"""
github.com/mikedh/trimesh
----------------------------
Library for importing, exporting and doing simple operations on triangular meshes.
"""
import copy
import warnings
import numpy as np
from numpy import float64, int64, ndarray
from . import (
boolean,
caching,
comparison,
convex,
curvature,
decomposition,
geometry,
graph,
grouping,
inertia,
intersections,
permutate,
poses,
proximity,
ray,
registration,
remesh,
repair,
sample,
transformations,
triangles,
units,
util,
)
from .caching import TrackedArray
from .constants import log, tol
from .exceptions import ExceptionWrapper
from .exchange.export import export_mesh
from .parent import Geometry3D
from .scene import Scene
from .triangles import MassProperties
from .typed import Any, ArrayLike, Dict, List, NDArray, Number, Optional, Sequence, Union
from .visual import ColorVisuals, TextureVisuals, create_visual
try:
from scipy.sparse import coo_matrix
from scipy.spatial import cKDTree
except BaseException as E:
cKDTree = ExceptionWrapper(E)
coo_matrix = ExceptionWrapper(E)
try:
from networkx import Graph
except BaseException as E:
Graph = ExceptionWrapper(E)
try:
from rtree.index import Index
except BaseException as E:
Index = ExceptionWrapper(E)
try:
from .path import Path2D, Path3D
except BaseException as E:
Path2D = ExceptionWrapper(E)
Path3D = ExceptionWrapper(E)
[docs]
class Trimesh(Geometry3D):
[docs]
def __init__(
self,
vertices: Optional[ArrayLike] = None,
faces: Optional[ArrayLike] = None,
face_normals: Optional[ArrayLike] = None,
vertex_normals: Optional[ArrayLike] = None,
face_colors: Optional[ArrayLike] = None,
vertex_colors: Optional[ArrayLike] = None,
face_attributes: Optional[Dict[str, ArrayLike]] = None,
vertex_attributes: Optional[Dict[str, ArrayLike]] = None,
metadata: Optional[Dict[str, Any]] = None,
process: bool = True,
validate: bool = False,
merge_tex: Optional[bool] = None,
merge_norm: Optional[bool] = None,
use_embree: bool = True,
initial_cache: Optional[Dict[str, ndarray]] = None,
visual: Optional[Union[ColorVisuals, TextureVisuals]] = None,
**kwargs,
) -> None:
"""
A Trimesh object contains a triangular 3D mesh.
Parameters
------------
vertices : (n, 3) float
Array of vertex locations
faces : (m, 3) or (m, 4) int
Array of triangular or quad faces (triangulated on load)
face_normals : (m, 3) float
Array of normal vectors corresponding to faces
vertex_normals : (n, 3) float
Array of normal vectors for vertices
metadata : dict
Any metadata about the mesh
process : bool
if True, Nan and Inf values will be removed
immediately and vertices will be merged
validate : bool
If True, degenerate and duplicate faces will be
removed immediately, and some functions will alter
the mesh to ensure consistent results.
use_embree : bool
If True try to use pyembree raytracer.
If pyembree is not available it will automatically fall
back to a much slower rtree/numpy implementation
initial_cache : dict
A way to pass things to the cache in case expensive
things were calculated before creating the mesh object.
visual : ColorVisuals or TextureVisuals
Assigned to self.visual
"""
# self._data stores information about the mesh which
# CANNOT be regenerated.
# in the base class all that is stored here is vertex and
# face information
# any data put into the store is converted to a TrackedArray
# which is a subclass of np.ndarray that provides hash and crc
# methods which can be used to detect changes in the array.
self._data = caching.DataStore()
# self._cache stores information about the mesh which CAN be
# regenerated from self._data, but may be slow to calculate.
# In order to maintain consistency
# the cache is cleared when self._data.__hash__() changes
self._cache = caching.Cache(id_function=self._data.__hash__, force_immutable=True)
if initial_cache is not None:
self._cache.update(initial_cache)
# check for None only to avoid warning messages in subclasses
# (n, 3) float array of vertices
self.vertices = vertices
# (m, 3) int of triangle faces that references self.vertices
self.faces = faces
# hold visual information about the mesh (vertex and face colors)
if visual is None:
self.visual = create_visual(
face_colors=face_colors, vertex_colors=vertex_colors, mesh=self
)
else:
self.visual = visual
# normals are accessed through setters/properties and are regenerated
# if dimensions are inconsistent, but can be set by the constructor
# to avoid a substantial number of cross products
if face_normals is not None:
self.face_normals = face_normals
# (n, 3) float of vertex normals, can be created from face normals
if vertex_normals is not None:
self.vertex_normals = vertex_normals
# embree is a much, much faster raytracer written by Intel
# if you have pyembree installed you should use it
# although both raytracers were designed to have a common API
if ray.has_embree and use_embree:
self.ray = ray.ray_pyembree.RayMeshIntersector(self)
else:
