https://github.com/ltriess/semantic_kitti_stats
:chart_with_downwards_trend: Get some nice plots with statistics about the Semantic KITTI dataset
https://github.com/ltriess/semantic_kitti_stats
analysis kitti-dataset lidar plots semantic-segmentation statistics
Last synced: 8 months ago
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:chart_with_downwards_trend: Get some nice plots with statistics about the Semantic KITTI dataset
- Host: GitHub
- URL: https://github.com/ltriess/semantic_kitti_stats
- Owner: ltriess
- License: mit
- Created: 2019-08-23T15:59:27.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T22:36:28.000Z (about 4 years ago)
- Last Synced: 2025-09-04T22:41:38.372Z (10 months ago)
- Topics: analysis, kitti-dataset, lidar, plots, semantic-segmentation, statistics
- Language: Python
- Homepage:
- Size: 89.8 KB
- Stars: 26
- Watchers: 2
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Semantic KITTI Dataset Statistics
[](LICENSE)
This repository holds a script that allows an analysis of the [Semantic KITTI Dataset](http://semantic-kitti.org/) [1,2].
The main focus is on distance and label analysis.
For all statistics a csv file and a plot are generated.
Some Examples:
* see which label has how many points over the distance
* see how many points belong to a specific label in each sequence
* ... and many more, such as the analysis per sequence or labels over azimuth and elevation angle
## Contents
* [Getting Started](#getting-started)
* [Running the code](#running-the-code)
* [License](#license)
* [References](#references)
## Getting Started
These instructions will get you a copy of the project up and running on your local machine.
### Installing
```
$ git clone https://github.com/ltriess/semantic_kitti_stats.git
$ cd semantic_kitti_stats
$ pip install requirements.txt
```
### Get the Data
Download the data and unzip it in the same folder.
* for the labels: [Semantic KITTI](http://semantic-kitti.org/dataset.html#download)
* for the point clouds: [KITTI Odometry](http://www.cvlibs.net/datasets/kitti/eval_odometry.php)
## Running the code
The main script is `analyse.py` which can be called according to
```
Usage: analyse_sequence.py [OPTIONS] PATH
Options:
--mode [compute|from_data] If compute is selected, PATH must be the path to the dataset.
All statistics will be calculated from the data. If from_data
is selected, PATH must be a a folder in which csv files with
the computed statistics are located.
--save_dir PATH Path where to save the generated graphs. If not provided, show on display.
--help Show this message and exit.
```
The script first iterates over all trainval sequences and generates separate statistics for each sequence.
Finally, all the sequence statistics are combined and a total analysis as well as a sequence overview is generated.
There are two modes in which the script dan be called:
* _compute_: PATH must point to the root directory of the dataset which contains the folders
dataset/sequences/{00..10}/{velodyne/labels} according to how the dataset is extracted after the download.
All statistics will be computed from the dataset and then plots will be generated.
If _save_dir_ is set to a valid path, all the statistics will be saved to csv files for later usage.
* _from_data_: PATH must point to the folder in which all the generated csv files are located.
This is useful when the statistics are available, but a redo of the plots is needed.
In both modes, if _save_dir_ is set, the plots are saved as png files to the specified location.
If it is not set, the plots will be displayed on the screen.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## References
[1] J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall,
"SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences", ICCV 2019
[2] A. Geiger and P. Lenz and C. Stiller and R. Urtasun, Vision meets Robotics: The KITTI Dataset, IJRR 2013