A simple image-level annotation tool supporting multi-channel images for napari.

Overview

napari-labelimg4classification

License PyPI Python Version tests codecov napari hub

A simple image-level annotation tool supporting multi-channel images for napari.


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

Usage

Start the labeling tool from the menu Utilities > label tool for classification.
First, click on the Choose directory button to open the folder selection window, and select the folder that contains the images you want to label and annotate.
It will automatically list and display the images of tif, png, jpg, and bmp formats. If you want to view the channels of a multi-channel image separately, check the split channels checkbox.

Initially, all channels will be opened in grayscale, but the pseudo-color and contrast adjustments you specified will be carried over when you open the next image.
Thanks to napari, you can freely merge channels and turn them on and off.
Label classes can be added, and can be removed by typing the same name as an already added class.

It will automatically save the labels.csv file with the image path and label, and the class.txt file with the class of the label.

If labels.csv and class.txt are already in the folder, they will be loaded and reflected automatically.

Installation

You can install napari-labelimg4classification via pip:

pip install napari-labelimg4classification

To install latest development version :

pip install git+https://github.com/hiroalchem/napari-labelimg4classification.git

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 MIT license, "napari-labelimg4classification" is free and open source software

Issues

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

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Comments
  • fix links to videos on napari-hub

    fix links to videos on napari-hub

    Hi @hiroalchem ,

    great to see this plugin out! I'm sure it will be useful for many scientists out there.

    Just a minor thing, the videos don't show up on the napari-hub plugin page. This PR should fix this.

    Let me know if it works!

    Best, Robert

    opened by haesleinhuepf 2
  • Add scikit-image to requirements

    Add scikit-image to requirements

    Hi team,

    My name is Draga and I am a CZI contractor working on napari.

    As part of our work on the new napari plugin engine, we’ve been running some automated tests to see whether plugins will be easily converted to the new plugin engine. While running these tests, we noticed that your plugin fails to be imported after installation in a fresh environment due to a missing requirement. You can see details in this github action run.

    This PR therefore adds scikit-image to your install requirements so that users can install your plugin more confidently.

    Note that this may not be a complete list - to check whether your plugin can be easily installed on other machines, we recommend using GitHub workflows to test your plugin in a fresh environment - the napari plugin cookiecutter provides an example of such a workflow.

    If you believe this PR has been opened in error, please feel free to close it and let us know where we went wrong!

    opened by DragaDoncila 0
  • doesn't recognise jpg.

    doesn't recognise jpg.

    Hi Hiroalchem,

    thanks for this interesting tool. I am testing it for some jpg images, but it doesn't load them.

    is it because imgs = sorted(list(Path(self.target_dir.value).glob('./**/*png')) + list(Path(self.target_dir.value).glob('./**/*tif'))) in load_dir(self) doesn't check the jpg?

    Regards Chen

    opened by cloudsforest 1
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