juggling table tennis balls\n",
      "hush (quite)\n",
      "flick hair\n",
      "thumb up\n",
      "thumb down\n",
      "make ok sign\n",
      "make victory sign\n",
      "staple book\n",
      "counting money\n",
      "cutting nails\n",
      "cutting paper (using scissors)\n",
      "snapping fingers\n",
      "open bottle\n",
      "sniff (smell)\n",
      "squat down\n",
      "toss a coin\n",
      "fold paper\n",
      "ball up paper\n",
      "play magic cube\n",
      "apply cream on face\n",
      "apply cream on hand back\n",
      "put on bag\n",
      "take off bag\n",
      "put something into a bag\n",
      "take something out of a bag\n",
      "open a box\n",
      "move heavy objects\n",
      "shake fist\n",
      "throw up cap/hat\n",
      "hands up (both hands)\n",
      "cross arms\n",
      "arm circles\n",
      "arm swings\n",
      "running on the spot\n",
      "butt kicks (kick backward)\n",
      "cross toe touch\n",
      "side kick\n",
      "yawn\n",
      "stretch oneself\n",
      "blow nose\n",
      "hit other person with something\n",
      "wield knife towards other person\n",
      "knock over other person (hit with body)\n",
      "grab other person’s stuff\n",
      "shoot at other person with a gun\n",
      "step on foot\n",
      "high-five\n",
      "cheers and drink\n",
      "carry something with other person\n",
      "take a photo of other person\n",
      "follow other person\n",
      "whisper in other person’s ear\n",
      "exchange things with other person\n",
      "support somebody with hand\n",
      "finger-guessing game (playing rock-paper-scissors)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                               \r"
     ]
    }
   ],
   "source": [
    "# Visualize 2D Skeletons without video\n",
    "annotations = load('../data/nturgbd/ntu60_hrnet.pkl')['annotations']\n",
    "for anno_type in labels:\n",
    "    save_dir = f'examples/{anno_type}'\n",
    "    os.makedirs(save_dir, exist_ok=True)\n",
    "    anns = [anno for anno in annotations if labels[anno['label']] == anno_type]\n",
    "    if len(anns) < 10:\n",
    "        print(anno_type)\n",
    "    # for i in range(10):\n",
    "    #     anno = anns[i]\n",
    "    #     vid = Vis2DPose(anno, thre=0.2, out_shape=(540, 960), layout='coco', fps=30, video=None)\n",
    "    #     vid.write_videofile(os.path.join(save_dir, f\"{i}.mp4\"), verbose = False, logger = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize 2D Skeletons with the original RGB Video\n",
    "annotations = load('ntu60_2d.pkl')\n",
    "index = 0\n",
    "anno = annotations[index]\n",
    "frame_dir = anno['frame_dir']\n",
    "video_url = f\"http://download.openmmlab.com/mmaction/pyskl/demo/nturgbd/{frame_dir}.avi\"\n",
    "download_file(video_url, frame_dir + '.avi')\n",
    "vid = Vis2DPose(anno, thre=0.2, out_shape=(540, 960), layout='coco', fps=12, video=frame_dir + '.avi')\n",
    "vid.ipython_display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter stderr:\n",
      "[libopenh264 @ 0x563f9eabc340] Incorrect library version loaded\n",
      "Error initializing output stream 0:0 -- Error while opening encoder for output stream #0:0 - maybe incorrect parameters such as bit_rate, rate, width or height\n",
      "\n"
     ]
    },
    {
     "ename": "CalledProcessError",
     "evalue": "Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '800x800', '-pix_fmt', 'rgba', '-r', '12', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-y', 'S013C002P019R001A034.mp4']' returned non-zero exit status 1.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1075\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0manim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_anim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1076\u001b[0;31m                 \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Clear the initial frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1077\u001b[0m             \u001b[0mframe_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1695\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1696\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1697\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1717\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1718\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pyskl/pyskl/utils/visualize.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mangle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: can't set attribute",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_19038/1592260546.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0manno\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mannotations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0manno\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPreNormalize3D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manno\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# * Need Pre-Normalization before Visualization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mvid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVis3DPose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nturgb+d'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m45\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_grid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mvid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_display\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pyskl/pyskl/utils/visualize.py\u001b[0m in \u001b[0;36mVis3DPose\u001b[0;34m(item, layout, fps, angle, fig_size, with_grid)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mani\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFuncAnimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepeat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m     \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0mvideo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVideoFileClip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1091\u001b[0m                         \u001b[0mprogress_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m                         \u001b[0mframe_number\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m                 \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m    128\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mfinish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0moverridden_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Inline _cleanup() once cleanup() is removed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgrab_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_cleanup\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m             raise subprocess.CalledProcessError(\n\u001b[0;32m--> 374\u001b[0;31m                 self._proc.returncode, self._proc.args, out, err)\n\u001b[0m\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3.4\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCalledProcessError\u001b[0m: Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '800x800', '-pix_fmt', 'rgba', '-r', '12', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-y', 'S013C002P019R001A034.mp4']' returned non-zero exit status 1."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7f9daa6d87a0> (for post_execute):\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "can't set attribute",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m                 \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2058\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2059\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2060\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2062\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    434\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    435\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 436\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2853\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2855\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2857\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_without_rendering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_event\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1777\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'draw_event'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1778\u001b[0m         \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDrawEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1779\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1781\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mresize_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    290\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 292\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    293\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m                         \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_exception_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_get_running_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"headless\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m                     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m                 \u001b[0;31m# this does not capture KeyboardInterrupt, SystemExit,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# and GeneratorExit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_start\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    905\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m         \u001b[0;31m# Now do any initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 907\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;31m# Add our callback for stepping the animation and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1694\u001b[0m                 )\n\u001b[1;32m   1695\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1696\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1697\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1698\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pyskl/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1716\u001b[0m         \u001b[0;31m# Call the func with framedata and args. If blitting is desired,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1717\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1718\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1720\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pyskl/pyskl/utils/visualize.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mangle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkinematic_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: can't set attribute"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize 3D Skeletons\n",
    "from pyskl.datasets.pipelines import PreNormalize3D\n",
    "annotations = load('ntu60_3d.pkl')\n",
    "index = 0\n",
    "anno = annotations[index]\n",
    "anno = PreNormalize3D()(anno)  # * Need Pre-Normalization before Visualization\n",
    "vid = Vis3DPose(anno, layout='nturgb+d', fps=12, angle=(30, 45), fig_size=(8, 8), with_grid=False)\n",
    "vid.ipython_display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean directories\n",
    "os.remove('ntu60_3d.pkl')\n",
    "os.remove('ntu60_2d.pkl')\n",
    "for f in glob.glob(\"S*.avi\"):\n",
    "    os.remove(f)\n",
    "os.remove('__temp__.mp4')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8516388d98336f8f25b57d66898c29467d9fc2c465b9ee1e16d04a4702b7bff1"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}