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{
"cells": [
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"import av\n",
"import math\n",
"from fractions import Fraction\n",
"import contextlib\n",
"from pathlib import Path\n",
"import numpy as np\n",
"np.seterr(\"raise\")\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PosixPath('/home/roman/Desktop/multi-part-audio-recording-pi-0.7.3-rc3-stage/pi-0.7.3-rc3-stage/exports/043/world.mp4')]"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"video_path = Path(\"/home/roman/Desktop/multi-part-audio-recording-pi-0.7.3-rc3-stage/pi-0.7.3-rc3-stage/exports/043\").glob(\"world*.mp4\")\n",
"video_path = list(video_path)\n",
"video_path"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/home/roman/Desktop/multi-part-audio-recording-pi-0.7.3-rc3-stage/pi-0.7.3-rc3-stage/exports/043/world.mp4')"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"world_1_video = video_path[-1]\n",
"world_1_video"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<av.InputContainer '/home/roman/Desktop/multi-part-audio-recording-pi-0.7.3-rc3-stage/pi-0.7.3-rc3-stage/exports/043/world.mp4'>"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"container = av.open(str(world_1_video))\n",
"container"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<av.VideoStream #0 mpeg4, yuv420p 1088x1080 at 0x7efbf4070b38>"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stream = container.streams.video[0]\n",
"stream"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" ...]"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frames = [f for p in container.demux(video=0) for f in stream.decode(p)]\n",
"frames"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 2108, 4121, ..., 3987907, 3991803, 3994405])"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pts = np.asarray([frame.pts for frame in frames])\n",
"pts"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1662"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diff(pts).min()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"18150"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diff(pts).max()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2404.819385912101"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diff(pts).mean()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/home/roman/Desktop/multi-part-audio-recording-pi-0.7.3-rc3-stage/pi-0.7.3-rc3-stage/exports/043/world_timestamps.npy')"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"world_1_timestamps = world_1_video.with_name(world_1_video.stem + \"_timestamps\").with_suffix(\".npy\")\n",
"world_1_timestamps"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.2169855, 1.2491635, 1.2798785, ..., 62.0685515, 62.1280035,\n",
" 62.1677085])"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"timestamps = np.load(str(world_1_timestamps))\n",
"timestamps"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1.216985499, 1.2491515171582361, 1.279867928236286, ...,\n",
" 62.06854573398894, 62.12799488329847, 62.167698858273674],\n",
" dtype=object)"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reconstructed_timestamps = (pts * Fraction(1, 65535)) + timestamps[0]\n",
"reconstructed_timestamps = (pts * stream.time_base) + timestamps[0]\n",
"reconstructed_timestamps"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.0, 1.1981841763875067e-05, 1.0570763714135012e-05, ...,\n",
" 5.765011060532288e-06, 8.615701531766717e-06,\n",
" 9.640726332804661e-06], dtype=object)"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assert len(timestamps) == len(reconstructed_timestamps)\n",
"timestamp_diffs = np.abs(timestamps - reconstructed_timestamps)\n",
"timestamp_diffs"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"timestamp_diffs.min()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.5256885633974093e-05"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"timestamp_diffs.max()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7.644094047243912e-06"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"timestamp_diffs.mean()"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-1338"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"world_1_exported_frame_count = 324\n",
"world_1_actual_frame_count = len(frames)\n",
"world_1_exported_frame_count - world_1_actual_frame_count"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(timestamp_diffs[timestamp_diffs > 3.25])"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
"def plot_timestamps(actual_timestamps, reconstructed_timestamps):\n",
" assert len(actual_timestamps) == len(reconstructed_timestamps)\n",
"\n",
" x = np.arange(len(timestamps))\n",
"\n",
" plt.figure(figsize=(16, 4))\n",
" plt.plot(x, actual_timestamps, label=\"actual_timestamps\")\n",
" plt.plot(x, reconstructed_timestamps, label=\"reconstructed_timestamps\")\n",
"\n",
" plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_timestamps(timestamps, reconstructed_timestamps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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