{"name":"frontveg","display_name":"Frontveg","visibility":"public","icon":"","categories":["Annotation","Segmentation","Acquisition"],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"frontveg.vegetation","title":"Vegetation plugin","python_name":"frontveg:vegetation","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"frontveg.vegetation","display_name":"Frontground vegetation","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"frontveg","version":"0.3.5","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"Segmentation of vegetation located to close to camera","description":"# frontveg\n\n[![License BSD-3](https://img.shields.io/pypi/l/frontveg.svg?color=green)](https://github.com/hereariim/frontveg/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/frontveg.svg?color=green)](https://pypi.org/project/frontveg)\n[![Python Version](https://img.shields.io/pypi/pyversions/frontveg.svg?color=green)](https://python.org)\n[![tests](https://github.com/hereariim/frontveg/workflows/tests/badge.svg)](https://github.com/hereariim/frontveg/actions)\n[![codecov](https://codecov.io/gh/hereariim/frontveg/branch/main/graph/badge.svg)](https://codecov.io/gh/hereariim/frontveg)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/frontveg)](https://napari-hub.org/plugins/frontveg)\n[![npe2](https://img.shields.io/badge/plugin-npe2-blue?link=https://napari.org/stable/plugins/index.html)](https://napari.org/stable/plugins/index.html)\n[![Copier](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/copier-org/copier/master/img/badge/badge-grayscale-inverted-border-purple.json)](https://github.com/copier-org/copier)\n\nA plugin for foreground vegetation segmentation, tailored for trellised vegetation row images. It uses RGB images to perform inference and allows users to manually refine the generated mask.\n\n----------------------------------\n\nThe method was developped by Herearii Metuarea, PHENET PhD at LARIS (French laboratory located in Angers, France) and Abdoul Djalil Ousseini Hamza, AgroEcoPhen Engineer at IRHS (French Institute located in INRAe Angers, France) in Imhorphen team (bioimaging research group lead) under the supervision of Eric Duchêne (Research Engineer), Morgane Roth (Research Engineer) and David Rousseau (Full professor). This plugin was written by Herearii Metuarea and was designed in the context of the european project PHENET.\n\n![Data Warehouse](https://github.com/user-attachments/assets/4a110408-5854-4e8c-b655-4cb588434b79)\n\n\n----------------------------------\n\nThis [napari] plugin was generated with [copier] using the [napari-plugin-template].\n\n<!--\nDon't miss the full getting started guide to set up your new package:\nhttps://github.com/napari/napari-plugin-template#getting-started\n\nand review the napari docs for plugin developers:\nhttps://napari.org/stable/plugins/index.html\n-->\n\n## Installation\n\nYou can install `frontveg` via [pip]:\n\n    pip install frontveg\n\nTo install latest development version :\n\n    pip install git+https://github.com/hereariim/frontveg.git\n\nGPU is mandatory for time processing and models running (especially Grounding-DINO). Please visit the official PyTorch website to get the appropriate installation command: 👉 https://pytorch.org/get-started/locally\n\n**Exemple : GPU (CUDA 12.1)**\n\n    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n\n## Description\n\nThis plugin is a tool to perform image inference. This plugin contained two steps of image processing. First, from RGB image, a depth map is estimated and then thresholded based on the estimated depth histogram modes to detect foreground and background regions in image. Second, a Grounding DINO model detects foliage in the foreground. The output is a binary mask where white colour are associated to foliage in the foreground.\n\nThe plugin is applicable to images of trellised plants; in this configuration, it has been applied to images of pome fruit trees (apple), stone fruit trees (apricot) and climbing plants (grapevine).\n\n![sample_example](https://github.com/user-attachments/assets/ae845e01-9f48-4bcf-98ad-bf5f6e037f01)\n\n## Contact\n\nImhorphen team, bioimaging research group\n\n42 rue George Morel, Angers, France\n\n- Pr David Rousseau, david.rousseau@univ-angers.fr\n- Abdoul Djalil Ousseini Hamza, abdoul-djalil.ousseini-hamza@inrae.fr\n- Herearii Metuarea, herearii.metuarea@univ-angers.fr\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"frontveg\" 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[copier]: https://copier.readthedocs.io/en/stable/\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[napari-plugin-template]: https://github.com/napari/napari-plugin-template\n\n[file an issue]: https://github.com/hereariim/frontveg/issues\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":"Herearii Metuarea","author_email":"herearii.metuarea@univ-angers.fr","maintainer":null,"maintainer_email":null,"license":"Copyright (c) 2025, Herearii Metuarea\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n","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 :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["numpy","magicgui","qtpy","scikit-image","transformers==4.51.3","torch>=2.3.1","torchvision>=0.18.1","hydra-core==1.3.2","iopath>=0.1.10","pillow>=9.4.0","sam2==1.1.0","tox; extra == \"testing\"","pytest; extra == \"testing\"","pytest-cov; extra == \"testing\"","pytest-qt; extra == \"testing\"","napari; extra == \"testing\"","pyqt5; extra == \"testing\""],"requires_python":"==3.11.12","requires_external":null,"project_url":["Bug Tracker, https://github.com/hereariim/frontveg/issues","Documentation, https://github.com/hereariim/frontveg#README.md","Source Code, https://github.com/hereariim/frontveg","User Support, https://github.com/hereariim/frontveg/issues"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}