# create a ray-mesh query object for the current mesh
# initializing is very inexpensive and object is convenient to have.
# On first query expensive bookkeeping is done (creation of r-tree),
# and is cached for subsequent queries
self.ray = ray.ray_triangle.RayMeshIntersector(self)
# a quick way to get permuted versions of the current mesh
self.permutate = permutate.Permutator(self)
# convenience class for nearest point queries
self.nearest = proximity.ProximityQuery(self)
# update the mesh metadata with passed metadata
self.metadata = {}
if isinstance(metadata, dict):
self.metadata.update(metadata)
elif metadata is not None:
raise ValueError(f"metadata should be a dict or None, got {metadata!s}")
# store per-face and per-vertex attributes which will
# be updated when an update_faces call is made
self.face_attributes = {}
self.vertex_attributes = {}
# use update to copy items
if face_attributes is not None:
self.face_attributes.update(face_attributes)
if vertex_attributes is not None:
self.vertex_attributes.update(vertex_attributes)
# process will remove NaN and Inf values and merge vertices
# if validate, will remove degenerate and duplicate faces
if process or validate:
self.process(validate=validate, merge_tex=merge_tex, merge_norm=merge_norm)
[docs]
def process(
self,
validate: bool = False,
merge_tex: Optional[bool] = None,
merge_norm: Optional[bool] = None,
) -> "Trimesh":
"""
Do processing to make a mesh useful.
Does this by:
1) removing NaN and Inf values
2) merging duplicate vertices
If validate:
3) Remove triangles which have one edge
of their 2D oriented bounding box
shorter than tol.merge
4) remove duplicated triangles
5) Attempt to ensure triangles are consistently wound
and normals face outwards.
Parameters
------------
validate : bool
Remove degenerate and duplicate faces.
Returns
------------
self: trimesh.Trimesh
Current mesh
"""
# if there are no vertices or faces exit early
if self.is_empty:
return self
# avoid clearing the cache during operations
with self._cache:
# if we're cleaning remove duplicate
# and degenerate faces
if validate:
# get a mask with only unique and non-degenerate faces
mask = self.unique_faces() & self.nondegenerate_faces()
self.update_faces(mask)
self.fix_normals()
# since none of our process operations moved vertices or faces
# we can keep face and vertex normals in the cache without recomputing
# if faces or vertices have been removed, normals are validated before
# being returned so there is no danger of inconsistent dimensions
self.remove_infinite_values()
self.merge_vertices(merge_tex=merge_tex, merge_norm=merge_norm)
self._cache.clear(exclude={"face_normals", "vertex_normals"})
self.metadata["processed"] = True
return self
@property
def mutable(self) -> bool:
"""
Is the current mesh allowed to be altered in-place?
Returns
-------------
mutable
If data is allowed to be set for the mesh.
"""
return self._data.mutable
@mutable.setter
def mutable(self, value: bool) -> None:
"""
Set the mutability of the current mesh.
Parameters
----------
value
Change whether the current mesh is allowed to be altered in-place.
"""
self._data.mutable = value
@property
def faces(self) -> TrackedArray:
"""
The faces of the mesh.
This is regarded as core information which cannot be
regenerated from cache and as such is stored in
`self._data` which tracks the array for changes and
clears cached values of the mesh altered.
Returns
----------
faces : (n, 3) int64
References for `self.vertices` for triangles.
"""
return self._data["faces"]
@faces.setter
def faces(self, values: Optional[ArrayLike]) -> None:
"""
Set the vertex indexes that make up triangular faces.
Parameters
--------------
values : (n, 3) int64
Indexes of self.vertices
"""
if values is None:
# if passed none store an empty array
values = np.empty(shape=(0, 3), dtype=int64)
else:
values = np.asanyarray(values, dtype=int64)
# automatically triangulate quad faces
if len(values.shape) == 2 and values.shape[1] != 3:
log.info("triangulating faces")
values = geometry.triangulate_quads(values)
self._data["faces"] = values
@caching.cache_decorator
def faces_sparse(self) -> coo_matrix:
"""
A sparse matrix representation of the faces.
Returns
----------
sparse : scipy.sparse.coo_matrix
Has properties:
dtype : bool
shape : (len(self.vertices), len(self.faces))
"""
return geometry.index_sparse(columns=len(self.vertices), indices=self.faces)
@property
def face_normals(self) -> NDArray[float64]:
"""
Return the unit normal vector for each face.
