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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import cv2\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"dataframes = [\n",
" \"../data/dataset-unfiltered.parquet\",\n",
" \"../data/dataset.parquet\",\n",
"]\n",
"df_path = dataframes[1]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_parquet(df_path)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>video_id</th>\n",
" <th>frame_idx</th>\n",
" <th>timestamp</th>\n",
" <th>base64_image</th>\n",
" <th>dim_0</th>\n",
" <th>dim_1</th>\n",
" <th>dim_2</th>\n",
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" <th>dim_5</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>8Ilh1ewceco</td>\n",
" <td>145</td>\n",
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" <td>0.086240</td>\n",
" <td>-0.029653</td>\n",
" <td>0.035949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8Ilh1ewceco</td>\n",
" <td>290</td>\n",
" <td>10.0</td>\n",
" <td>b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8Ilh1ewceco</td>\n",
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" <td>-0.018341</td>\n",
" <td>-0.006733</td>\n",
" <td>-0.007040</td>\n",
" <td>-0.008368</td>\n",
" <td>0.009755</td>\n",
" <td>-0.045662</td>\n",
" <td>0.116601</td>\n",
" <td>-0.000572</td>\n",
" <td>-0.000985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8Ilh1ewceco</td>\n",
" <td>580</td>\n",
" <td>20.0</td>\n",
" <td>b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH...</td>\n",
" <td>-0.015903</td>\n",
" <td>0.033545</td>\n",
" <td>0.009257</td>\n",
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" <td>0.010586</td>\n",
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" <td>0.020868</td>\n",
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" <td>0.010914</td>\n",
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" <td>0.063971</td>\n",
" <td>-0.038024</td>\n",
" <td>0.025840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8Ilh1ewceco</td>\n",
" <td>725</td>\n",
" <td>25.0</td>\n",
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" <td>0.023012</td>\n",
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" <td>-0.035481</td>\n",
" <td>-0.024743</td>\n",
" <td>-0.009812</td>\n",
" <td>0.035214</td>\n",
" <td>-0.008957</td>\n",
" <td>0.124215</td>\n",
" <td>-0.012410</td>\n",
" <td>0.040907</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 516 columns</p>\n",
"</div>"
],
"text/plain": [
" video_id frame_idx timestamp \n",
"0 8Ilh1ewceco 145 5.0 \\\n",
"1 8Ilh1ewceco 290 10.0 \n",
"2 8Ilh1ewceco 435 15.0 \n",
"3 8Ilh1ewceco 580 20.0 \n",
"4 8Ilh1ewceco 725 25.0 \n",
"\n",
" base64_image dim_0 dim_1 \n",
"0 b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH... 0.009040 0.003338 \\\n",
"1 b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH... 0.004891 0.006527 \n",
"2 b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH... -0.022159 0.020703 \n",
"3 b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH... -0.015903 0.033545 \n",
"4 b'/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgH... -0.010193 -0.012323 \n",
"\n",
" dim_2 dim_3 dim_4 dim_5 ... dim_502 dim_503 dim_504 \n",
"0 0.029684 -0.033058 0.040864 -0.006447 ... 0.033575 -0.019076 0.047166 \\\n",
"1 0.004417 -0.000323 0.006400 -0.024191 ... -0.043122 -0.010695 0.005672 \n",
"2 -0.021607 -0.019721 -0.006067 -0.035070 ... -0.017047 -0.018341 -0.006733 \n",
"3 0.009257 -0.033540 0.010586 -0.028067 ... -0.016532 0.012388 0.020868 \n",
"4 0.023012 -0.015893 0.047041 0.050783 ... -0.024013 -0.009684 -0.035481 \n",
"\n",
" dim_505 dim_506 dim_507 dim_508 dim_509 dim_510 dim_511 \n",
"0 -0.010574 -0.018608 -0.013465 -0.020017 0.086240 -0.029653 0.035949 \n",
"1 0.000172 -0.014442 -0.014647 -0.016840 0.100285 0.013794 0.015046 \n",
"2 -0.007040 -0.008368 0.009755 -0.045662 0.116601 -0.000572 -0.000985 \n",
"3 -0.012635 0.010914 0.009203 -0.010078 0.063971 -0.038024 0.025840 \n",
"4 -0.024743 -0.009812 0.035214 -0.008957 0.124215 -0.012410 0.040907 \n",
"\n",
"[5 rows x 516 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.head())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"60844"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wierdly all the timestamps are rounded to the nearest second o.O\n",
"(df[\"timestamp\"].apply(lambda x: x % 1) == 0).sum()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1417/1417 [00:02<00:00, 626.05it/s]\n"
]
}
],
"source": [
"video_fps = {}\n",
"for video_id in tqdm(df.video_id.unique()):\n",
" video_path = f\"../data/videos/{video_id}.mp4\"\n",
" cap = cv2.VideoCapture(str(video_path))\n",
" num_video_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
" fps = cap.get(cv2.CAP_PROP_FPS)\n",
" video_fps[video_id] = fps"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def correct_timestamp(row):\n",
" # pd.Series([1, 2], index=['foo', 'bar'])\n",
" video_id = row[\"video_id\"]\n",
" frame_idx = row[\"frame_idx\"]\n",
" fps = video_fps[video_id]\n",
" timestamp = frame_idx / fps\n",
" return timestamp\n",
"\n",
"\n",
"df[\"timestamp\"] = df.apply(correct_timestamp, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df.to_parquet(df_path, index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "visual-content-search-over-videos",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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