599 lines
662 KiB
Plaintext
599 lines
662 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Copyright (c) Meta Platforms, Inc. and affiliates."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Segment Anything Video (SA-V) Dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook presents an example showing how to load and visualize the SA-V annotations.\n",
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"To run this notebook, you will need to have the following installed:\n",
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"- jupyter\n",
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"- matplotlib\n",
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"- opencv-python\n",
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"- numpy\n",
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"- pycocoevalcap\n",
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"- pandas"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import libaries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"import pandas as pd\n",
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"from utils.sav_utils import SAVDataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example video and annotations from SA-V"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"There are 483 frames decoded from example/sav_000001.mp4 (24fps).\n",
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"Videos are annotated every 4 frames. To align with the annotations, downsample the video to 121 frames.\n"
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]
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}
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],
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"source": [
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"sav_dataset = SAVDataset(sav_dir=\"example/\")\n",
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"frames, manual_annot, auto_annot = sav_dataset.get_frames_and_annotations(\"sav_000001\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Show the SA-V annotations in frame 0 - auto + manual"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"sav_dataset.visualize_annotation(\n",
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" frames, manual_annot, auto_annot, \n",
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" annotated_frame_id=0,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Show the SA-V annotations in another frame - auto + manual"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sav_dataset.visualize_annotation(\n",
|
||
|
|
" frames, manual_annot, auto_annot, \n",
|
||
|
|
" annotated_frame_id=30,\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Show the SA-V annotations in frame 0 - manual only"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sav_dataset.visualize_annotation(\n",
|
||
|
|
" frames, manual_annot, auto_annot,\n",
|
||
|
|
" annotated_frame_id=0,\n",
|
||
|
|
" show_auto=False,\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Show the SA-V annotations in frame 0 - auto only"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
|
||
|
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}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sav_dataset.visualize_annotation(\n",
|
||
|
|
" frames, manual_annot, auto_annot,\n",
|
||
|
|
" annotated_frame_id=0,\n",
|
||
|
|
" show_manual=False,\n",
|
||
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|
")"
|
||
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|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Masklet annotations and Metadata"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### Manual annotations and metadata"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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||
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"\n",
|
||
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
|
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|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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|
" }\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>video_duration</th>\n",
|
||
|
|
" <th>video_frame_count</th>\n",
|
||
|
|
" <th>video_height</th>\n",
|
||
|
|
" <th>video_width</th>\n",
|
||
|
|
" <th>video_resolution</th>\n",
|
||
|
|
" <th>video_environment</th>\n",
|
||
|
|
" <th>video_split</th>\n",
|
||
|
|
" <th>masklet</th>\n",
|
||
|
|
" <th>masklet_id</th>\n",
|
||
|
|
" <th>masklet_size_rel</th>\n",
|
||
|
|
" <th>masklet_size_abs</th>\n",
|
||
|
|
" <th>masklet_size_bucket</th>\n",
|
||
|
|
" <th>masklet_visibility_changes</th>\n",
|
||
|
|
" <th>masklet_first_appeared_frame</th>\n",
|
||
|
|
" <th>masklet_frame_count</th>\n",
|
||
|
|