If a face is degenerate and a normal can't be generated
a zero magnitude unit vector will be returned for that face.
Returns
-----------
normals : (len(self.faces), 3) float64
Normal vectors of each face
"""
# check shape of cached normals
cached = self._cache["face_normals"]
# get faces from datastore
if "faces" in self._data:
faces = self._data.data["faces"]
else:
faces = None
# if we have no faces exit early
if faces is None or len(faces) == 0:
return np.array([], dtype=float64).reshape((0, 3))
# if the shape of cached normals equals the shape of faces return
if np.shape(cached) == np.shape(faces):
return cached
# use cached triangle cross products to generate normals
# this will always return the correct shape but some values
# will be zero or an arbitrary vector if the inputs had
# a cross product below machine epsilon
normals, valid = triangles.normals(
triangles=self.triangles, crosses=self.triangles_cross
)
# if all triangles are valid shape is correct
if valid.all():
# put calculated face normals into cache manually
self._cache["face_normals"] = normals
return normals
# make a padded list of normals for correct shape
padded = np.zeros((len(self.triangles), 3), dtype=float64)
padded[valid] = normals
# put calculated face normals into cache manually
self._cache["face_normals"] = padded
return padded
@face_normals.setter
def face_normals(self, values: Optional[ArrayLike]) -> None:
"""
Assign values to face normals.
Parameters
-------------
values : (len(self.faces), 3) float
Unit face normals. If None will clear existing normals.
"""
# if nothing passed exit
if values is None:
return
# make sure candidate face normals are C-contiguous float
values = np.asanyarray(values, order="C", dtype=float64)
# face normals need to correspond to faces
if len(values) == 0 or values.shape != self.faces.shape:
log.debug("face_normals incorrect shape, ignoring!")
return
# check if any values are larger than tol.merge
# don't set the normals if they are all zero
ptp = values.ptp()
if not np.isfinite(ptp):
log.debug("face_normals contain NaN, ignoring!")
return
if ptp < tol.merge:
log.debug("face_normals all zero, ignoring!")
return
# make sure the first few normals match the first few triangles
check, valid = triangles.normals(self.vertices.view(np.ndarray)[self.faces[:20]])
compare = np.zeros((len(valid), 3))
compare[valid] = check
if not np.allclose(compare, values[:20]):
log.debug("face_normals didn't match triangles, ignoring!")
return
# otherwise store face normals
self._cache["face_normals"] = values
@property
def vertices(self) -> TrackedArray:
"""
The vertices of the mesh.
This is regarded as core information which cannot be
generated from cache and as such is stored in self._data
which tracks the array for changes and clears cached
values of the mesh if this is altered.
Returns
----------
vertices : (n, 3) float
Points in cartesian space referenced by self.faces
"""
# get vertices if already stored
return self._data["vertices"]
@vertices.setter
def vertices(self, values: Optional[ArrayLike]):
"""
Assign vertex values to the mesh.
Parameters
--------------
values : (n, 3) float
Points in space
"""
if values is None:
# remove any stored data and store an empty array
values = np.empty(shape=(0, 3), dtype=float64)
self._data["vertices"] = np.asanyarray(values, order="C", dtype=float64)
@caching.cache_decorator
def vertex_normals(self) -> NDArray[float64]:
"""
The vertex normals of the mesh. If the normals were loaded
we check to make sure we have the same number of vertex
normals and vertices before returning them. If there are
no vertex normals defined or a shape mismatch we calculate
the vertex normals from the mean normals of the faces the
vertex is used in.
Returns
----------
vertex_normals : (n, 3) float
Represents the surface normal at each vertex.
Where n == len(self.vertices)
"""
# make sure we have faces_sparse
assert hasattr(self.faces_sparse, "dot")
return geometry.weighted_vertex_normals(
vertex_count=len(self.vertices),
faces=self.faces,
face_normals=self.face_normals,
face_angles=self.face_angles,
)
@vertex_normals.setter
def vertex_normals(self, values: ArrayLike) -> None:
"""
Assign values to vertex normals.
Parameters
-------------
values : (len(self.vertices), 3) float
Unit normal vectors for each vertex
"""
if values is not None:
values = np.asanyarray(values, order="C", dtype=float64)
if values.shape == self.vertices.shape:
# check to see if they assigned all zeros
if values.ptp() < tol.merge:
log.debug("vertex_normals are all zero!")
self._cache["vertex_normals"] = values
@caching.cache_decorator
def vertex_faces(self) -> NDArray[int64]:
"""
A representation of the face indices that correspond to each vertex.
