| Name | vbr-devkit JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Development kit for VBR SLAM dataset |
| upload_time | 2024-06-18 14:23:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<h1>VBR Development Kit</h1>
<a href=""><img src=https://github.com/rvp-group/vbr-devkit/actions/workflows/python.yml/badge.svg /></a>
<a href=""><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/vbr-devkit" /></a>
<a href=""><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/vbr-devkit" /></a>
<br />
<br />
<a href="https://github.com/rvp-group/vbr-devkit"><img src="https://github.com/rvp-group/vbr-devkit/assets/5305530/f1a8d22a-af1e-42d4-b296-d94021a980cf"/></a>
</div>
This kit contains utilities to work on the VBR SLAM dataset
# Install
```shell
pip install vbr-devkit
```
You can install autocompletion for our package by typing:
```shell
vbr --install-completion
```
you might need to restart the shell for the autocompletion to take effect.
# Usage
## Download sequences
You can list the available sequences you can download by typing:
```shell
vbr list
```
You should see something similar to this
After choosing your sequence, you can type
```shell
vbr download <sequence_name> <save_directory>
```
For instance, we could save `campus_train0` as follows:
```shell
vbr download campus_train0 ~/data/
```
**N.B.** The script will actually save the sequence at `<save_directory>/vbr_slam/<sequence_prefix>/<sequence_name>`. Moreover, by calling the previous command, we expect the following directory:
```
data
- vbr_slam
- campus
- campus_train0
- vbr_calib.yaml
- campus_train0_gt.txt
- campus_train0_00.bag
- campus_train0_01.bag
- campus_train0_02.bag
- campus_train0_03.bag
- campus_train0_04.bag
```
## Convert format
The sequences are provided in ROS1 format. We offer a convenient tool to change representation if you prefer working on a different format.
You can see the supported formats by typing:
```shell
vbr convert --help
```
To convert a bag or a sequence of bags, type:
```shell
vbr convert <desired_format> <input_directory/input_bag> <output_directory>
```
for instance, we could convert the `campus_train0` sequence to `kitti` format as follows:
```shell
vbr convert kitti ~/data/vbr_slam/campus/campus_train0/campus_train0_00.bag ~/data/campus_train0_00_kitti/
```
We can expect the following result:
```
data
- campus_train0_00_kitti
- camera_left
- timestamps.txt
- data
- 0000000000.png
- 0000000001.png
- ...
- camera_right
- timestamps.txt
- data
- 0000000000.png
- 0000000001.png
- ...
- ouster_points
- timestamps.txt
- data
- .dtype.pkl
- 0000000000.bin
- 0000000001.bin
- ...
- ...
```
**N.B.** In KITTI format, point clouds are embedded in binary files that can be opened using `Numpy` and `pickle` as follows:
```python
import numpy as np
import pickle
with open("campus_train0_00_kitti/ouster_points/data/.dtype.pkl", "rb") as f:
cdtype = pickle.load(f)
cloud_numpy = np.fromfile("/campus_train0_00_kitti/ouster_points/data/0000000000.bin", dtype=cdtype)
```
Raw data
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"description": "<div align=\"center\">\n <h1>VBR Development Kit</h1>\n <a href=\"\"><img src=https://github.com/rvp-group/vbr-devkit/actions/workflows/python.yml/badge.svg /></a>\n <a href=\"\"><img alt=\"PyPI - Version\" src=\"https://img.shields.io/pypi/v/vbr-devkit\" /></a>\n <a href=\"\"><img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/vbr-devkit\" /></a>\n <br />\n <br />\n <a href=\"https://github.com/rvp-group/vbr-devkit\"><img src=\"https://github.com/rvp-group/vbr-devkit/assets/5305530/f1a8d22a-af1e-42d4-b296-d94021a980cf\"/></a> \n</div>\nThis kit contains utilities to work on the VBR SLAM dataset\n\n# Install\n\n```shell\npip install vbr-devkit\n```\n\nYou can install autocompletion for our package by typing:\n\n```shell\nvbr --install-completion\n```\n\nyou might need to restart the shell for the autocompletion to take effect.\n\n# Usage\n## Download sequences\n\nYou can list the available sequences you can download by typing:\n\n```shell\nvbr list\n```\nYou should see something similar to this\n\n\nAfter choosing your sequence, you can type\n\n```shell\nvbr download <sequence_name> <save_directory>\n```\n\nFor instance, we could save `campus_train0` as follows:\n\n```shell\nvbr download campus_train0 ~/data/\n```\n**N.B.** The script will actually save the sequence at `<save_directory>/vbr_slam/<sequence_prefix>/<sequence_name>`. Moreover, by calling the previous command, we expect the following directory:\n```\ndata\n - vbr_slam\n - campus\n - campus_train0\n - vbr_calib.yaml\n - campus_train0_gt.txt\n - campus_train0_00.bag\n - campus_train0_01.bag\n - campus_train0_02.bag\n - campus_train0_03.bag\n - campus_train0_04.bag \n```\n\n## Convert format\n\nThe sequences are provided in ROS1 format. We offer a convenient tool to change representation if you prefer working on a different format.\nYou can see the supported formats by typing:\n\n```shell\nvbr convert --help\n```\n\nTo convert a bag or a sequence of bags, type:\n```shell\nvbr convert <desired_format> <input_directory/input_bag> <output_directory>\n```\n\nfor instance, we could convert the `campus_train0` sequence to `kitti` format as follows:\n\n```shell\nvbr convert kitti ~/data/vbr_slam/campus/campus_train0/campus_train0_00.bag ~/data/campus_train0_00_kitti/\n```\n\nWe can expect the following result:\n\n```\ndata\n - campus_train0_00_kitti\n - camera_left\n - timestamps.txt\n - data\n - 0000000000.png\n - 0000000001.png\n - ...\n - camera_right\n - timestamps.txt\n - data\n - 0000000000.png\n - 0000000001.png\n - ...\n - ouster_points\n - timestamps.txt\n - data\n - .dtype.pkl\n - 0000000000.bin\n - 0000000001.bin\n - ...\n - ... \n```\n\n**N.B.** In KITTI format, point clouds are embedded in binary files that can be opened using `Numpy` and `pickle` as follows:\n\n```python\nimport numpy as np\nimport pickle\n\nwith open(\"campus_train0_00_kitti/ouster_points/data/.dtype.pkl\", \"rb\") as f:\n cdtype = pickle.load(f)\n\ncloud_numpy = np.fromfile(\"/campus_train0_00_kitti/ouster_points/data/0000000000.bin\", dtype=cdtype)\n```\n\n",
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