| Name | raillabel JSON |
| Version |
4.0.0
JSON |
| download |
| home_page | None |
| Summary | A devkit for working with recorded and annotated train ride data from Deutsche Bahn. |
| upload_time | 2024-11-18 12:15:00 |
| maintainer | None |
| docs_url | None |
| author | DB InfraGO AG |
| requires_python | <3.14,>=3.8 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!--
~ Copyright DB InfraGO AG and contributors
~ SPDX-License-Identifier: Apache-2.0
-->
# RailLabel
<!-- prettier-ignore -->
A devkit for working with recorded and annotated train ride data from Deutsche Bahn.
# Documentation
<!-- prettier-ignore -->
Read the [full documentation on Github pages](https://dsd-dbs.github.io/raillabel).
# Installation
You can install the latest released version directly from PyPI.
```zsh
pip install raillabel
```
To set up a development environment, clone the project and install it into a
virtual environment.
```zsh
git clone https://github.com/DSD-DBS/raillabel
cd raillabel
python -m venv .venv
source .venv/bin/activate.sh # for Linux / Mac
.venv\Scripts\activate # for Windows
pip install -U pip pre-commit
pip install -e '.[docs,test]'
pre-commit install
```
# Usage
The first step in using `raillabel` is downloading a desired dataset (like [OSDaR23](https://data.fid-move.de/dataset/osdar23)). You can then load any scene by running
```python
import raillabel
scene = raillabel.load("path/to/annotation_file.json")
```
This returns a [`raillabel.Scene`](https://dsd-dbs.github.io/raillabel/code/raillabel.html#raillabel.Scene), which is the root class for the annotations.
If a file is too extensive for your use-case you can filter out certain parts of a scene like this
```python
from raillabel.filter import (
IncludeObjectTypeFilter,
ExcludeAnnotationTypeFilter,
StartTimeFilter, ExcludeFrameIdFilter,
IncludeAttributesFilter
)
scene_with_only_trains = scene.filter([IncludeObjectTypeFilter(["rail_vehicle"])])
scene_without_bboxs = scene.filter([ExcludeAnnotationTypeFilter(["bbox"])])
cut_scene_with_only_red_trains = scene.filter([
StartTimeFilter("1587349200.004200000"),
ExcludeFrameIdFilter([2, 4]),
IncludeObjectTypeFilter(["rail_vehicle"]),
IncludeAttributesFilter({"color": "red"}),
])
```
An overview of all available filters can be found [here](https://dsd-dbs.github.io/raillabel/code/raillabel.filter.html#module-raillabel.filter).
If you then want to save your changes, then use
```python
raillabel.save(cut_scene_with_only_red_trains, "/path/to/target.json")
```
# Contributing
We'd love to see your bug reports and improvement suggestions! Please take a
look at our [guidelines for contributors](CONTRIBUTING.md) for details.
# Licenses
This project is compliant with the
[REUSE Specification Version 3.0](https://git.fsfe.org/reuse/docs/src/commit/d173a27231a36e1a2a3af07421f5e557ae0fec46/spec.md).
Copyright DB InfraGO AG, licensed under Apache 2.0 (see full text in
[LICENSES/Apache-2.0.txt](LICENSES/Apache-2.0.txt))
Dot-files are licensed under CC0-1.0 (see full text in
[LICENSES/CC0-1.0.txt](LICENSES/CC0-1.0.txt))
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"description": "<!--\n ~ Copyright DB InfraGO AG and contributors\n ~ SPDX-License-Identifier: Apache-2.0\n -->\n\n# RailLabel\n\n<!-- prettier-ignore -->\n\n\n\nA devkit for working with recorded and annotated train ride data from Deutsche Bahn.\n\n# Documentation\n\n<!-- prettier-ignore -->\nRead the [full documentation on Github pages](https://dsd-dbs.github.io/raillabel).\n\n# Installation\n\nYou can install the latest released version directly from PyPI.\n\n```zsh\npip install raillabel\n```\n\nTo set up a development environment, clone the project and install it into a\nvirtual environment.\n\n```zsh\ngit clone https://github.com/DSD-DBS/raillabel\ncd raillabel\npython -m venv .venv\n\nsource .venv/bin/activate.sh # for Linux / Mac\n.venv\\Scripts\\activate # for Windows\n\npip install -U pip pre-commit\npip install -e '.[docs,test]'\npre-commit install\n```\n\n# Usage\n\nThe first step in using `raillabel` is downloading a desired dataset (like [OSDaR23](https://data.fid-move.de/dataset/osdar23)). You can then load any scene by running\n```python\nimport raillabel\n\nscene = raillabel.load(\"path/to/annotation_file.json\")\n```\n\nThis returns a [`raillabel.Scene`](https://dsd-dbs.github.io/raillabel/code/raillabel.html#raillabel.Scene), which is the root class for the annotations.\n\nIf a file is too extensive for your use-case you can filter out certain parts of a scene like this\n```python\nfrom raillabel.filter import (\n IncludeObjectTypeFilter,\n ExcludeAnnotationTypeFilter,\n StartTimeFilter, ExcludeFrameIdFilter,\n IncludeAttributesFilter\n)\n\nscene_with_only_trains = scene.filter([IncludeObjectTypeFilter([\"rail_vehicle\"])])\n\nscene_without_bboxs = scene.filter([ExcludeAnnotationTypeFilter([\"bbox\"])])\n\ncut_scene_with_only_red_trains = scene.filter([\n StartTimeFilter(\"1587349200.004200000\"),\n ExcludeFrameIdFilter([2, 4]),\n IncludeObjectTypeFilter([\"rail_vehicle\"]),\n IncludeAttributesFilter({\"color\": \"red\"}),\n])\n```\nAn overview of all available filters can be found [here](https://dsd-dbs.github.io/raillabel/code/raillabel.filter.html#module-raillabel.filter).\n\nIf you then want to save your changes, then use\n```python\nraillabel.save(cut_scene_with_only_red_trains, \"/path/to/target.json\")\n```\n\n# Contributing\n\nWe'd love to see your bug reports and improvement suggestions! Please take a\nlook at our [guidelines for contributors](CONTRIBUTING.md) for details.\n\n# Licenses\n\nThis project is compliant with the\n[REUSE Specification Version 3.0](https://git.fsfe.org/reuse/docs/src/commit/d173a27231a36e1a2a3af07421f5e557ae0fec46/spec.md).\n\nCopyright DB InfraGO AG, licensed under Apache 2.0 (see full text in\n[LICENSES/Apache-2.0.txt](LICENSES/Apache-2.0.txt))\n\nDot-files are licensed under CC0-1.0 (see full text in\n[LICENSES/CC0-1.0.txt](LICENSES/CC0-1.0.txt))\n",
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