Returns
----------
vertex_faces : (n,m) int
Each row contains the face indices that correspond to the given vertex,
padded with -1 up to the max number of faces corresponding to any one vertex
Where n == len(self.vertices), m == max number of faces for a single vertex
"""
vertex_faces = geometry.vertex_face_indices(
vertex_count=len(self.vertices),
faces=self.faces,
faces_sparse=self.faces_sparse,
)
return vertex_faces
@caching.cache_decorator
def bounds(self) -> Optional[NDArray[float64]]:
"""
The axis aligned bounds of the faces of the mesh.
Returns
-----------
bounds : (2, 3) float or None
Bounding box with [min, max] coordinates
If mesh is empty will return None
"""
# return bounds including ONLY referenced vertices
in_mesh = self.vertices[self.referenced_vertices]
# don't crash if we have no vertices referenced
if len(in_mesh) == 0:
return None
# get mesh bounds with min and max
return np.array([in_mesh.min(axis=0), in_mesh.max(axis=0)])
@caching.cache_decorator
def extents(self) -> Optional[NDArray[float64]]:
"""
The length, width, and height of the axis aligned
bounding box of the mesh.
Returns
-----------
extents : (3, ) float or None
Array containing axis aligned [length, width, height]
If mesh is empty returns None
"""
# if mesh is empty return None
if self.bounds is None:
return None
extents = self.bounds.ptp(axis=0)
return extents
@caching.cache_decorator
def centroid(self) -> NDArray[float64]:
"""
The point in space which is the average of the triangle
centroids weighted by the area of each triangle.
This will be valid even for non-watertight meshes,
unlike self.center_mass
Returns
----------
centroid : (3, ) float
The average vertex weighted by face area
"""
# use the centroid of each triangle weighted by
# the area of the triangle to find the overall centroid
try:
centroid = np.average(self.triangles_center, weights=self.area_faces, axis=0)
except BaseException:
# if all triangles are zero-area weights will not work
centroid = self.triangles_center.mean(axis=0)
return centroid
@property
def center_mass(self) -> NDArray[float64]:
"""
The point in space which is the center of mass/volume.
Returns
-----------
center_mass : (3, ) float
Volumetric center of mass of the mesh.
"""
return self.mass_properties.center_mass
@center_mass.setter
def center_mass(self, value: ArrayLike) -> None:
"""
Override the point in space which is the center of mass and volume.
Parameters
-----------
center_mass : (3, ) float
Volumetric center of mass of the mesh.
"""
value = np.array(value, dtype=float64)
if value.shape != (3,):
raise ValueError("shape must be (3,) float!")
self._data["center_mass"] = value
self._cache.delete("mass_properties")
@property
def density(self) -> float:
"""
The density of the mesh used in inertia calculations.
Returns
-----------
density
The density of the primitive.
"""
return float(self.mass_properties.density)
@density.setter
def density(self, value: Number) -> None:
"""
Set the density of the primitive.
Parameters
-------------
density
Specify the density of the primitive to be
used in inertia calculations.
"""
self._data["density"] = float(value)
self._cache.delete("mass_properties")
@property
def volume(self) -> float64:
"""
Volume of the current mesh calculated using a surface
integral. If the current mesh isn't watertight this is
garbage.
Returns
---------
volume : float
Volume of the current mesh
"""
return self.mass_properties.volume
@property
def mass(self) -> float64:
"""
Mass of the current mesh, based on specified density and
volume. If the current mesh isn't watertight this is garbage.
Returns
---------
mass : float
Mass of the current mesh
"""
return self.mass_properties.mass
@property
def moment_inertia(self) -> NDArray[float64]:
"""
Return the moment of inertia matrix of the current mesh.
If mesh isn't watertight this is garbage. The returned
moment of inertia is *axis aligned* at the mesh's center
of mass `mesh.center_mass`. If you want the moment at any
other frame including the origin call:
`mesh.moment_inertia_frame`
Returns
---------
inertia : (3, 3) float
Moment of inertia of the current mesh at the center of
mass and aligned with the cartesian axis.
"""
return self.mass_properties.inertia
[docs]
def moment_inertia_frame(self, transform: ArrayLike) -> NDArray[float64]:
"""
Get the moment of inertia of this mesh with respect to
an arbitrary frame, versus with respect to the center
of mass as returned by `mesh.moment_inertia`.
For example if `transform` is an identity matrix `np.eye(4)`
this will give the moment at the origin.
Uses the parallel axis theorum to move the center mass
tensor to this arbitrary frame.
Parameters
------------
transform : (4, 4) float
Homogeneous transformation matrix.
