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ITKBridgeNumPy
==============
This is a port from the original WrapITK PyBuffer to an ITKv4 module.
Differences from the original PyBuffer:
- Support for VectorImage's
- Option to not swap the axes (only for GetArrayViewFromImage)
- Tests
- Based on the `Python Buffer Protocol <https://docs.python.org/3/c-api/buffer.html>`_ -- only requires NumPy at run time, not build time.
This module is available in the ITK source tree as a Remote
module. To enable it, set::
ITK_WRAP_PYTHON:BOOL=ON
Module_ITKBridgeNumPy:BOOL=ON
in ITK's CMake build configuration. In ITK 4.12 and later, this module is
enabled by default when `ITK_WRAP_PYTHON` is enabled.
To get a view of an ITK image in a NumPy array::
import itk
PixelType = itk.ctype('float')
Dimension = 3
ImageType = itk.Image[PixelType, Dimension]
image = ImageType.New()
size = itk.Size[Dimension]()
size.Fill(100)
region = itk.ImageRegion[Dimension](size)
image.SetRegions(region)
image.Allocate()
arr = itk.PyBuffer[ImageType].GetArrayViewFromImage(image)
To get a view of a NumPy array in an ITK image::
import numpy as np
import itk
PixelType = itk.ctype('float')
Dimension = 3
ImageType = itk.Image[PixelType, Dimension]
arr = np.zeros((100, 100, 100), np.float32)
image = itk.PyBuffer[ImageType].GetImageViewFromArray(arr)
It is also possible to get views of VNL matrices and arrays from NumPy arrays and
back::
import numpy as np
import itk
ElementType = itk.ctype('float')
vector = itk.vnl_vector[ElementType]()
vector.set_size(8)
arr = itk.PyVnl[ElementType].GetArrayViewFromVnlVector(vector)
matrix = itk.vnl_matrix[ElementType]()
matrix.set_size(3, 4)
arr = itk.PyVnl[ElementType].GetArrayViewFromVnlMatrix(matrix)
arr = np.zeros((100,), np.float32)
vector = itk.PyVnl[ElementType].GetVnlVectorViewFromArray(arr)
arr = np.zeros((100, 100), np.float32)
matrix = itk.PyVnl[ElementType].GetVnlMatrixViewFromArray(arr)
.. warning::
The conversions create `NumPy Views
<https://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.html>`_, i.e. it
presents the ITK image pixel buffer in the NumPy array, and the buffer is
shared. This means that no copies are made (which increases speed and
reduces memory consumption). It also means that any changes in the NumPy
array change the ITK image content. Additionally, a reference to an ITK
image object must be available to use its NumPy array view. Using an array
view after its source image has been deleted can results in corrupt values
or a segfault.
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