| Name | pandora JSON |
| Version |
1.7.1
JSON |
| download |
| home_page | None |
| Summary | Pandora is a stereo matching framework that helps emulate state of the art algorithms |
| upload_time | 2025-09-04 10:10:57 |
| maintainer | None |
| docs_url | None |
| author | CNES |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
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No coveralls.
|
<div align="center">
<a target="_blank" href="https://github.com/CNES/pandora">
<picture>
<img
src="https://raw.githubusercontent.com/CNES/Pandora/master/docs/source/Images/logo/logo_typo_large.png?inline=false""
alt="Pandora"
width="40%"
/>
</picture>
</a>
<h4> Pandora, a stereo matching framework</h4>
[](https://www.python.org/downloads/release/python-390/)
[](CONTRIBUTING.md)
[](https://opensource.org/licenses/Apache-2.0/)
[](https://pandora.readthedocs.io/)
[](https://github.com/CNES/Pandora/actions)
[](https://codecov.io/gh/CNES/Pandora)
[](https://mybinder.org/v2/gh/CNES/Pandora/master)
<p>
<a href="#overview">Overview</a> •
<a href="#install">Install</a> •
<a href="#quick-start">Quick Start</a> •
<a href="#Documentation">Documentation</a> •
<a href="#credits">Credits</a> •
<a href="#related">Related</a> •
<a href="#references">References</a>
</p>
</div>
## Overview
From stereo rectified images to disparity map | Pandora is working with cost volumes
:-------------------------:|:-------------------------:
 | 
Pandora is a stereo matching flexible framework made for research and production with state of the art performances:
- Inspired from the (Scharstein et al., 2002) modular taxonomy, it allows one to emulate, analyse and hopefully improve state of the art stereo algorithms with a few lines of code.
- For production purpose, Pandora have been created for the CNES & Airbus <a href="https://co3d.cnes.fr/en/co3d-0">CO3D project</a> processing chain, as [CARS](https://github.com/CNES/CARS) core stereo matching tool.
The tool is open for contributions, contact us to pandora AT cnes.fr !
## Install
Pandora is available on Pypi and can be installed by:
```bash
pip install pandora
```
For stereo reconstruction, install pandora **with** following plugins:
```bash
# SGM regularization
pip install pandora[sgm]
# MCCNN AI matching cost capability (heavy!)
pip install pandora[mccnn]
```
## Quick Start
```bash
# Download configuration file
wget https://raw.githubusercontent.com/CNES/Pandora/master/data_samples/json_conf_files/a_local_block_matching.json
# Download data samples
wget https://raw.githubusercontent.com/CNES/Pandora/master/data_samples/images/cones.zip
# Uncompress data
unzip cones.zip
# Run pandora
pandora a_local_block_matching.json output_dir
# Left and right disparity maps are saved in output_dir: left_disparity.tif and right_disparity.tif
```
## Documentation
To go further, please consult [our online documentation](https://pandora.readthedocs.io/).
## Credits
- *Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision, 47(1-3), 7-42.*
- *Scharstein, D., & Szeliski, R. (2003, June). High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. (Vol. 1, pp. I-I).*
- *2003 Middleburry dataset (D. Scharstein & R. Szeliski, 2003).*
## Related
[Plugin_LibSGM](https://github.com/CNES/pandora_plugin_libsgm) - Stereo Matching Algorithm plugin for Pandora
[Plugin_MC-CNN](https://github.com/CNES/pandora_plugin_mccnn) - MC-CNN Neural Network plugin for Pandora
[Pandora2D](https://github.com/CNES/Pandora2D) - CNES Image Registration framework based on Pandora, with 2D disparity maps.
[CARS](https://github.com/CNES/CARS) - CNES 3D reconstruction software
## References
Please cite the following papers when using Pandora:
- *Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.*
- *Youssefi D., Michel, J., Sarrazin, E., Buffe, F., Cournet, M., Delvit, J., L’Helguen, C., Melet, O., Emilien, A., Bosman, J., 2020. Cars: A photogrammetry pipeline using dask graphs to construct a global 3d model. IGARSS - IEEE International Geoscience and Remote Sensing Symposium.*
Raw data
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