Simple library to make working with STL files (and 3D objects in general) fast and easy.
Due to all operations heavily relying on numpy this is one of the fastest STL editing libraries for Python available.
- The source: https://github.com/WoLpH/numpy-stl
- Project page: https://pypi.python.org/pypi/numpy-stl
- Reporting bugs: https://github.com/WoLpH/numpy-stl/issues
- Documentation: http://numpy-stl.readthedocs.org/en/latest/
- My blog: http://w.wol.ph/
- numpy any recent version
- python-utils version 1.6 or greater
pip install numpy-stl
- stl2bin your_ascii_stl_file.stl new_binary_stl_file.stl
- stl2ascii your_binary_stl_file.stl new_ascii_stl_file.stl
- stl your_ascii_stl_file.stl new_binary_stl_file.stl
import numpy
from stl import mesh
# Using an existing stl file:
your_mesh = mesh.Mesh.from_file('some_file.stl')
# Or creating a new mesh (make sure not to overwrite the `mesh` import by
# naming it `mesh`):
VERTICE_COUNT = 100
data = numpy.zeros(VERTICE_COUNT, dtype=mesh.Mesh.dtype)
your_mesh = mesh.Mesh(data, remove_empty_areas=False)
# The mesh normals (calculated automatically)
your_mesh.normals
# The mesh vectors
your_mesh.v0, your_mesh.v1, your_mesh.v2
# Accessing individual points (concatenation of v0, v1 and v2 in triplets)
assert (your_mesh.points[0][0:3] == your_mesh.v0[0]).all()
assert (your_mesh.points[0][3:6] == your_mesh.v1[0]).all()
assert (your_mesh.points[0][6:9] == your_mesh.v2[0]).all()
assert (your_mesh.points[1][0:3] == your_mesh.v0[1]).all()
your_mesh.save('new_stl_file.stl')Plotting using matplotlib is equally easy:
from stl import mesh
from mpl_toolkits import mplot3d
from matplotlib import pyplot
# Create a new plot
figure = pyplot.figure()
axes = mplot3d.Axes3D(figure)
# Load the STL files and add the vectors to the plot
your_mesh = mesh.Mesh.from_file('tests/stl_binary/HalfDonut.stl')
axes.add_collection3d(mplot3d.art3d.Poly3DCollection(your_mesh.vectors))
# Auto scale to the mesh size
scale = your_mesh.points.flatten(-1)
axes.auto_scale_xyz(scale, scale, scale)
# Show the plot to the screen
pyplot.show()from stl import mesh
import math
import numpy
# Create 3 faces of a cube
data = numpy.zeros(6, dtype=mesh.Mesh.dtype)
# Top of the cube
data['vectors'][0] = numpy.array([[0, 1, 1],
[1, 0, 1],
[0, 0, 1]])
data['vectors'][1] = numpy.array([[1, 0, 1],
[0, 1, 1],
[1, 1, 1]])
# Right face
data['vectors'][2] = numpy.array([[1, 0, 0],
[1, 0, 1],
[1, 1, 0]])
data['vectors'][3] = numpy.array([[1, 1, 1],
[1, 0, 1],
[1, 1, 0]])
# Left face
data['vectors'][4] = numpy.array([[0, 0, 0],
[1, 0, 0],
[1, 0, 1]])
data['vectors'][5] = numpy.array([[0, 0, 0],
[0, 0, 1],
[1, 0, 1]])
# Since the cube faces are from 0 to 1 we can move it to the middle by
# substracting .5
data['vectors'] -= .5
# Generate 4 different meshes so we can rotate them later
meshes = []
for _ in range(4):
meshes.append(mesh.Mesh(data.copy()))
# Rotate 90 degrees over the Y axis
meshes[0].rotate([0.0, 0.5, 0.0], math.radians(90))
# Translate 2 points over the X axis
meshes[1].x += 2
# Rotate 90 degrees over the X axis
meshes[2].rotate([0.5, 0.0, 0.0], math.radians(90))
# Translate 2 points over the X and Y points
meshes[2].x += 2
meshes[2].y += 2
# Rotate 90 degrees over the X and Y axis
meshes[3].rotate([0.5, 0.0, 0.0], math.radians(90))
meshes[3].rotate([0.0, 0.5, 0.0], math.radians(90))
# Translate 2 points over the Y axis
meshes[3].y += 2
# 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()from stl import mesh
import math
import numpy
# Create 3 faces of a cube
data = numpy.zeros(6, dtype=mesh.Mesh.dtype)
# Top of the cube
data['vectors'][0] = numpy.array([[0, 1, 1],
[1, 0, 1],
[0, 0, 1]])
data['vectors'][1] = numpy.array([[1, 0, 1],
[0, 1, 1],
[1, 1, 1]])
# Right face
data['vectors'][2] = numpy.array([[1, 0, 0],
[1, 0, 1],
[1, 1, 0]])
data['vectors'][3] = numpy.array([[1, 1, 1],
[1, 0, 1],
[1, 1, 0]])
# Left face
data['vectors'][4] = numpy.array([[0, 0, 0],
[1, 0, 0],
[1, 0, 1]])
data['vectors'][5] = numpy.array([[0, 0, 0],
[0, 0, 1],
[1, 0, 1]])
# Since the cube faces are from 0 to 1 we can move it to the middle by
# substracting .5
data['vectors'] -= .5
cube_back = mesh.Mesh(data.copy())
cube_front = mesh.Mesh(data.copy())
# Rotate 90 degrees over the X axis followed by the Y axis followed by the
# X axis
cube_back.rotate([0.5, 0.0, 0.0], math.radians(90))
cube_back.rotate([0.0, 0.5, 0.0], math.radians(90))
cube_back.rotate([0.5, 0.0, 0.0], math.radians(90))
cube = mesh.Mesh(numpy.concatenate([
cube_back.data.copy(),
cube_front.data.copy(),
]))
# 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
axes.add_collection3d(mplot3d.art3d.Poly3DCollection(cube.vectors))
# Auto scale to the mesh size
scale = cube_back.points.flatten(-1)
axes.auto_scale_xyz(scale, scale, scale)
# Show the plot to the screen
pyplot.show()