pyimagej-fiji-bridge
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
.ijmmacro files as part of a Python workflow without rewriting them - Use
scikit-imageinstead when you need pure Python processing without Fiji plugins — scikit-image is faster to install and avoids JVM overhead - Use
napariinstead for interactive multi-dimensional image visualization and annotation; PyImageJ does not replace a viewer
Prerequisites
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