This package contains a diverse collection of Python functions dealing with paths, I/O (file handles, ...), strings etc. and tons of Fiji / ImageJ2 convenience wrappers to simplify scripting and reduce cross-script redundanciees.
Initially this has been a multi-purpose package where a substantial part had been useful in CPython as well. However, since the latest Jython release is still based on Python 2.7 (see the Jython 3 roadmap for more info), imcflibs is now basically limited to the Fiji / ImageJ2 ecosystem.
Releases are made through Maven and published to the SciJava Maven
repository. The easiest way to use the lib is by adding the IMCF Uni Basel update site to your ImageJ installation.
The pip installable package is probably only useful for two cases:
running pytest (where applicable) and rendering HTML-based API docs
using pdoc. Let us know in case you're having another use case 🎪 for
it.
Developed and provided by the Imaging Core Facility (IMCF) of the Biozentrum, University of Basel, Switzerland.
Apply a shading correction model and create a maximum-intensity projection:
from imcflibs.imagej.shading import correct_and_project
model = "/path/to/shading_model.tif"
raw_image = "/path/to/raw_data/image.ome.tif"
out_path = "/path/to/processed_data/"
correct_and_project(raw_image, out_path, model, "Maximum", ".ics")- See the Split_TIFFs_By_Channels_And_Slices.py script.
- See the FluoView_OIF_OIB_OIR_Simple_Stitcher.py script.