{"name":"napari-rembg","display_name":"Napari Select Foreground","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-rembg.local","title":"Select foreground","python_name":"napari_rembg:LocalPredictorWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-rembg.remote","title":"Select foreground (Web API)","python_name":"napari_rembg:RemotePredictorWidget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari-rembg.local","display_name":"Select foreground","autogenerate":false},{"command":"napari-rembg.remote","display_name":"Select foreground (Web API)","autogenerate":false}],"sample_data":[{"key":"napari","display_name":"Tabueran Kiribati","uri":"https://upload.wikimedia.org/wikipedia/commons/d/dc/Tabuaeran_Kiribati.jpg","reader_plugin":null}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari-rembg","version":"0.0.7","dynamic":null,"platform":null,"supported_platform":null,"summary":"AI-based foreground extraction in scientific and natural images.","description":"![EPFL Center for Imaging logo](https://imaging.epfl.ch/resources/logo-for-gitlab.svg)\n# napari-rembg\n\nSegment images using a collection of fast and lightweight generalist segmentation models in Napari. This plugin is based on the [rembg](https://github.com/danielgatis/rembg) project.\n\n![demo](./assets/demo.gif)\n\n**Key features**\n\n- Choose among **five generalist segmentation models**, including SAM (Segment Anything Model).\n- Quickly annotate individual objects by drawing **bounding boxes** around them.\n- Possibility to generate predictions via a remote **web API** and keep the installation lightweight on client machines.\n- Compatible with 2D, RGB, 2D+t, and 3D images (slice by slice).\n\n## Installation\n\nYou can install `napari-rembg` via [pip]. If you wish to use your local machine for the predictions (most users):\n\n    pip install \"napari-rembg[local]\"\n\nIf you wish to generate predictions from a [web api](#running-the-segmentation-via-a-web-api), go for a minimal install:\n\n    pip install napari-rembg\n\n## Models\n\n- [u2net](https://github.com/xuebinqin/U-2-Net): A pre-trained model for general use cases.\n- [u2netp](https://github.com/xuebinqin/U-2-Net): A lightweight version of u2net.\n- [silueta](https://github.com/xuebinqin/U-2-Net/issues/295): Same as u2net with a size reduced to 43 Mb.\n- [isnet](https://github.com/xuebinqin/DIS): A pre-trained model for general use cases.\n- [sam](https://github.com/facebookresearch/segment-anything): Segment Anything Model pre-trained for any use cases (`vit_b`)\n\n![models](./assets/comparison.png)\n\nThe models automatically get downloaded in the user's home folder in the `.u2net` directory the first time inference is run.\n\n## Usage\n\nStart `napari-rembg` from the `Plugins` menu of Napari:\n\n```\nPlugins > Napari Select Foreground > Select foreground\n```\n\n### Segment an image loaded into Napari\n\nSelect your image in the `Image` dropdown and press `Run`. The output segmentation appears in the `Labels` layer selected in the `Mask` field (if no layer is selected, a new one is created).\n\n### Segment individual objects using bounding boxes\n\n- Click on the `Add` button next to the `ROI` field. This adds a `Shapes layer` to the viewer.\n- Click and drag bounding boxes around objects in the image. Each time you draw a bounding box a segmentation is generated in the region selected.\n\n![screenshot](./assets/screenshot.gif)\n\nYou can choose to auto-increment the label index to distinguish individual objects. Deselect that option to annotate a single foreground class.\n\n## Running the segmentation via a web API\n\nYou can run the `rembg` segmentation via a web API running in a `docker` container.\n\n**Advantages**\n- The segmentation can be run on a remote machine with optimization (e.g. GPU).\n- The segmentation models will be downloaded inside the docker container instead of the user's file system.\n- You can minimally install the package with `pip install napari-rembg` on the client's machine. This will *not* install the `rembg` library, which can solve potential dependency conflicts or bugs.\n\n**Setup**\n\nSee [these instructions](./src/rembg-api/README.md) on how to set up the docker container and web API.\n\n**Usage**\n\nStart `napari-rembg` from the `Plugins` menu of Napari:\n\n```\nPlugins > Napari Select Foreground > Select foreground (Web API)\n```\n\n## Related projects\n\nIf you are looking for similar generalist segmentation plugins, check out these related projects:\n\n- [napari-sam](https://github.com/MIC-DKFZ/napari-sam)\n- [napari-segment-anything](https://github.com/royerlab/napari-segment-anything)\n\n## Contributing\n\nContributions are very welcome. \n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"napari-rembg\" is free and open source software.\n\n## Issues\n\nIf you encounter any problems, please file an issue along with a detailed description.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n\n----------------------------------\n\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/EPFL-Center-for-Imaging/napari-rembg.git","download_url":null,"author":"Mallory Wittwer","author_email":"mallory.wittwer@epfl.ch","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","classifier":["Development Status :: 2 - Pre-Alpha","Framework :: napari","Intended Audience :: Developers","License :: OSI Approved :: BSD License","Operating System :: OS Independent","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3 :: Only","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["magicgui","qtpy","napari[all] >=0.4.16","rembg ; extra == 'local'","pytest ; extra == 'testing'","pytest-cov ; extra == 'testing'","pytest-qt ; extra == 'testing'","napari ; extra == 'testing'","pyqt5 ; extra == 'testing'"],"requires_python":">=3.8","requires_external":null,"project_url":["Source Code, https://github.com/EPFL-Center-for-Imaging/napari-rembg.git"],"provides_extra":["local","testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}