This is a BIIGLE module that offers the Magic SAM image annotation instrument.
composer require biigle/magic-sam
.php artisan vendor:publish --tag=public
to refresh the public assets of the modules. Do this for every update of this module.MAGIC_SAM_EMBEDDING_STORAGE_DISK
variable in the .env
file to the name of the respective storage disk. The content of the storage disk should be publicly accessible. Example for local disks:
'magic-sam' => [
'driver' => 'local',
'root' => storage_path('app/public/magic-sam'),
'url' => env('APP_URL').'/storage/magic-sam',
'visibility' => 'public',
],
This requires the link storage -> ../storage/app/public
in the public
directory.
Image embeddings are computed in jobs submitted to the default
on the CPU. They require a queue worker Docker container that satisfies the Python requirements of this repository. Embeddings can be computed much faster on a GPU. You can cnfigure the queue name with the MAGIC_SAM_REQUEST_QUEUE
and the device (cpu
or cuda
) with the MAGIC_SAM_DEVICE
environment variables.
Image embedding files are automatically deleted after 30 days. You can configure this with the MAGIC_SAM_PRUNE_AGE_DAYS
environment variable.
Reference publications that you should cite if you use Magic SAM for one of your studies.
BIIGLE 2.0
Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). Biigle 2.0-browsing and annotating large marine image collections.
Frontiers in Marine Science, 4, 83. doi: 10.3389/fmars.2017.00083
Segment Anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y. and Dollár, P., (2023). Segment anything.
arXiv preprint arXiv:2304.02643. doi: 10.48550/arXiv.2304.02643
Take a look at the development guide of the core repository to get started with the development setup.
Want to develop a new module? Head over to the biigle/module template repository.
Contributions to BIIGLE are always welcome. Check out the contribution guide to get started.