Improved documentation, the return of normalization

This release tackles the following aspects

• Erroneous message informing that an unimplemented 'global normalization' was being performed on downsampled data
• Update to documentation with Cellpose installation instructions and practical information
• Adds an updated JavaDoc
• Adds the option to normalize in QuPath by clipping values below 0.0 and above 1.0 after calling normalizePercentiles(double min, double max) in the builder.

Note
This implies that the order of normalizePercentiles(double min, double max) and preprocess() is important. Always finish with the normalization if using `preprocess()

Please read the updated documentation for some more small details

New Way to call cellpose

Inspired by the TrackMate-Cellpose and the following issue #12

This update adds the possibility to simply add the complete path to your python executable in order to run cellpose.

This also includes a fix that allows QuPath projects to have whitespaces in their path on Linux/MacOS .

Cellpose 2.0, with fewer bugs

Thanks to the community some bugs were spotted and fixed.

Improved training verbose support

This fixes training when using cellpose 1.0 as there was no output

Support for Cellpose 1.0

Adds option to Cellpose preferences for cellpose 1.0
Adds --verbose flag when using cellpose 1.0
Removes --resample flag

QuPath Compatibility with 0.3.2 and logging display

This small update fixes a slowdown in saving images caused by the new way the Bioformats reader is setup.

Furthermore this update includes an easier way to see and display the training of your cellpose model.
After building your cellpose object and after running the training with train(), you can now do the following

1. cellpose.getOutputLog() which will return a String array with all the logged steps from cellpose. You could save these as a file if you want.
2. Call cellpose.getTrainingResults() which parses the raw output log and returns a ResultsTable with the losses per epoch
3. Call cellpose.showTrainingGraph(), which displays a simple JavaFX plot of the Training and Validation Losses per Epoch.

The cellpose.getOutputLog() also would work when doing prediction instead of training. The others would simply be empty.

Happy training

Overhaul, Javadocs and new training options

Adds much documentation and a rework of the builder class.

Please check the documentation to spot the differenced in this version and do not hesitate to file Issues here or on https://forum.image.sc

Omnipose implementation

This new version uses cellpose/omnipose instead, which has an updated CLI.

This means that your previous cellpose environment will no longer work

If you have not done it before, please update cellpose from their instructions or create a new virtual environment for omnipose as per
https://github.com/MouseLand/cellpose

Fixes overlap issues and changes how training is handled

adds new logic for filtering objects

fixes training example script to remove diameter as it is not useful

Adds Training capability and demo scripts

v0.1.2

matches reported version to jar version