Release for first publication in Zenodo

This is not a release per se, but will trigger Zenodo to generate a DOI for the software.

Experimental faster label image read

Adds an option readResultsAsynchronously() in the cellpose builder that, when activated, tries to

1. Create a file Watcher on the folder where cellpose is supposed to write the predicted labels
2. On new files being created, calls the image to label conversion while the prediction continues for the rest of the tiles

This is experimental, feel free to test it, with no promises

QuPath 0.5.0 compatibility

The new cellpose extension with QuPath 0.5.0 support.

Also includes a new version of que Quality Control Python script, so please update that one too

Small bugfixes, custom folders export, script templates

This new release adds the following

1. You can define output folders for all things now when invoking the builder:
   • groundTruthDirectory(java.io.File groundTruthDirectory) Specify the training directory where thes test and train images will be added
   • tempDirectory(java.io.File groundTruthDirectory) Specify the temporary directory when using cellpose inference
   • modelDirectory(java.io.File groundTruthDirectory) Specify the where the model will be saved
   • the Quality Control results remain inside the model directory
2. an Omnipose internal variable name had a typo and was fixed. You will have to re-enter the path in in your Edit > Preferences if you are using Omnipose
3. Script templates: Training and detection script templates are now in Extensions > Cellpose, Just like the StarDist extension (Thank you @petebankhead)
4. Names for Cellpose preferences were changed to empahsise how we need the full path to python.exe

Happy Cellposing

Bugfixes and the return of Omnipose

With the current versions of Cellpose and Omnipose, we have gotten them to play nice enough with each other and drafter this new release with a (small) breaking change.

Python Path is now the only available way of calling your cellpose or omnipose installation. You will need to update your Preferences under Cellpose/Omnipose

This follows with what was done for TrackMate, and simplifies our life so we can be conda/mamba/venv agnostic. This has been tested on Windows and Mac with no problems. We of course welcome feedback!

The documentation has been updated to reflect this change.

A small bug where preprocessing steps were not taken into account on training images was also fixed

Tile creation rollback

This update simply adds a rollback on how tiles were computed for cellpose.

When QuPath 0.4.1 came out, the logic of the tiles was rewritted based on the StarDist Extension, which would force tiles to be a particular size, with optional padding. In our case, this tended to create tiles that were coering way more image space than necessary, creating large overhead.

So with this update, we have reverted back to the older way of doing it.

This should have no change, or perhaps very minor changes in the global normalization numbers and in cellpose normalization, because of the change in tile size and thus contents.

QuPath v0.4.0 Flavored Release

This release implements the awesome new changes that came with the release of QuPath 0.4.0 and the updated StarDist extension for it.

And this has also led to a couple of small modifications

1. Some Cellpose builder methods were removed in favor or a more flexible way of passing parameters
2. The new version of the extension only supports Cellpose versions above 2.0
3. You can now add any cellpose parameter using addParameter() in the builder
4. There is now a possibility for global normalization!

Check out the updated readme for further information and do not hesitate to go to https://forum.image.sc to discuss usage issues.

Object overlap, quality of life updates and new training option

This update fixes an issue in the way objects were being detected and re-added to QuPath.

It now follows much more closely what is done in the StarDist Extension and should result in fewer issues.

Now the Z and T positions are also appended to the names of the exported images. Useful for 3D and T stacks

Adds the min_training_masks option to help avoid cellpose from excluding images with less than 5 annotations during training

Finally, it adds a bit more verbosity. The full command used to run or train is output in such a way that it can be run from the command line by copy-paste

You should unzip the contents of the file below into your extensions directory

Improved Integrated Quality Control

This release builds on v0.4.0 #7365020 following comments by @romainGuiet to integrate the whole QC directly rather than run it separately (Which no-one will ever do until it's too late 😅).

Improvements

Quality control is perfomed after training provided that:

1. You added the scikit-image dependency to your cellpose installation
2. You copy run-cellpose-qc.py into your QuPath extensions folder

Read the updated ReadMe to see how to do these installation steps.
Here is an example gif in real time (only 100 epochs though)

Possibility to save the training graph as a PNG in your QuPath project's QC folder

On top of showing you the training graph if you request it after training using showTrainingGraph(),
you can also choose to save it but not show it using showTrainingGraph(boolean show, boolean save)

Updated JavaDoc and ReadMe to account for the new features

As ususal, JavaDoc is here: https://biop.github.io/qupath-extension-cellpose/qupath/ext/biop/cellpose

Always happy to receive comments and suggestions!

Adds Quality Control Notebook and Training now also saves Validation Predictions

Please see the forum post for more information
https://forum.image.sc/t/qupath-extension-cellpose-update-validation/71320

Also adds a fix to how the QuPath Proejct is recovered when training from another thread (Like when creating a button dynamically)