pandaset2bag

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Command-line utility (and Python library) for converting the PandaSet Dataset to ROS2 bag files

pandaset2bag 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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<h3 align="center">pandaset2bag</h3>

Command-line utility (and Python library) for converting the <a href="https://pandaset.org/">PandaSet</a> Dataset to ROS2 bag files
 
 <a href="#quickstart">Quickstart</a>
 ·
 <a href="https://github.com/bplus-group/pandaset2bag/issues">Report Bug</a>
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<details>
 <summary>Table of Contents</summary>
 <ol>
 <li>
 <a href="#quickstart">Quickstart</a>
 <ul>
 <li><a href="#installation">Installation</a></li>
 <li><a href="#using-the-command-line-interface">Using the command-line interface</a></li>
 <li><a href="#using-pandaset2bag-in-a-python-script">Using pandaset2bag in a Python script</a></li>
 </ul>
 </li>
 <li><a href="#using-pandaset2bag-with-foxglove">Using pandaset2bag with Foxglove</a></li>
 <li><a href="#coordinate-system">Coordinate system</a></li>
 <li><a href="#annotations">Annotations</a></li>
 <li><a href="#contributing">Contributing</a></li>
 <li><a href="#license">License</a></li>
 </ol>
</details>

---

# pandaset2bag

> [!TIP]
> The [PandaSet](https://pandaset.org/) dataset is no longer available for download from the official website (see issues [#104](https://github.com/scaleapi/pandaset-devkit/issues/104) and [#151](https://github.com/scaleapi/pandaset-devkit/issues/151) for details). However, you can still access the dataset through alternative sources, such as [Kaggle](https://www.kaggle.com/datasets/usharengaraju/pandaset-dataset/data).
> 
> **Note**: The dataset structure on Kaggle is different from the original one.
> Use
> ```bash
> export FROM_KAGGLE=true
> ```
> to ensure that the converter uses the correct file structure.

## Quickstart

### Installation

With the use of the Python library [Rosbags](https://gitlab.com/ternaris/rosbags) which does not have any dependencies on the ROS2 software stacks, *pandaset2bag* can be used without the need to install ROS2. *pandaset2bag* requires an installation of Python 3.10+, as well as pip. Other dependencies can be found in the [requirements.txt](https://github.com/bplus-group/pandaset2bag/blob/master/requirements.txt).

To install from source with pip:

```bash
$ python3 -m pip install git+https://github.com/bplus-group/pandaset2bag
```
<a href="#top">Back to top</a>

### Configuration

Per default, *pandaset2bag* uses the `Marker.msg` definition before the [marker textures](https://github.com/ros2/common_interfaces/commit/4b1ba4621b07cf7130997185b25eabcf0e256b59) were added on Aug 2021 to support the visualization of the generated bag files via [Foxglove](https://foxglove.dev/). To use the updated `Marker.msg` definition, you can set the `UPDATED_VISUALIZATION_MSG_MARKER` environment variable to `true`:

```bash
export UPDATED_VISUALIZATION_MSG_MARKER=true
```

When calling the converter, the used `Marker.msg` definition will be displayed at the beginning:

```plaintext
█████ Using DEFAULT/UPDATED visualization_msgs
```

See <a href="#using-pandaset2bag-with-foxglove">below</a> for details on using pandaset2bag with Foxglove.

### Using the command-line interface

**basic example**

```bash
$ pandaset2bag --dataset-dir /data/pandaset --sequence-id 2 --output pandasetbag_002
```

```plaintext
█████ Using UPDATED visualization_msgs
Converting cameras... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:06
Converting GPS... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Converting pandar64 lidar... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:01
Converting pandarGT lidar... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:01
Converting cuboids... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:05
Generating /tf for ego_vehicle... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for left_camera... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for front_right_camera... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for front_camera... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for right_camera... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for back_camera... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for front_left_camera... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for pandar64... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Generating /tf for pandarGT... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
```
**advanced example**
  - resizing images to 1280x720
  - use JPEG compression for the corresponding topics

```plaintext
$ pandaset2bag --dataset-dir /data/pandaset \
               --sequence-id 2 \
               --max-image-size 1280 \
               --type compressed \
               --output pandasetbag_002
```

#### Options

For more advanced options while converting:

```plaintext
 -h, --help show this help message and exit
 -d DATASET_DIR, --dataset-dir DATASET_DIR
 the directory path of the dataset (default: None)
 -i SEQUENCE_ID, --sequence-id SEQUENCE_ID
 the ID of the sequence to be converted (default: None)
 -o OUTPUT, --output OUTPUT
 the save path of the rosbag file; if '', i.e the rosbag file will be saved
 in the current working directory with the name 'pandasetbag_{sequence_id}'
 (default: )
 -s MAX_IMAGE_SIZE [MAX_IMAGE_SIZE ...], --max-image-size MAX_IMAGE_SIZE [MAX_IMAGE_SIZE ...]
 maximum image size to convert to (default: [])
 -t {raw,compressed,raw_compressed}, --type {raw,compressed,raw_compressed}
 type to be used to convert an image (default: raw)
 -f {jpeg,png}, --format {jpeg,png}
 image format used for compression (default: jpeg)
 -q JPEG_QUALITY, --jpeg-quality JPEG_QUALITY
 image compression quality for JPEG format; the image quality, on a scale
 from 0 (worst) to 95 (best), or the string keep. Values above 95 should be
 avoided; 100 disables portions of the JPEG compression algorithm, and
 results in large files with hardly any gain in image quality; the value keep
 is only valid for JPEG files and will retain the original image quality
 level, subsampling, and qtables (default: 75)
 -l {0,1,2,3,4,5,6,7,8,9}, --png-compress-level {0,1,2,3,4,5,6,7,8,9}
 image compression level for PNG format; ZLIB compression level, a number
 between 0 and 9: 1 gives best speed, 9 gives best compression, 0 gives no
 compression at all. When optimize option is True compress_level has no
 effect (it is set to 9 regardless of a value passed); the value is only
 valid for PNG files and will be otherwise ignored (default: 6)
 -O, --png-optimize image optimization status for PNG format; if True, instructs the PNG writer
 to make the output file as small as possible; this includes extra processing
 in order to find optimal encoder settings; the value is only valid for PNG
 files and will be otherwise ignored (default: False)
 -m {none,file,message}, --mode {none,file,message}
 compression mode for rosbag file. (default: none)
 -c, --cuboids save cuboids `DataFrame` status; if True, save the converted cuboids
 `DataFrame`'s as `.pkl.gz` files, representing the properties of the cuboids
 in normalized ego coordinates (default: False)
 -v, --version show program's version number and exit
```
<a href="#top">Back to top</a>