Returns
-------------
inertia : (3, 3)
Moment of inertia in the requested frame.
"""
# we'll need the inertia tensor and the center of mass
props = self.mass_properties
# calculated moment of inertia is at the center of mass
# so we want to offset our requested translation by that
# center of mass
offset = np.eye(4)
offset[:3, 3] = -props["center_mass"]
# apply the parallel axis theorum to get the new inertia
return inertia.transform_inertia(
inertia_tensor=props["inertia"],
transform=np.dot(offset, transform),
mass=props["mass"],
parallel_axis=True,
)
@caching.cache_decorator
def principal_inertia_components(self) -> NDArray[float64]:
"""
Return the principal components of inertia
Ordering corresponds to mesh.principal_inertia_vectors
Returns
----------
components : (3, ) float
Principal components of inertia
"""
# both components and vectors from inertia matrix
components, vectors = inertia.principal_axis(self.moment_inertia)
# store vectors in cache for later
self._cache["principal_inertia_vectors"] = vectors
return components
@property
def principal_inertia_vectors(self) -> NDArray[float64]:
"""
Return the principal axis of inertia as unit vectors.
The order corresponds to `mesh.principal_inertia_components`.
Returns
----------
vectors : (3, 3) float
Three vectors pointing along the
principal axis of inertia directions
"""
_ = self.principal_inertia_components
return self._cache["principal_inertia_vectors"]
@caching.cache_decorator
def principal_inertia_transform(self) -> NDArray[float64]:
"""
A transform which moves the current mesh so the principal
inertia vectors are on the X,Y, and Z axis, and the centroid is
at the origin.
Returns
----------
transform : (4, 4) float
Homogeneous transformation matrix
"""
order = np.argsort(self.principal_inertia_components)[1:][::-1]
vectors = self.principal_inertia_vectors[order]
vectors = np.vstack((vectors, np.cross(*vectors)))
transform = np.eye(4)
transform[:3, :3] = vectors
transform = transformations.transform_around(
matrix=transform, point=self.centroid
)
transform[:3, 3] -= self.centroid
return transform
@caching.cache_decorator
def symmetry(self) -> Optional[str]:
"""
Check whether a mesh has rotational symmetry around
an axis (radial) or point (spherical).
Returns
-----------
symmetry : None, 'radial', 'spherical'
What kind of symmetry does the mesh have.
"""
symmetry, axis, section = inertia.radial_symmetry(self)
self._cache["symmetry_axis"] = axis
self._cache["symmetry_section"] = section
return symmetry
@property
def symmetry_axis(self) -> Optional[NDArray[float64]]:
"""
If a mesh has rotational symmetry, return the axis.
Returns
------------
axis : (3, ) float
Axis around which a 2D profile was revolved to create this mesh.
"""
if self.symmetry is None:
return None
return self._cache["symmetry_axis"]
@property
def symmetry_section(self) -> Optional[NDArray[float64]]:
"""
If a mesh has rotational symmetry return the two
vectors which make up a section coordinate frame.
Returns
----------
section : (2, 3) float
Vectors to take a section along
"""
if self.symmetry is None:
return None
return self._cache["symmetry_section"]
@caching.cache_decorator
def triangles(self) -> NDArray[float64]:
"""
Actual triangles of the mesh (points, not indexes)
Returns
---------
triangles : (n, 3, 3) float
Points of triangle vertices
"""
# use of advanced indexing on our tracked arrays will
# trigger a change flag which means the hash will have to be
# recomputed. We can escape this check by viewing the array.
return self.vertices.view(np.ndarray)[self.faces]
@caching.cache_decorator
def triangles_tree(self) -> Index:
"""
An R-tree containing each face of the mesh.
Returns
----------
tree : rtree.index
Each triangle in self.faces has a rectangular cell
"""
return triangles.bounds_tree(self.triangles)
@caching.cache_decorator
def triangles_center(self) -> NDArray[float64]:
"""
The center of each triangle (barycentric [1/3, 1/3, 1/3])
Returns
---------
triangles_center : (len(self.faces), 3) float
Center of each triangular face
"""
return self.triangles.mean(axis=1)
@caching.cache_decorator
def triangles_cross(self) -> NDArray[float64]:
"""
The cross product of two edges of each triangle.
Returns
---------
crosses : (n, 3) float
Cross product of each triangle
"""
crosses = triangles.cross(self.triangles)
return crosses
@caching.cache_decorator
def edges(self) -> NDArray[int64]:
"""
Edges of the mesh (derived from faces).
