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imglyb - Bridging The Chasm Between ImageJ and NumPy
The Python and Java communities have produced outstanding image processing tools, in particular the whole numpy environment on the Python side and imglib2 in Java. Combining these tools in a single workflow has been cumbersome at best, and required duplicating data in inter-process communication. I will present imglyb, a compatibility layer for numpy and imglib2 with shared memory between numpy arrays and imglib2 data structures. Python users now have access to the all imglib2 based image processing frameworks, including fast visualization of 3D image sequences for terabyte-sized data with the BigDataViewer.
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