Python-VTK：直接坐标到PolyData

我想使用步骤1将特定范围内x、y和z的所有坐标组合直接转换为vtk.polyData或vtk.points。我的第一种方法是使用itertools.product，但我认为这会有一个非常糟糕的运行时。所以我用vtk找到了另一种方法，我的程序的下一部分需要它

第一次使用itertools.product进行评估

import numpy as np
import itertools
import vtk

x1=[10,11,12....310]
y1=[10,11,12....310]
z1=[0,1,2....65]

points1 = vtk.vtkPoints()                      
for coords in itertools.product(x1,y1,z1):
   points1.InsertNextPoint(coords)
boxPolyData1 = vtk.vtkPolyData()
boxPolyData1.SetPoints(points1)

迄今为止我对vtk的做法：

import numpy as np
from vtk.util import numpy_support

coords = np.mgrid[10:310, 10:310, 0:65]
vtk_data_array = numpy_support.numpy_to_vtk(num_array=coords.ravel(),deep=True,array_type=vtk.VTK_FLOAT)

points = vtk.vtkPoints()
points.SetData(vtk_data_array)

但是他的话让我的python崩溃了。有人有主意吗

---

致以最良好的祝愿

将那些

coords

堆叠在带有或的列中，然后将它们作为输入馈送到

num\u数组中，如下所示-

x,y,z = np.mgrid[10:310, 10:310, 0:65]
out_data = np.column_stack((x.ravel(), y.ravel(), z.ravel()))

vtk_data_array = numpy_support.numpy_to_vtk(num_array=out_data,\
                              deep=True,array_type=vtk.VTK_FLOAT)

def create_mgrid_array(d00,d01,d10,d11,d20,d21,dtype=int):
    df0 = d01-d00
    df1 = d11-d10
    df2 = d21-d20
    a = np.zeros((df0,df1,df2,3),dtype=dtype)
    X,Y,Z = np.ogrid[d00:d01,d10:d11,d20:d21]
    a[:,:,:,2] = Z
    a[:,:,:,1] = Y
    a[:,:,:,0] = X
    a.shape = (-1,3)
    return a

或者，直接获取
数据
-

out_data = np.mgrid[10:310, 10:310, 0:65].reshape(3,-1).T

使用
initialization
替换
np.mgrid
创建的
3D
数组的另一种方法如下-

x,y,z = np.mgrid[10:310, 10:310, 0:65]
out_data = np.column_stack((x.ravel(), y.ravel(), z.ravel()))

vtk_data_array = numpy_support.numpy_to_vtk(num_array=out_data,\
                              deep=True,array_type=vtk.VTK_FLOAT)

def create_mgrid_array(d00,d01,d10,d11,d20,d21,dtype=int):
    df0 = d01-d00
    df1 = d11-d10
    df2 = d21-d20
    a = np.zeros((df0,df1,df2,3),dtype=dtype)
    X,Y,Z = np.ogrid[d00:d01,d10:d11,d20:d21]
    a[:,:,:,2] = Z
    a[:,:,:,1] = Y
    a[:,:,:,0] = X
    a.shape = (-1,3)
    return a

示例运行以展示
创建\u mgrid\u数组的用法-

In [151]: create_mgrid_array(3,6,10,14,20,22,dtype=int)
Out[151]: 
array([[ 3, 10, 20],
       [ 3, 10, 21],
       [ 3, 11, 20],
       [ 3, 11, 21],
       [ 3, 12, 20],
       [ 3, 12, 21],
       [ 3, 13, 20],
       [ 3, 13, 21],
       [ 4, 10, 20],
       [ 4, 10, 21],
       [ 4, 11, 20],
       [ 4, 11, 21],
       [ 4, 12, 20],
       [ 4, 12, 21],
       [ 4, 13, 20],
       [ 4, 13, 21],
       [ 5, 10, 20],
       [ 5, 10, 21],
       [ 5, 11, 20],
       [ 5, 11, 21],
       [ 5, 12, 20],
       [ 5, 12, 21],
       [ 5, 13, 20],
       [ 5, 13, 21]])

运行时测试

import numpy as np
import itertools
import vtk

x1=[10,11,12....310]
y1=[10,11,12....310]
z1=[0,1,2....65]

points1 = vtk.vtkPoints()                      
for coords in itertools.product(x1,y1,z1):
   points1.InsertNextPoint(coords)
boxPolyData1 = vtk.vtkPolyData()
boxPolyData1.SetPoints(points1)

接近-

def loopy_app():
    x1 = range(10,311)
    y1 = range(10,311)
    z1 = range(0,66)

    points1 = vtk.vtkPoints()                      
    for coords in itertools.product(x1,y1,z1):
       points1.InsertNextPoint(coords)
    return points1

def vectorized_app():
    out_data = create_mgrid_array(10,311,10,311,0,66,dtype=float)
    vtk_data_array = numpy_support.numpy_to_vtk(num_array=out_data,\
                                    deep=True,array_type=vtk.VTK_FLOAT)

    points2 = vtk.vtkPoints()
    points2.SetData(vtk_data_array)
    return points2

时间安排和核查-

In [155]: # Verify outputs with loopy and vectorized approaches    
     ...: out1 =  vtk_to_numpy(loopy_app().GetData())
     ...: out2 =  vtk_to_numpy(vectorized_app().GetData())
     ...: print np.allclose(out1, out2)
     ...: 
True

In [156]: %timeit loopy_app()
1 loops, best of 3: 923 ms per loop

In [157]: %timeit vectorized_app()
10 loops, best of 3: 67.3 ms per loop

In [158]: 923/67.3
Out[158]: 13.714710252600298

13x+在循环的基础上，使用建议的矢量化方法加速
 谢谢：），但现在我得到了错误：AssertionError:只支持连续数组：（@Varlor Use
out\u data=np.ascontiguousarray（out\u data）
，然后馈送到
numpy\u-to\u-vtk
？很好，非常感谢，它可以工作。但不幸的是，它没有对itertools的运行时改进。产品：（@Varlor使用新的基于初始化的方法检查编辑，显示
13x+
加速。