" <th>masklet_edited_frame_count</th>\n",
|
||
|
|
" <th>masklet_type</th>\n",
|
||
|
|
" <th>masklet_stability_score</th>\n",
|
||
|
|
" <th>masklet_num</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>sav_000001</td>\n",
|
||
|
|
" <td>20.125</td>\n",
|
||
|
|
" <td>483.0</td>\n",
|
||
|
|
" <td>848.0</td>\n",
|
||
|
|
" <td>480.0</td>\n",
|
||
|
|
" <td>407040.0</td>\n",
|
||
|
|
" <td>Indoor</td>\n",
|
||
|
|
" <td>train</td>\n",
|
||
|
|
" <td>[[{'size': [848, 480], 'counts': 'i\\Y4<Qj05K4L...</td>\n",
|
||
|
|
" <td>[0, 1, 2, 3, 4]</td>\n",
|
||
|
|
" <td>[0.0035249812, 0.0946159778, 0.011285757, 0.00...</td>\n",
|
||
|
|
" <td>[1434.8083333333, 38512.4876033058, 4593.75454...</td>\n",
|
||
|
|
" <td>[medium, large, medium, medium, medium]</td>\n",
|
||
|
|
" <td>[2, 0, 10, 0, 0]</td>\n",
|
||
|
|
" <td>[0.0, 0.0, 0.0, 113.0, 0.0]</td>\n",
|
||
|
|
" <td>[121, 121, 121, 121, 121]</td>\n",
|
||
|
|
" <td>[41, 11, 22, 4, 115]</td>\n",
|
||
|
|
" <td>[manual, manual, manual, manual, manual]</td>\n",
|
||
|
|
" <td>[None, None, None, None, None]</td>\n",
|
||
|
|
" <td>5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" video_id video_duration video_frame_count video_height video_width \\\n",
|
||
|
|
"0 sav_000001 20.125 483.0 848.0 480.0 \n",
|
||
|
|
"\n",
|
||
|
|
" video_resolution video_environment video_split \\\n",
|
||
|
|
"0 407040.0 Indoor train \n",
|
||
|
|
"\n",
|
||
|
|
" masklet masklet_id \\\n",
|
||
|
|
"0 [[{'size': [848, 480], 'counts': 'i\\Y4<Qj05K4L... [0, 1, 2, 3, 4] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_size_rel \\\n",
|
||
|
|
"0 [0.0035249812, 0.0946159778, 0.011285757, 0.00... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_size_abs \\\n",
|
||
|
|
"0 [1434.8083333333, 38512.4876033058, 4593.75454... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_size_bucket masklet_visibility_changes \\\n",
|
||
|
|
"0 [medium, large, medium, medium, medium] [2, 0, 10, 0, 0] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_first_appeared_frame masklet_frame_count \\\n",
|
||
|
|
"0 [0.0, 0.0, 0.0, 113.0, 0.0] [121, 121, 121, 121, 121] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_edited_frame_count masklet_type \\\n",
|
||
|
|
"0 [41, 11, 22, 4, 115] [manual, manual, manual, manual, manual] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_stability_score masklet_num \n",
|
||
|
|
"0 [None, None, None, None, None] 5 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pd.DataFrame([manual_annot])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### Auto annotations and metadata"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
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"<style scoped>\n",
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" vertical-align: middle;\n",
|
||
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|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
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|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</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>video_duration</th>\n",
|
||
|
|
" <th>video_frame_count</th>\n",
|
||
|
|
" <th>video_height</th>\n",
|
||
|
|
" <th>video_width</th>\n",
|
||
|
|
" <th>video_resolution</th>\n",
|
||
|
|
" <th>video_environment</th>\n",
|
||
|
|
" <th>video_split</th>\n",
|
||
|
|
" <th>masklet</th>\n",
|
||
|
|
" <th>masklet_id</th>\n",
|
||
|
|
" <th>masklet_size_rel</th>\n",
|
||
|
|
" <th>masklet_size_abs</th>\n",
|
||
|
|
" <th>masklet_size_bucket</th>\n",
|
||
|
|
" <th>masklet_visibility_changes</th>\n",
|
||
|
|
" <th>masklet_first_appeared_frame</th>\n",
|
||
|
|
" <th>masklet_frame_count</th>\n",
|
||
|
|
" <th>masklet_edited_frame_count</th>\n",
|
||
|
|
" <th>masklet_type</th>\n",
|
||
|
|
" <th>masklet_stability_score</th>\n",
|
||
|
|
" <th>masklet_num</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>sav_000001</td>\n",
|
||
|
|
" <td>20.125</td>\n",
|
||
|
|
" <td>483.0</td>\n",
|
||
|
|
" <td>848.0</td>\n",
|
||
|
|
" <td>480.0</td>\n",
|
||
|
|
" <td>407040.0</td>\n",
|
||
|
|
" <td>Indoor</td>\n",
|
||
|
|
" <td>train</td>\n",
|
||
|
|
" <td>[[{'size': [848, 480], 'counts': 'ka0e8ka001O1...</td>\n",
|
||
|
|
" <td>[0, 1, 2, 3, 4, 5, 6, 7, 8]</td>\n",
|
||
|
|
" <td>[0.010841089678796047, 0.038489445267425544, 0...</td>\n",
|
||
|
|
" <td>[4412.757142857143, 15666.743801652892, 7663.1...</td>\n",
|
||
|
|
" <td>[medium, large, medium, large, medium, medium,...</td>\n",
|
||
|
|
" <td>[5, 0, 0, 0, 0, 0, 10, 3, 12]</td>\n",
|
||
|
|
" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
|
||
|
|
" <td>[121, 121, 121, 121, 121, 121, 121, 121, 121]</td>\n",
|
||
|
|
" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
|
||
|
|
" <td>[auto, auto, auto, auto, auto, auto, auto, aut...</td>\n",
