Presentation
This repository contains the code of DISIR: Deep Image Segmentation with Interactive Refinement. In a nutshell, it consists in neural networks trained to perform semantic segmentation with human guidance. You may refer to our paper for detailed explanations.
This repository is divided into two parts:
train
which contains the training code of the networks (README)qgs_plugin
which contains the code of the QGIS plugin used to perform the interactive segmentation (README)
Install Python dependencies
conda create -n disir python=3.7 rtree gdal=2.4 opencv scipy shapely -c 'conda-forge'
conda activate disir
pip install -r requirements.txt
To use
Please note that this repository has been tested on Ubuntu 18.4, QGIS 3.8 and python 3.7 only.
- Download a segmentation dataset such as ISPRS Potsdam or INRIA dataset.
- Prepare this dataset according to
Dataset preprocessing
intrain/README.md
. - Train a model and convert it to a torch script still following
train/README.md
. - Install the QGIS plugin following
qgs_plugin/README.md
. - Follow
How to start
inqgs_plugin/README.md
and start segmenting your data !
References
If you use this work for your projects, please take the time to cite our ISPRS Congress conference paper:
@Article{isprs-annals-V-2-2020-877-2020,
AUTHOR = {Lenczner, G. and Le Saux, B. and Luminari, N. and Chan-Hon-Tong, A. and Le Besnerais, G.},
TITLE = {DISIR: DEEP IMAGE SEGMENTATION WITH INTERACTIVE REFINEMENT},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-2-2020},
YEAR = {2020},
PAGES = {877--884},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/877/2020/},
DOI = {10.5194/isprs-annals-V-2-2020-877-2020}
}
Licence
Code is released under the MIT license for non-commercial and research purposes only. For commercial purposes, please contact the authors.
See LICENSE for more details.
Authors
See AUTHORS.md
Acknowledgements
This work has been jointly conducted at Delair and ONERA-DTIS.