This is the official code for our ICCV 2019 paper, "Bayesian Adaptive Superpixel Segmentation" , co-authored by Roy Uziel, Meitar Ronen, and Oren Freifeld.
You can run the code using either GPU or CPU.
Remark (17/4/2020): we are currently working on an even faster GPU implementation.
The code uses Python 3.6 and it was tested on Pytorch 1.3.0
Install pip and virtualenv
sudo apt-get install python-pip python-virtualenv
Clone the git project:
$ git clone https://github.com/BGU-CS-VIL/BASS.git
Set up virtual environment:
$ mkdir <your_home_dir>/.virtualenvs
$ virtualenv -p python3 <your_home_dir>/.virtualenvs/BASS
Activate virtual environment:
$ cd BASS
$ source <your_home_dir>/BASS/bin/activate
The requirements can be installed using:
pip install -r requirements.txt
Saving csv file
python BASS.py --img_folder /path/to/image/folder --csv
Saving mean colors and contours images
python BASS.py --img_folder /path/to/image/folder --vis
Run without gpu
python BASS.py --img_folder /path/to/image/folder --cpu
Run in verbose mode
python BASS.py --img_folder /path/to/image/folder --v
This software is released under the MIT License (included with the software). Note, however, that if you are using this code (and/or the results of running it) to support any form of publication (e.g., a book, a journal paper, a conference paper, a patent application, etc.) then we request you will cite our paper:
@inproceedings{Uziel:ICCV:2019:BASS,
title = {Bayesian Adaptive Superpixel Segmentation},
author = {Roy Uziel and Meitar Ronen and Oren Freifeld},
booktitle = {ICCV},
year={2019}
}