Returns
---------
edges : (n, 2) int
List of vertex indices making up edges
"""
edges, index = geometry.faces_to_edges(
self.faces.view(np.ndarray), return_index=True
)
self._cache["edges_face"] = index
return edges
@caching.cache_decorator
def edges_face(self) -> NDArray[int64]:
"""
Which face does each edge belong to.
Returns
---------
edges_face : (n, ) int
Index of self.faces
"""
_ = self.edges
return self._cache["edges_face"]
@caching.cache_decorator
def edges_unique(self) -> NDArray[int64]:
"""
The unique edges of the mesh.
Returns
----------
edges_unique : (n, 2) int
Vertex indices for unique edges
"""
unique, inverse = grouping.unique_rows(self.edges_sorted)
edges_unique = self.edges_sorted[unique]
# edges_unique will be added automatically by the decorator
# additional terms generated need to be added to the cache manually
self._cache["edges_unique_idx"] = unique
self._cache["edges_unique_inverse"] = inverse
return edges_unique
@caching.cache_decorator
def edges_unique_length(self) -> NDArray[float64]:
"""
How long is each unique edge.
Returns
----------
length : (len(self.edges_unique), ) float
Length of each unique edge
"""
vector = np.subtract(*self.vertices[self.edges_unique.T])
length = util.row_norm(vector)
return length
@caching.cache_decorator
def edges_unique_inverse(self) -> NDArray[int64]:
"""
Return the inverse required to reproduce
self.edges_sorted from self.edges_unique.
Useful for referencing edge properties:
mesh.edges_unique[mesh.edges_unique_inverse] == m.edges_sorted
Returns
----------
inverse : (len(self.edges), ) int
Indexes of self.edges_unique
"""
_ = self.edges_unique
return self._cache["edges_unique_inverse"]
@caching.cache_decorator
def edges_sorted(self) -> NDArray[int64]:
"""
Edges sorted along axis 1
Returns
----------
edges_sorted : (n, 2)
Same as self.edges but sorted along axis 1
"""
edges_sorted = np.sort(self.edges, axis=1)
return edges_sorted
@caching.cache_decorator
def edges_sorted_tree(self) -> cKDTree:
"""
A KDTree for mapping edges back to edge index.
Returns
------------
tree : scipy.spatial.cKDTree
Tree when queried with edges will return
their index in mesh.edges_sorted
"""
return cKDTree(self.edges_sorted)
@caching.cache_decorator
def edges_sparse(self) -> coo_matrix:
"""
Edges in sparse bool COO graph format where connected
vertices are True.
Returns
----------
sparse: (len(self.vertices), len(self.vertices)) bool
Sparse graph in COO format
"""
sparse = graph.edges_to_coo(self.edges, count=len(self.vertices))
return sparse
@caching.cache_decorator
def body_count(self) -> int:
"""
How many connected groups of vertices exist in this mesh.
Note that this number may differ from result in mesh.split,
which is calculated from FACE rather than vertex adjacency.
Returns
-----------
count : int
Number of connected vertex groups
"""
# labels are (len(vertices), int) OB
count, labels = graph.csgraph.connected_components(
self.edges_sparse, directed=False, return_labels=True
)
self._cache["vertices_component_label"] = labels
return count
@caching.cache_decorator
def faces_unique_edges(self) -> NDArray[int64]:
"""
For each face return which indexes in mesh.unique_edges constructs
that face.
Returns
---------
faces_unique_edges : (len(self.faces), 3) int
Indexes of self.edges_unique that
construct self.faces
Examples
---------
In [0]: mesh.faces[:2]
Out[0]:
TrackedArray([[ 1, 6946, 24224],
[ 6946, 1727, 24225]])
In [1]: mesh.edges_unique[mesh.faces_unique_edges[:2]]
Out[1]:
array([[[ 1, 6946],
[ 6946, 24224],
[ 1, 24224]],
[[ 1727, 6946],
[ 1727, 24225],
[ 6946, 24225]]])
"""
# make sure we have populated unique edges
_ = self.edges_unique
# we are relying on the fact that edges are stacked in triplets
result = self._cache["edges_unique_inverse"].reshape((-1, 3))
return result
@caching.cache_decorator
def euler_number(self) -> int:
"""
Return the Euler characteristic (a topological invariant) for the mesh
In order to guarantee correctness, this should be called after
remove_unreferenced_vertices
Returns
----------
euler_number : int
Topological invariant
"""
return int(
self.referenced_vertices.sum() - len(self.edges_unique) + len(self.faces)
)
@caching.cache_decorator
def referenced_vertices(self) -> NDArray[np.bool_]:
"""
Which vertices in the current mesh are referenced by a face.
