[ITK] [ITK-users] ITK Python: numpy to itk image (and viceversa)

[Matt McCormick]

Hi Fabio,

Yes, building Python extensions that depend on libraries like Cuda is muich
easier with CMake build system support in scikit-build.

I will follow up with an example of how to set up scikit-build for
ITK-based C++ code.

Thanks,
Matt

On Sat, Apr 22, 2017 at 11:48 AM, D'Isidoro Fabio <fisidoro at ethz.ch> wrote:

> Thank you Matt,
>
> I am looking now at scikit-build.
>
> Do you think I can use it to build C++/CUDA accelerated codes that use ITK
> too?
>
> Is there a link to a simple example of how to setup a scikit-build for an
> ITK-based C++ code?
>
> Thank you,
> Fabio.
>
> -----Original Message-----
> From: Matt McCormick [mailto:matt.mccormick at kitware.com]
> Sent: Montag, 3. April 2017 20:41
> To: D'Isidoro Fabio <fisidoro at ethz.ch>
> Cc: insight-users at itk.org
> Subject: Re: [ITK-users] ITK Python: numpy to itk image (and viceversa)
>
> Hallo Fabio,
>
>
> > I use ITK with Python Wrap. I need to interface my Python code with a
> > Cython-wrapped C++ code that takes only numpy array as input and
> > returns numpy array as output.
>
> Cool. By the way, you may be interested in scikit-build [1], which is a
> good way to build Cython-wrapped C++ code. We are using it for the ITK and
> SimpleITK Python packages, and it has good Cython and CMake support.
>
>
> > Hence, I need to convert the Python itk images into numpy array to be
> > given as input to the wrapped C++ code, and then convert the numpy
> > array in output from the wrapped C++ code back into python itk images.
> >
> >
> >
> > Question 1) How can I do that in an efficient way? I found some posts
> > on itk.PyBuffer but I could not find anywhere any reference on how to
> > install it on my itk wrap build.
>
> Yes, itk.PyBuffer works great for that. Please review a PR for some
> additional documentation:
>
>   https://github.com/InsightSoftwareConsortium/ITKBridgeNumPy/pull/18
>
> This has been available in ITK for a few releases as a Remote module,
> which can be enabled by setting
>
>   Module_BridgeNumPy=ON
>
> in ITK's CMake configuration.
>
>
> Since ITK 4.11.0, it is easier to build since it does not require the
> NumPy headers.
>
>
> In current ITK Git master (to be 4.12.0) the module is enabled by default.
>
>
> Nightly ITK Python packages for ITK Git master are now being built:
>
>   https://github.com/InsightSoftwareConsortium/ITKPythonPackage
>
> macOS and Linux are available. Windows packages will be available over the
> coming weeks.
>
>
>
> > Question 2) The purpose of writing a part of my algorithm in C++ is to
> > speed up the code. If the conversion between python itk images and
> > numpy arrays is slow, I would lose all the speed gain obtained with the
> C++ implementation.
> > Are there better ways to deal with that?
>
> The newer versions ITKBridgeNumPy use a NumPy array view, which does not
> do any copies during the conversion, and it is very fast.
>
>
> HTH,
> Matt
>
>
> [1] http://scikit-build.org/
>
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