This repository has been integrated into directly into ITK . This repository is now deprecated.
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 -- does not require NumPy to build.
This module is available in the ITK source tree as a Remote module. To enable it, set:
ITK_WRAP_PYTHON:BOOL=ON Module_BridgeNumPy: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, 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.