Returns
-------------
referenced : (len(self.vertices), ) bool
Which vertices are referenced by a face
"""
referenced = np.zeros(len(self.vertices), dtype=bool)
referenced[self.faces] = True
return referenced
[docs]
def convert_units(self, desired: str, guess: bool = False) -> "Trimesh":
"""
Convert the units of the mesh into a specified unit.
Parameters
------------
desired : string
Units to convert to (eg 'inches')
guess : boolean
If self.units are not defined should we
guess the current units of the document and then convert?
"""
units._convert_units(self, desired, guess)
return self
[docs]
def merge_vertices(
self,
merge_tex: Optional[bool] = None,
merge_norm: Optional[bool] = None,
digits_vertex: Optional[bool] = None,
digits_norm: Optional[bool] = None,
digits_uv: Optional[bool] = None,
) -> None:
"""
Removes duplicate vertices grouped by position and
optionally texture coordinate and normal.
Parameters
-------------
mesh : Trimesh object
Mesh to merge vertices on
merge_tex : bool
If True textured meshes with UV coordinates will
have vertices merged regardless of UV coordinates
merge_norm : bool
If True, meshes with vertex normals will have
vertices merged ignoring different normals
digits_vertex : None or int
Number of digits to consider for vertex position
digits_norm : int
Number of digits to consider for unit normals
digits_uv : int
Number of digits to consider for UV coordinates
"""
grouping.merge_vertices(
mesh=self,
merge_tex=merge_tex,
merge_norm=merge_norm,
digits_vertex=digits_vertex,
digits_norm=digits_norm,
digits_uv=digits_uv,
)
[docs]
def update_vertices(
self,
mask: ArrayLike,
inverse: Optional[ArrayLike] = None,
) -> None:
"""
Update vertices with a mask.
Parameters
------------
vertex_mask : (len(self.vertices)) bool
Array of which vertices to keep
inverse : (len(self.vertices)) int
Array to reconstruct vertex references
such as output by np.unique
"""
# if the mesh is already empty we can't remove anything
if self.is_empty:
return
# make sure mask is a numpy array
mask = np.asanyarray(mask)
if (mask.dtype.name == "bool" and mask.all()) or len(mask) == 0 or self.is_empty:
# mask doesn't remove any vertices so exit early
return
# create the inverse mask if not passed
if inverse is None:
inverse = np.zeros(len(self.vertices), dtype=int64)
if mask.dtype.kind == "b":
inverse[mask] = np.arange(mask.sum())
elif mask.dtype.kind == "i":
inverse[mask] = np.arange(len(mask))
else:
inverse = None
# re-index faces from inverse
if inverse is not None and util.is_shape(self.faces, (-1, 3)):
self.faces = inverse[self.faces.reshape(-1)].reshape((-1, 3))
# update the visual object with our mask
self.visual.update_vertices(mask)
# get the normals from cache before dumping
cached_normals = self._cache["vertex_normals"]
# apply to face_attributes
count = len(self.vertices)
for key, value in self.vertex_attributes.items():
try:
# covers un-len'd objects as well
if len(value) != count:
raise TypeError()
except TypeError:
continue
# apply the mask to the attribute
self.vertex_attributes[key] = value[mask]
# actually apply the mask
self.vertices = self.vertices[mask]
# if we had passed vertex normals try to save them
if util.is_shape(cached_normals, (-1, 3)):
try:
self.vertex_normals = cached_normals[mask]
except BaseException:
pass
[docs]
def update_faces(self, mask: ArrayLike) -> None:
"""
In many cases, we will want to remove specific faces.
However, there is additional bookkeeping to do this cleanly.
This function updates the set of faces with a validity mask,
as well as keeping track of normals and colors.
Parameters
------------
valid : (m) int or (len(self.faces)) bool
Mask to remove faces
"""
# if the mesh is already empty we can't remove anything
if self.is_empty:
return
mask = np.asanyarray(mask)
if mask.dtype.name == "bool" and mask.all():
# mask removes no faces so exit early
return
# try to save face normals before dumping cache
cached_normals = self._cache["face_normals"]
faces = self._data["faces"]
# if Trimesh has been subclassed and faces have been moved
# from data to cache, get faces from cache.
if not util.is_shape(faces, (-1, 3)):
faces = self._cache["faces"]
# apply to face_attributes
count = len(self.faces)
for key, value in self.face_attributes.items():
try:
# covers un-len'd objects as well
if len(value) != count:
raise TypeError()
except TypeError:
continue
# apply the mask to the attribute
self.face_attributes[key] = value[mask]
# actually apply the mask
self.faces = faces[mask]
# apply to face colors
self.visual.update_faces(mask)
# if our normals were the correct shape apply them
if util.is_shape(cached_normals, (-1, 3)):
self.face_normals = cached_normals[mask]
[docs]
def remove_infinite_values(self) -> None:
"""
Ensure that every vertex and face consists of finite numbers.
