Package containing helper functions for loading and evaluating DAVIS.

A Matlab version of the same package is also available.

DAVIS is released under the BSD License [see LICENSE for details]

DAVIS (Densely Annotated VIdeo Segmentation), consists of high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes. Each video is accompanied by densely annotated, pixel-accurate and per-frame ground truth segmentation.

Execute the script ROOT/python/tools/eval.py providing the resulting segmentation and setting the correct phase (train,val etc...) and year (2016,2017). See script documentation for mode details.

Example: python tools/eval.py -i path-to-my-technique -o results.yaml --year 2017 --phase val

Execute the script ROOT/python/tools/visualize.py. The command-line arguments are similar to the evaluation script. Use --single-object to visualize the original DAVIS 2016.

Example: python tools/visualize.py -i path-to-my-technique --year 2017 --phase val

C++

• Boost.Python

Python

• See ROOT/python/requirements.txt (Optionally to visualize results install cv2)

C++

1. ./configure.sh && make -C build/release

Python:

1. pip install virtualenv virtualenvwrapper
2. source /usr/local/bin/virtualenvwrapper.sh
3. mkvirtualenv davis
4. pip install -r python/requirements.txt
5. export PYTHONPATH=$(pwd)/python/lib
6. See ROOT/python/lib/davis/config.py for a list of available options

See source code for documentation.

The directory is structured as follows:

• ROOT/cpp: Implementation and python wrapper of the temporal stability measure.

• ROOT/python/tools: contains scripts for evaluating segmentation.

  • eval.py : evaluate a technique and store results in HDF5 file
  • eval_view.py: read and display evaluation from HDF5.
  • visulize.py: visualize segmentation results.

• ROOT/python/lib/davis : library package contains helper functions for parsing and evaluating DAVIS

• ROOT/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}
}

• Federico Perazzi
• Jordi Pont-Tuset

• Temporal stability measure (T)
• Per-attribute evaluation script
• Add usage examples