Python VTK numpy数据3D可视化

在Python的3D图像处理中，通常用numpy array来进行非常方便的计算或者转化，这里记录一下numpy数据的VTK可视化基本流程，包括面绘制（Surfase Rendering）和体绘制（Volume Rendering）。除去数据格式转化，面绘制和体绘制在C++中也是类似的处理方法。

numpy数据转成`vtkImageData`

首先得把numpy数据转成vtk里可以用的格式：numpy array -> vtkIImageData。这里的numpy array是一个离散的三维空间数据场，0代表背景，非0代表前景点。

方法一：`numpy_to_vtk`

可以直接使用vtk.util.numpy_support里的转换方法，非常方便。这是它的说明文档：

numpy_to_vtk(num_array, deep=0, array_type=None)Converts a contiguous real numpy Array to a VTK array object.This function only works for real arrays that are contiguous.Complex arrays are NOT handled.  It also works for multi-componentarrays.  However, only 1, and 2 dimensional arrays are supported.This function is very efficient, so large arrays should not be aproblem.If the second argument is set to 1, the array is deep-copied fromfrom numpy. This is not as efficient as the default behavior(shallow copy) and uses more memory but detaches the two arrayssuch that the numpy array can be released.WARNING: You must maintain a reference to the passed numpy array, ifthe numpy data is gc'd and VTK will point to garbage which will inthe best case give you a segfault.Parameters----------- num_array :  a contiguous 1D or 2D, real numpy array.

对于3D array，可以用flatten或者ravel先转成1D array就可以使用了。这里的1D array得是C order（row-major order），最好使用deep copy以免出现一些内存管理的问题。

import numpy as np
import vtk
from vtk.util import numpy_support# numpy_data is a 3D numpy array
shape = numpy_data.shape[::-1]
vtk_data = numpy_support.numpy_to_vtk(numpy_data.ravel(), 1, vtk.VTK_SHORT)vtk_image_data = vtk.vtkImageData()
vtk_image_data.SetDimensions(shape)
vtk_image_data.SetSpacing(spacing)
vtk_image_data.SetOrigin(origin)
vtk_image_data.GetPointData().SetScalars(vtk_data)# vtk_image_data: ready to use

方法二：`vtkImageImport`

使用Python也可以用vtkImageImport来直接导入C array，就是要先把numpy array变成string。

import numpy as np
import vtknumpy_str = numpy_data.astype(np.int16).tostring()
x_extent = numpy_data.shape[2]
y_extent = numpy_data.shape[1]
z_extent = numpy_data.shape[0]image_import = vtk.vtkImageImport()
image_import.SetImportVoidPointer(numpy_str, len(numpy_str))
# 也可以使用CopyImportVoidPointer() 会copy一份numpy_str
image_import.SetWholeExtent(0, x_extent-1, 0, y_extent-1, 0, z_extent-1)
image_import.SetDataExtent(0, x_extent-1, 0, y_extent-1, 0, z_extent-1)
image_import.SetDataScalarTypeToShort() # 根据需求指定数据类型
image_import.SetNumberOfScalarComponents(1)
# 如果是RGB数据的话，SetNumberOfScalarComponents(3)
image_import.Update()vtk_image_data = vtk.vtkImageData()
vtk_image_data.SetSpacing(spacing)
vtk_image_data.SetOrigin(origin)# vtk_image_data: ready to use

绘制

参考：https://www.cnblogs.com/XDU-Lakers/p/10822840.html
这个作者总结的很全面，还有代码示例