|
||
|
|
" <td>[[1.0, 0.999616265296936, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
|
||
|
|
" <td>9</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" video_id video_duration video_frame_count video_height video_width \\\n",
|
||
|
|
"0 sav_000001 20.125 483.0 848.0 480.0 \n",
|
||
|
|
"\n",
|
||
|
|
" video_resolution video_environment video_split \\\n",
|
||
|
|
"0 407040.0 Indoor train \n",
|
||
|
|
"\n",
|
||
|
|
" masklet \\\n",
|
||
|
|
"0 [[{'size': [848, 480], 'counts': 'ka0e8ka001O1... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_id \\\n",
|
||
|
|
"0 [0, 1, 2, 3, 4, 5, 6, 7, 8] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_size_rel \\\n",
|
||
|
|
"0 [0.010841089678796047, 0.038489445267425544, 0... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_size_abs \\\n",
|
||
|
|
"0 [4412.757142857143, 15666.743801652892, 7663.1... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_size_bucket \\\n",
|
||
|
|
"0 [medium, large, medium, large, medium, medium,... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_visibility_changes masklet_first_appeared_frame \\\n",
|
||
|
|
"0 [5, 0, 0, 0, 0, 0, 10, 3, 12] [0, 0, 0, 0, 0, 0, 0, 0, 0] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_frame_count masklet_edited_frame_count \\\n",
|
||
|
|
"0 [121, 121, 121, 121, 121, 121, 121, 121, 121] [0, 0, 0, 0, 0, 0, 0, 0, 0] \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_type \\\n",
|
||
|
|
"0 [auto, auto, auto, auto, auto, auto, auto, aut... \n",
|
||
|
|
"\n",
|
||
|
|
" masklet_stability_score masklet_num \n",
|
||
|
|
"0 [[1.0, 0.999616265296936, 1.0, 1.0, 1.0, 1.0, ... 9 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pd.DataFrame([auto_annot])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### Video info"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"sav_000001 is 20.125 seconds long with 483.0 frames. The video resolution is 848.0 x 480.0.\n",
|
||
|
|
"This video is captured in Indoor environment.\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"video_id = manual_annot[\"video_id\"]\n",
|
||
|
|
"video_duration = manual_annot[\"video_duration\"]\n",
|
||
|
|
"video_frame_count = manual_annot[\"video_frame_count\"]\n",
|
||
|
|
"H = manual_annot[\"video_height\"]\n",
|
||
|
|
"W = manual_annot[\"video_width\"]\n",
|
||
|
|
"environment = manual_annot[\"video_environment\"]\n",
|
||
|
|
"print(\n",
|
||
|
|
" f\"{video_id} is {video_duration} seconds long with {video_frame_count} frames. The video resolution is {H} x {W}.\"\n",
|
||
|
|
")\n",
|
||
|
|
"print(f\"This video is captured in {environment} environment.\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### Masklet info"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"There are 5 manually labeled masklets and 9 automatically generated masklets.\n",
|
||
|
|
"In SA-V, videos are annotated every 4 frames. Therefore, there are 121 frames being annotated.\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(\n",
|
||
|
|
" f\"There are {manual_annot['masklet_num']} manually labeled masklets and {auto_annot['masklet_num']} automatically generated masklets.\"\n",
|
||
|
|
")\n",
|
||
|
|
"print(\n",
|
||
|
|
" f\"In SA-V, videos are annotated every 4 frames. Therefore, there are {manual_annot['masklet_frame_count'][0]} frames being annotated.\"\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"{'size': [848, 480],\n",
|
||
|
|
" 'counts': 'i\\\\Y4<Qj05K4L4M3M2N3O000010O00001O00010O00000O101O0000000000000O010000000O10O10000O01000O100O010O1O1O1N2O1O0O2O1N20O01O1001N101O1N100O100O10O01O2O00000O1O10O1O11N1O1O100O1O2N1O1O3L4HTXk5'}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Get the rle of the manual masklet with masklet_id=0 in frame 0\n",
|
||
|
|
"masklet_id = 0\n",
|
||
|
|
"annotated_frame_id = 0\n",
|
||
|
|
"manual_annot[\"masklet\"][annotated_frame_id][masklet_id]\n",
|
||
|
|
"# decode the rle using `mask_util.decode(rle)>0` to get the binary segmentation mask"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"{'size': [848, 480],\n",
|
||
|
|
" 'counts': 'Q_T6S1Xh0X1eNY1[Od0E;M4N10000O101O00000000000000O0100000000001M2O1O1N3N1M4H8B?@e0POc1jMfZ[5'}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Get the rle of the masklet with masklet_id=5 in frame 100\n",
|
||
|
|
"masklet_id = 5\n",
|
||
|
|
"annotated_frame_id = 100\n",
|
||
|
|
"auto_annot[\"masklet\"][annotated_frame_id][masklet_id]\n",
|
||
|
|
"# decode the rle using `mask_util.decode(rle)>0` to get the binary segmentation mask"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "onevision_ta_2_pseudo_labeling",
|
||
|
|
"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": "3.10.12"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|