Metadata-Version: 2.1
Name: slamcore-utils
Version: 0.1.4
Summary: SLAMcore SLAM Utilities
Home-page: https://github.com/slamcore/slamcore_utils
License: BSD License
Author: Nikos Koukis
Author-email: nikolaos@slamcore.com
Requires-Python: >=3.6.8,<3.11
Classifier: License :: OSI Approved :: BSD License
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Provides-Extra: tqdm
Requires-Dist: numpy (>=1.19.5,<2.0.0)
Requires-Dist: prompt-toolkit (>3.0,<=3.0.23)
Requires-Dist: questionary (>=1.10.0,<2.0.0)
Requires-Dist: tqdm (>=4.64.0,<5.0.0); extra == "tqdm"
Project-URL: Repository, https://github.com/slamcore/slamcore_utils
Description-Content-Type: text/markdown

# slamcore_utils

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## Description

<!-- Change this when we add more scripts -->

This repo contains the `slamcore-setup-dataset` script. It can be used for
installing a sample dataset for offline testing and evaluation of [SLAMcore][slamcore]'s
Localization and Mapping capabilities.

Currently the following types of datasets are supported:

- [EuRoC MAV Datasets](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets)
- [OpenLORIS-Scene Datasets](https://lifelong-robotic-vision.github.io/dataset/scene)
- [TUM VI Datasets](https://vision.in.tum.de/data/datasets/visual-inertial-dataset)

## Usage

After [installation](#installation) the script should be available in your path.
Executing it will guide you through a list of questions in order to properly
setup a sample SLAM dataset.

Here is a sample execution of the said script to enable processing of the `TUM-VI`
`dataset-room4_1024_16`

![setup-dataset2](https://github.com/slamcore/slamcore_utils/raw/master/share/images/slamcore-setup-dataset2.gif)

Here's the same execution for the `OpenLORIS` `cafe1-1` dataset

![setup-dataset1](https://github.com/slamcore/slamcore_utils/raw/master/share/images/slamcore-setup-dataset1.gif)

And here's the execution guiding the user to the right download page, when
the datasets are not available locally yet.

![setup-dataset3](https://github.com/slamcore/slamcore_utils/raw/master/share/images/slamcore-setup-dataset3.gif)

## Installation

Install it directly from PyPI:

```sh
pip3 install --user --upgrade slamcore_utils[tqdm]

# Or if you don't want tqdm's polished progress bars
pip3 install --user --upgrade slamcore_utils
```

<details>
  <summary>I don't want to have to install it</summary>

Make sure the project dependencies are installed:

`pip3 install -r requirements.txt`

Then adjust your `PYTHONPATH` variable and run accordingly:

```sh
git clone https://github.com/slamcore/slamcore_utils
cd slamcore_utils
export PYTHONPATH=$PYTHONPATH:$PWD
./slamcore_utils/scripts/setup_dataset.py
```

</details>

<details>
  <summary>I don't want to install any of your dependencies in my user's install directory</summary>

Consider using either [pipx](https://github.com/pypa/pipx) or
[poetry](https://github.com/python-poetry/poetry) to install this package and
its dependencies isolated in a virtual environment:

```sh
git clone https://github.com/slamcore/slamcore_utils
poetry install
poetry shell

# the executables should now be available in your $PATH
setup-dataset
```

</details>

## About SLAMcore

[SLAMcore][slamcore] offers commercial-grade visual-inertial
simultaneous localisation and mapping (SLAM) software for real-time autonomous
navigation on robots and drones. [Request
access](https://www.slamcore.com/sdk-access) today to get started.


[slamcore]: https://www.slamcore.com/