### Using pandaset2bag in a Python script

**basic example**

```python
from pandaset2bag.pandaset2bag_converter import PandaSet2BagConverter

converter = PandaSet2BagConverter('./data/pandaset')

sequence_id = '002'
converter.convert(sequence_id)
```

**advanced example**
- resizing images to 1280x720
- convert the image both as `Image.msg` and as `CompressedImage.msg`
- use JPEG compression for the corresponding topics
- enable compression (equivalent to `ros2 bag ... --compression-mode file`)

```python
from pandaset2bag.pandaset2bag_converter import PandaSet2BagConverter
from pandaset2bag.enums import CompressedImageFormat, ImageConvertType
from rosbags.rosbag2 import Writer

converter = PandaSet2BagConverter('./data/pandaset')

converter.image_convert_type = ImageConvertType.RAW_COMPRESSED
converter.image_format = CompressedImageFormat.JPEG
converter.max_image_size = (1280, 720)
converter.compression_mode = Writer.CompressionMode.FILE

sequence_id = '002'
converter.convert(sequence_id)
```
<a href="#top">Back to top</a>

## Using *pandaset2bag* with Foxglove

As mentioned <a href="#configuration">before</a>, to visualize the generated bag files (`.db3`; sqlite3 storage plugin) with Foxglove, use the `Marker.msg` definition set as default.

> **Warning**
> Foxglove does not support the playback of compressed (.db3) bag files.

For increased performance during playback or to support the compression of bag files, the use of [MCAP](https://mcap.dev/) is recommended. For conversion from `.db3` to `.mcap`, the corresponding [MCAP CLI](https://github.com/foxglove/mcap/tree/main/go/cli/mcap) provided by Foxglove can be used.

> **Warning**
> MCAP CLI uses the updated `Marker.msg` definition so you have to set the <a href="#configuration">`UPDATED_VISUALIZATION_MSG_MARKER`</a> environment variable to `true` while converting a sequence from the PandaSet using *pandaset2bag*.

## Coordinate system

It is important to note that in the PandaSet dataset, all point cloud data is referenced to a global coordinate system and not an ego coordinate system. See [arXiv:2112.12610](https://arxiv.org/abs/2112.12610) for more details.

Using *pandaset2bag* all data will refer to an ego coordinate system where the coordinates are transformed to a unified normative coordinate system, such that the x-axis corresponds positive to the front direction, the y-axis corresponds positive to the left direction, and the z-axis corresponds positive to the top direction.

## Annotations

### Cuboids

The LiDAR cuboid annotations can be accessed as JSON string through the `text` property of a `Marker.msg` from the `/panda/markers` topic of each scene/sequence. Using [`pandas.DataFrame.to_json`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) with `orient='table'`, the string also includes the JSON schema.

Additionally there is the option to export the LiDAR cuboid annotations as [`pandas.DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) (see <a href="#options">options</a> using the *pandaset2bag* CLI as an example). Using [`pandas.DataFrame.to_pickle`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_pickle.html) with `compression='gzip'` the exported cuboid annotations are fully compatible with the [pandaset-devkit](https://github.com/scaleapi/pandaset-devkit).

### Semantic segmentation

Similary the semantic segmentation annotations can be accessed through the binary data blob of a [`PointCloud2.msg`](http://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/PointCloud2.html) from the corresponding LiDAR topic (e.g. `/panda/ego_vehicle/pandar64`) of each scene. Each `PointCloud2.msg` has an additional channel ([`PointField.msg`](http://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/PointField.html)) for the class ID of the semantic segmentation result. The channels of a `PointCloud2.msg` are `x`, `y`, `z`, `intensity` and `class_id` each of datatype FLOAT32.

> **Note**
> Not all scenes of the PandaSet have semantic segmentation annotations. In that case, the class ID is set to -1.

Using Foxglove you can color the point cloud by the `class_id` using the *'Color by'* option under the correspondig LiDAR topic.

## Contributing

If you have a suggestion that would improve this, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/NewFeature`)
3. Commit your Changes (`git commit -m 'Add some NewFeature'`)
4. Push to the Branch (`git push origin feature/NewFeature`)
5. Open a Pull Request

## License

All code, unless otherwise noted, is licensed under the MIT License. See [`LICENSE`](https://github.com/bplus-group/pandaset2bag/blob/master/LICENSE) for more information.

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