Summer2009:Using ITK in python

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Key Investigators

  • GE: Jim Miller
  • Isomics: Steve Pieper
  • INRIA: Demian Wassermann

Objective

Develop strategies for embedding ITK filters in python code. See if this would be a workable strategy for creating slicer modules.

Approach, Plan

  • Look at how numpy arrays are passed to C/C++ code using techniques like cython, weave, etc.
  • Figure out what is needed to compile ITK code in these environments.
  • Compare and contrast this approach to WrapITK

Progress

  • Some pre-project week progress is shown on this page in the slicer.org wiki
  • Debugging of python internals with Demian and discussion of future directions.
  • Basic possibilities (all are somewhat convoluted):
    • WrapITK - combinatorial explosion; large libraries; C++ no longer available
    • Cython - yet another language; not clear how to use C++ idioms
    • Ctypeslib - good for teem.py, not good for C++
    • Weave - clear access to C++; can pass numpy arrays
    • Invoke CLI - clear distinction of C++ and python; easily supports multiple calling languages; CLIs are reusable executables/libraries; clear how to deploy; ITK IO requires one memcopy; approach is pretty indirect.
  • Confirmation that weave is a promising approach, but CLI still seems to be preferred approach.

References