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

Wed Apr 19 15:57:09 EDT 2017

Hi Fabio,

I just tested the master branch after CastXML was updated. Unfortunately,
it didn't help. The compile errors are still present in VS2017 with
wrapping.

Regards,
Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)

On Thu, Apr 13, 2017 at 10:48 AM, Dženan Zukić <dzenanz at gmail.com> wrote:

> Hi Fabio,
>
> the first error is coming from ITKCommonCastXML project, and the other
> first 1000 errors are also coming from*CastXML and *Swig projects. This
> means that the wrapping infrastructure is somehow triggering that bug in
> VS2017.
>
> The easiest thing to try is update CastXML, which Matt said would take
> care of. Let's see if that helps.
>
> Regards,
> Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)
>
> On Wed, Apr 12, 2017 at 4:50 PM, Dženan Zukić <dzenanz at gmail.com> wrote:
>
>> Hi Fabio,
>>
>> it looks like we will have to wait somewhat until VS writers get around
>> to implement __builtin_offsetof
>> <https://developercommunity.visualstudio.com/content/problem/22196/static-assert-cannot-compile-constexprs-method-tha.html>
>>
>> I don't know why it doesn't trigger when Python wrapping is OFF. I will
>> look into it more.
>>
>> Regards,
>> Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)
>>
>> On Wed, Apr 12, 2017 at 4:26 PM, Francois Budin <
>> francois.budin at kitware.com> wrote:
>>
>>> Fabio,
>>>
>>> To add a little bit of extra information about using BridgeNumpy:
>>>
>>> From NumPy to ITK, the memory is never copied, and always shared between
>>> the NumPy array and the ITK image.
>>> From ITK to NumPy, the memory is not copied at first, if the data is not
>>> changed from the NumPy side (e.g. if the ITK image is changed, the NumPy
>>> array will be modified too), but if you modify the NumPy array directly,
>>> NumPy will create a copy of the data. This also means that if you were to
>>> modify the NumPy array, the modifications would not be visible in the ITK
>>> image.
>>>
>>> A limited workaround to this is:
>>>
>>> # Get NumPy array from image
>>> arr_image = itk.GetArrayFromImage(image)
>>> # Create copy of array that can be modified in-place
>>> arr = arr_image.copy()
>>> # Modify array
>>> …
>>> # Update image in-place with new array
>>> arr_image.setfield(arr,arr_image.dtype)
>>>
>>> The advantage of this method is that the image will be updated. However,
>>> there is an explicit copy of your data.
>>> If the copy step is skipped and the original NumPy array is modified, a
>>> copy of the data will be performed by NumPy and you may not be aware of it
>>> until you see that the data in the NumPy array and the image do not match
>>> anymore.
>>>
>>> Hope this helps,
>>> Francois
>>>
>>> On Wed, Apr 12, 2017 at 4:02 PM, D'Isidoro Fabio <fisidoro at ethz.ch>
>>> wrote:
>>>
>>>> Ok, thanks. Moe specifically I am using now *Visual Studio 2017
>>>> Preview*.
>>>>
>>>>
>>>>
>>>> Fabio.
>>>>
>>>> *From:* Dženan Zukić [mailto:dzenanz at gmail.com]
>>>> *Sent:* Mittwoch, 12. April 2017 21:54
>>>> *To:* D'Isidoro Fabio <fisidoro at ethz.ch>
>>>> *Cc:* Matt McCormick <matt.mccormick at kitware.com>;
>>>> insight-users at itk.org
>>>>
>>>> *Subject:* Re: [ITK-users] ITK Python: numpy to itk image (and
>>>> viceversa)
>>>>
>>>>
>>>>
>>>> Hi Fabio,
>>>>
>>>>
>>>>
>>>> let me give it a try with VS2017 and Wrapping+NumPy. I will report back
>>>> when I have an update.
>>>>
>>>>
>>>>
>>>> Regards,
>>>>
>>>> Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)
>>>>
>>>>
>>>>
>>>> On Wed, Apr 12, 2017 at 2:16 PM, D'Isidoro Fabio <fisidoro at ethz.ch>
>>>> wrote:
>>>>
>>>> Thank you. I am trying to build ITK Wrap Python with
>>>> Module_BridgeNumPy=ON with Visual Studio 2017.
>>>>
>>>> I get the following type of errors:
>>>>
>>>> 12>C:/Program Files (x86)/Microsoft Visual
>>>> Studio/Preview/Community/VC/Tools/MSVC/14.10.25017/include\xstring(1905,26):
>>>> error G3F63BFAE: constexpr variable '_Memcpy_move_offset' must be
>>>> initialized by a constant expression
>>>> 12>        static constexpr size_t _Memcpy_move_offset =
>>>> offsetof(_Mydata_t, _Bx);
>>>> 12>                                ^
>>>>  ~~~~~~~~~~~~~~~~~~~~~~~~
>>>> 12>C:/Program Files (x86)/Microsoft Visual
>>>> Studio/Preview/Community/VC/Tools/MSVC/14.10.25017/include\stdexcept:23:21:
>>>> note: in instantiation of template class 'std::basic_string<char,
>>>> std::char_traits<char>, std::allocator<char> >' requested here
>>>> 12>                : _Mybase(_Message.c_str())
>>>> 12>                                  ^
>>>> 12>C:/Program Files (x86)/Microsoft Visual
>>>> Studio/Preview/Community/VC/Tools/MSVC/14.10.25017/include\xstring:1905:48:
>>>> note: cast that performs the conversions of a reinterpret_cast is not
>>>> allowed in a constant expression
>>>> 12>        static constexpr size_t _Memcpy_move_offset =
>>>> offsetof(_Mydata_t, _Bx);
>>>> 12>                                                      ^
>>>> 12>C:/Program Files (x86)/Windows Kits/10/Include/10.0.10586.0/ucrt\stddef.h:38:32:
>>>> note: expanded from macro 'offsetof'
>>>> 12>        #define offsetof(s,m) ((size_t)&reinterpret_cast<char const
>>>> volatile&>((((s*)0)->m)))
>>>>
>>>>
>>>> The build worked with Visual Studio 2015 instead.
>>>>
>>>> Can I fix this issue with Visual Studio 2017 somehow?
>>>>
>>>> Thank you.
>>>>
>>>> ----------------------------------------------------------------------
>>>> Fabio D’Isidoro - PhD Student
>>>> Institute of Biomechanics
>>>> HPP O 14
>>>> Hönggerbergring 64
>>>> 8093 Zürich, Switzerland
>>>>
>>>>
>>>> -----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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>>>
>>
>
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