pyimagej-fiji-bridgelisted

# PyImageJ — Python Bridge to ImageJ/Fiji ## Overview PyImageJ provides a Python interface to ImageJ2 and Fiji through PyJNIus and scyjava, embedding a full Java Virtual Machine inside a Python process. It enables bidirectional data exchange between NumPy arrays and ImageJ's ImagePlus/ImgLib2 data structures, so you can preprocess images in Python, pass them into Fiji plugins (Bio-Formats, TrackMate, Analyze Particles, Weka segmentation), and return results back to pandas DataFrames. The library supports headless operation for scripting and batch processing, as well as GUI mode for interactive Fiji sessions. ## When to Use - Running Fiji-specific plugins from Python: Bio-Formats multi-format I/O, TrackMate particle tracking, CLIJ2 GPU processing, or community Fiji update site plugins - Automating ImageJ macro pipelines headlessly without opening the Fiji GUI, e.g., batch processing an entire experiment overnight - Applying the ImageJ Ops framework (150+ image processing operations) with the full ImageJ type system - Converting between NumPy arrays (SciPy ecosystem) and ImageJ hyperstacks (TZCYX channel order) for round-trip processing - Parsing ImageJ Results tables and ROI Manager measurements into pandas DataFrames for downstream statistical analysis - Executing existing `.ijm` macro files as part of a Python workflow without rewriting them - Use `scikit-image` instead when you need pure Python processing without Fiji plugins — scikit-image is faster to install and avoi