Import, export, process, analyze and view triangular meshes.
Trimesh is a Python (2.7-3*) library for loading and using triangular meshes. The goal of the library is to provide a fully featured Trimesh object which allows for easy manipulation and analysis, in the style of the excellent Polygon object in the Shapely library.
The API is mostly stable, but this should not be relied on and is not guaranteed; install a specific version if you plan on deploying something using trimesh as a backend.
The easiest and recommended way to get the most functionality out of Trimesh is to install a conda environment, then:
# cyassimp is a fast binding for the assimp importers # note that it installs cleanly in Linux and Windows # but generally fails on OSX conda install -c menpo/label/master cyassimp # install modules for spatial indexing, polygon manipulation, and fast ray-mesh queries # these generally install cleanly on Linux, Windows, and OSX conda install -c conda-forge rtree shapely # install Trimesh and soft dependencies that are easy to install # these generally install cleanly on Linux, Windows, and OSX pip install trimesh[easy]
Or, for only minimal dependencies (no ray queries, vector path handling, mesh creation, viewer, etc):
pip install trimesh
Here is an example of loading a mesh from file and colorizing its faces. Here is a nicely formatted ipython notebook version of this example. Also check out the cross section example or possibly the integration of a function over a mesh example.
import numpy as np import trimesh # load a file by name or from a buffer mesh = trimesh.load_mesh('../models/featuretype.STL') # is the current mesh watertight? mesh.is_watertight # what's the euler number for the mesh? mesh.euler_number # the convex hull is another Trimesh object that is available as a property # lets compare the volume of our mesh with the volume of its convex hull np.divide(mesh.volume, mesh.convex_hull.volume) # since the mesh is watertight, it means there is a # volumetric center of mass which we can set as the origin for our mesh mesh.vertices -= mesh.center_mass # what's the moment of inertia for the mesh? mesh.moment_inertia # if there are multiple bodies in the mesh we can split the mesh by # connected components of face adjacency # since this example mesh is a single watertight body we get a list of one mesh mesh.split() # facets are groups of coplanar adjacent faces # set each facet to a random color # colors are 8 bit RGBA by default (n,4) np.uint8 for facet in mesh.facets: mesh.visual.face_colors[facet] = trimesh.visual.random_color() # preview mesh in an opengl window if you installed pyglet with pip mesh.show() # transform method can be passed a (4,4) matrix and will cleanly apply the transform mesh.apply_transform(trimesh.transformations.random_rotation_matrix()) # axis aligned bounding box is available mesh.bounding_box.extents # a minimum volume oriented bounding box also available # primitives are subclasses of Trimesh objects which automatically generate # faces and vertices from data stored in the 'primitive' attribute mesh.bounding_box_oriented.primitive.extents mesh.bounding_box_oriented.primitive.transform # show the mesh appended with its oriented bounding box # the bounding box is a trimesh.primitives.Box object, which subclasses # Trimesh and lazily evaluates to fill in vertices and faces when requested # (press w in viewer to see triangles) (mesh + mesh.bounding_box_oriented).show() # bounding spheres and bounding cylinders of meshes are also # available, and will be the minimum volume version of each # except in certain degenerate cases, where they will be no worse # than a least squares fit version of the primitive. print(mesh.bounding_box_oriented.volume, mesh.bounding_cylinder.volume, mesh.bounding_sphere.volume)
Trimesh includes an optional pyglet- based viewer for debugging/inspecting. In the mesh view window:
If you want to deploy something in a container that uses trimesh, automated builds containing trimesh and its dependencies are available on docker hub. For an image with all dependencies:
docker pull mikedh/trimesh
Or, for a much smaller image with no boolean operations and slightly slower graph operations (no graph-tool installed, trimesh will fall back to scipy or networkx):
docker pull mikedh/trimesh_minimal