effective_fps.ipynb

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   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "import av  # pip install av\n",
    "import numpy as np  # pip install numpy\n",
    "import pandas as pd  # pip install pandas\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "def mean_fps(df):\n",
    "    return 1.0 / df.diff().mean()\n",
    "\n",
    "def pts_dataframe(path):\n",
    "    cont = av.open(path)\n",
    "    stream = cont.streams.video[0]\n",
    "    packet_pts = [pack.pts for pack in cont.demux(stream)]\n",
    "    df = pd.DataFrame(packet_pts, columns=[\"packet_pts\"])\n",
    "    df.packet_pts *= stream.time_base\n",
    "    cont.close()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective FPS world_timestamps.csv: 29.61100354989063\n",
      "Effective FPS world.mp4: 60.0\n",
      "Effective FPS SL102world.mp4: 29.611004509264397\n"
     ]
    }
   ],
   "source": [
    "external_timestamps = \"world_timestamps.csv\"\n",
    "original = \"world.mp4\"\n",
    "exported = \"SL102world.mp4\"\n",
    "\n",
    "ts = pd.read_csv(external_timestamps)\n",
    "print(f\"Effective FPS {external_timestamps}: {mean_fps(ts).values[0]}\")\n",
    "\n",
    "for file in (original, exported):\n",
    "    print(f\"Effective FPS {file}: {mean_fps(pts_dataframe(file)).values[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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