如何将vtkimage转换为numpy数组

I tried to deal with MHD image files with python and python-vtk. The file is placed in google drive : mhd .

I want to convert it into numpy array and then split them according to a given value' 500 for instance '. then calculate the summary information. I followed the instruction of this
[post] How to convert a 3D vtkDataSet into a numpy array?
but it does not work for my case.

我试图用python和python-vtk处理MHD图像文件。该文件放在谷歌驱动器:mhd。我想将它转换为numpy数组,然后根据给定的值“500例如”分割它们。然后计算摘要信息。我按照这个[post]的说明如何将3D vtkDataSet转换为numpy数组?但它对我的情况不起作用。

import vtk
imageReader = vtk.vtkMetaImageReader()
imageReader.SetFileName(testfile1)
imageReader.Update() 
# from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy does not work for the data type issue

image = imageReader.GetOutput()
# List the dimensions of the image, for example
print image.GetDimensions()
pixelspace = imageReader.GetPixelSpacing()

an error comes here:

这里出现错误:

AttributeError: GetPixelSpacing

How could I achieve the conversion?
When I finish the data splitting, how could I save them back to mhd (or a raw data would be better ?)

我怎么能实现转换?当我完成数据分割时,我怎么能将它们保存回mhd(或原始数据会更好?)

2 个解决方案

#1

Adapting from this thread... you can do the following:

适应这个线程......您可以执行以下操作:

import numpy as np
import vtk
from vtk.util.numpy_support import vtk_to_numpy

imr = vtk.vtkMetaImageReader()
imr.SetFileName('t10-Subvolume-resample_scale-1.mhd')
imr.Update()

im = imr.GetOutput()
rows, cols, _ = im.GetDimensions()
sc = im.GetPointData().GetScalars()
a = vtk_to_numpy(sc)
a = a.reshape(rows, cols, -1)

assert a.shape==im.GetDimensions()

where a will be a NumPy array containing the image data.

其中a将是包含图像数据的NumPy数组。

#2

To get the channels right for later use as OpenCV image:

要使频道适合以后用作OpenCV图像:

import vtk
import numpy as np
from vtk.util import numpy_support

def vtkImgToNumpyArray(vtkImageData):
    rows, cols, _ = vtkImageData.GetDimensions()
    scalars = vtkImageData.GetPointData().GetScalars()
    resultingNumpyArray = numpy_support.vtk_to_numpy(scalars)
    resultingNumpyArray = resultingNumpyArray.reshape(cols, rows, -1)
    red, green, blue, alpha = np.dsplit(resultingNumpyArray, resultingNumpyArray.shape[-1])
    resultingNumpyArray = np.stack([blue, green, red, alpha], 2).squeeze()
    resultingNumpyArray = np.flip(resultingNumpyArray, 0)
    return resultingNumpyArray

#1