We want your feedback: take the 2023 annual survey and help us improve napari for you and the community!

Blossom

napari-blossom

Segmentation of blossom apple tree images

License BSD-3 PyPI Python Version tests codecov napari hub

Segmentation of blossom apple tree images


This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

This plugin was written by Herearii Metuarea, student intern at LARIS (French laboratory located in Angers, France) in Imhorphen, french scientific team lead by David Rousseau (Full professor). This plugin was designed as part of the european project INVITE.

Logo-IRHS-h_2022_png_large Logo-INRAE logo2 logolaris1 logo1

Installation

You can install napari-blossom via pip:

pip install napari-blossom

To install latest development version :

pip install git+https://github.com/hereariim/napari-blossom.git

How does it works

This module offers a plugin that allows you to segment the images of the apple tree flowers. As input, you can enter a single image with the image selection widget. Once the image is entered in the napari window, you can segment the apple blossoms with the image segmentation widget by running the run button. The segmented image will appear in the napari window.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-blossom" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

Version:

  • 0.1.5

Last updated:

  • 25 October 2023

First released:

  • 20 June 2022

License:

Supported data:

  • Information not submitted

Open extension:

Save extension:

Save layers:

GitHub activity:

  • Stars: 3
  • Forks: 0
  • Issues + PRs: 0

Python versions supported:

Operating system:

Requirements:

  • numpy >=1.23.0
  • magicgui >=0.6.1
  • qtpy
  • opencv-python-headless >=4.7.0.68
  • tensorflow >=2.11.0
  • scikit-image >=0.19.3
  • napari
  • focal-loss >=0.0.7
  • pillow >=9.3.0
  • tqdm >=4.64.1

Sign up to receive updates