Difference between revisions of "Summer2009:Using ITK in python"
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| − | Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]] | + | Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Project Week Main Page]] |
Image:Itk75.png|ITK provides body of algorithms in C++ | Image:Itk75.png|ITK provides body of algorithms in C++ | ||
Image:Python-logo.gif|Python provides interactive development environment. | Image:Python-logo.gif|Python provides interactive development environment. | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | + | * Some pre-project week progress is shown [https://www.slicer.org/wiki/Slicer3:Python:pitky 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. | ||
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* http://www.slideshare.net/gsroma/integrando-c-com-python-presentation (in portugese) | * http://www.slideshare.net/gsroma/integrando-c-com-python-presentation (in portugese) | ||
| + | * http://www.scipy.org/Cookbook/Weave | ||
Latest revision as of 17:39, 10 July 2017
Home < Summer2009:Using ITK in python
ITK provides body of algorithms in C++
Python provides interactive development environment.
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.
