This repo is modified according to the initial python2 version
-
used packages
You can install them by
pip install -r python/requirements.txt
-
DAVIS dataset
You need to download and unzip the DAVIS2017 trainval dataset to
/data
. You can rundata/get_davis.sh
to download it. Only need DAVIS 2017. Evaluation of DAVIS 2016 is also possibleThe data structure should be:
- data/ - DAVIS/ - Annotations - JPEGImages - ImageSets - Scribbles
Before evaluation, you should add PYTHONPATH:
`export PYTHONPATH=$(pwd)/python/lib`
`python tools/eval.py -i path-to-your-results -o results.yaml --year 2017 --phase val`
`python tools/eval.py -i path-to-your-results -o results.yaml --year 2016 --single-object --phase val`
The directory is structured as follows:
-
/cpp
: Implementation and python wrapper of the temporal stability measure. -
/python/tools
: contains scripts for evaluating segmentation.eval.py
: evaluate a technique and store results in HDF5 fileeval_view.py
: read and display evaluation from HDF5.visualize.py
: visualize segmentation results.
-
/python/lib/davis
: library package contains helper functions for parsing and evaluating DAVIS -
/data
:get_davis.sh
: download input images and annotations.
Please cite DAVIS
in your publications if it helps your research:
@inproceedings{Perazzi_CVPR_2016,
author = {Federico Perazzi and
Jordi Pont-Tuset and
Brian McWilliams and
Luc Van Gool and
Markus Gross and
Alexander Sorkine-Hornung},
title = {A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016}
}
@article{Pont-Tuset_arXiv_2017,
author = {Jordi Pont-Tuset and
Federico Perazzi and
Sergi Caelles and
Pablo Arbel\'aez and
Alexander Sorkine-Hornung and
Luc {Van Gool}},
title = {The 2017 DAVIS Challenge on Video Object Segmentation},
journal = {arXiv:1704.00675},
year = {2017}
}
DAVIS is released under the BSD License (see LICENSE for details)