如何使用numpy-stl在python中保存多个网格

我正在研究一段 python代码,它将采用灰度图像,缩放它,并输出一个3d模型,每个像素的高度由灰度值决定.除了3d模型的输出外,我的一切都在工作.我正在使用numpy-stl基于从图像派生的值数组来创建它.使用numpy-stl库我创建一个框,然后根据图像需要复制它多次.然后我将每一个翻译成与图像对应的位置和高度.这一切都有效.当我尝试将它们全部保存为一个.stl文件时出现问题.我无法弄清楚如何将立方体的所有单个网格组合成一个.

这里只是处理3d数组创建的代码.我可以绘制创建的网格但不保存它们.

from stl import mesh
import math
import numpy

test = [[1,2],[2,1]]

a = [[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]


# Create 6 faces of a cube,2 triagles per face
data = numpy.zeros(12,dtype=mesh.Mesh.dtype)
#cube defined in stl format 
# Top of the cube
data['vectors'][0] = numpy.array([[0,1,1],[1,[0,1]])
data['vectors'][1] = numpy.array([[1,1]])
# Right face
data['vectors'][2] = numpy.array([[1,0],0]])
data['vectors'][3] = numpy.array([[1,0]])
# Left face
data['vectors'][4] = numpy.array([[0,1]])
data['vectors'][5] = numpy.array([[0,1]])
# Bottem of the cube
data['vectors'][6] = numpy.array([[0,0]])
data['vectors'][7] = numpy.array([[1,0]])
# Right back
data['vectors'][8] = numpy.array([[0,0]])
data['vectors'][9] = numpy.array([[0,0]])
# Left back 
data['vectors'][10] = numpy.array([[0,1]])
data['vectors'][11] = numpy.array([[0,1]])


# Generate 4 different meshes so we can rotate them later
meshes = [mesh.Mesh(data.copy()) for _ in range(16)]

#iterates through the array and translates cube in the x and y direction according 
#to position in array and in the z direction according to eh value stored in the array
def ArrayToSTL(array,STLmesh):
  y_count = 0
  x_count = 0
  count = 0
  for row in array:
    x_count = 0
    for item in row:
      meshes[count].x += x_count
      meshes[count].y += y_count
      meshes[count].z += item
      x_count +=1
      count += 1
    y_count += 1

ArrayToSTL(a,meshes)



# Optionally render the rotated cube faces
from matplotlib import pyplot
from mpl_toolkits import mplot3d

# Create a new plot
figure = pyplot.figure()
axes = mplot3d.Axes3D(figure)

# Render the cube faces
for m in meshes:
    axes.add_collection3d(mplot3d.art3d.Poly3DCollection(m.vectors))

# Auto scale to the mesh size
scale = numpy.concatenate([m.points for m in meshes]).flatten(-1)
axes.auto_scale_xyz(scale,scale,scale)

# Show the plot to the screen
pyplot.show()

total_length_data = 0
for i in range(len(meshes)):
    total_length_data += len(meshes[i].data)

data = numpy.zeros(total_length_data,dtype = mesh.Mesh.dtype)
data['vectors'] = np.array(meshes).reshape((-1,9)).reshape((-1,3))
mesh_final = mesh.Mesh(data.copy())
mesh_final.save('whatever_filename')