This will remove vertices or faces containing np.nan and np.inf
Alters `self.faces` and `self.vertices`
"""
if util.is_shape(self.faces, (-1, 3)):
# (len(self.faces), ) bool, mask for faces
face_mask = np.isfinite(self.faces).all(axis=1)
self.update_faces(face_mask)
if util.is_shape(self.vertices, (-1, 3)):
# (len(self.vertices), ) bool, mask for vertices
vertex_mask = np.isfinite(self.vertices).all(axis=1)
self.update_vertices(vertex_mask)
[docs]
def unique_faces(self) -> NDArray[np.bool_]:
"""
On the current mesh find which faces are unique.
Returns
--------
unique : (len(faces),) bool
A mask where the first occurrence of a unique face is true.
"""
mask = np.zeros(len(self.faces), dtype=bool)
mask[grouping.unique_rows(np.sort(self.faces, axis=1))[0]] = True
return mask
[docs]
def remove_duplicate_faces(self) -> None:
"""
DERECATED MARCH 2024 REPLACE WITH:
`mesh.update_faces(mesh.unique_faces())`
"""
warnings.warn(
"`remove_duplicate_faces` is deprecated "
+ "and will be removed in March 2024: "
+ "replace with `mesh.update_faces(mesh.unique_faces())`",
category=DeprecationWarning,
stacklevel=2,
)
self.update_faces(self.unique_faces())
[docs]
def rezero(self) -> None:
"""
Translate the mesh so that all vertex vertices are positive.
Alters `self.vertices`.
"""
self.apply_translation(self.bounds[0] * -1.0)
[docs]
def split(self, **kwargs) -> List["Trimesh"]:
"""
Returns a list of Trimesh objects, based on face connectivity.
Splits into individual components, sometimes referred to as 'bodies'
Parameters
------------
only_watertight : bool
Only return watertight meshes and discard remainder
adjacency : None or (n, 2) int
Override face adjacency with custom values
Returns
---------
meshes : (n, ) trimesh.Trimesh
Separate bodies from original mesh
"""
return graph.split(self, **kwargs)
@caching.cache_decorator
def face_adjacency(self) -> NDArray[int64]:
"""
Find faces that share an edge i.e. 'adjacent' faces.
Returns
----------
adjacency : (n, 2) int
Pairs of faces which share an edge
Examples
---------
In [1]: mesh = trimesh.load('models/featuretype.STL')
In [2]: mesh.face_adjacency
Out[2]:
array([[ 0, 1],
[ 2, 3],
[ 0, 3],
...,
[1112, 949],
[3467, 3475],
[1113, 3475]])
In [3]: mesh.faces[mesh.face_adjacency[0]]
Out[3]:
TrackedArray([[ 1, 0, 408],
[1239, 0, 1]], dtype=int64)
In [4]: import networkx as nx
In [5]: graph = nx.from_edgelist(mesh.face_adjacency)
In [6]: groups = nx.connected_components(graph)
"""
adjacency, edges = graph.face_adjacency(mesh=self, return_edges=True)
self._cache["face_adjacency_edges"] = edges
return adjacency
@caching.cache_decorator
def face_neighborhood(self) -> NDArray[int64]:
"""
Find faces that share a vertex i.e. 'neighbors' faces.
Returns
----------
neighborhood : (n, 2) int
Pairs of faces which share a vertex
"""
return graph.face_neighborhood(self)
@caching.cache_decorator
def face_adjacency_edges(self) -> NDArray[int64]:
"""
Returns the edges that are shared by the adjacent faces.
Returns
--------
edges : (n, 2) int
Vertex indices which correspond to face_adjacency
"""
# this value is calculated as a byproduct of the face adjacency
_ = self.face_adjacency
return self._cache["face_adjacency_edges"]
@caching.cache_decorator
def face_adjacency_edges_tree(self) -> cKDTree:
"""
A KDTree for mapping edges back face adjacency index.
Returns
------------
tree : scipy.spatial.cKDTree
Tree when queried with SORTED edges will return
their index in mesh.face_adjacency
"""
return cKDTree(self.face_adjacency_edges)
@caching.cache_decorator
def face_adjacency_angles(self) -> NDArray[float64]:
"""
Return the angle between adjacent faces
Returns
--------
adjacency_angle : (n, ) float
Angle between adjacent faces
Each value corresponds with self.face_adjacency
"""
# get pairs of unit vectors for adjacent faces
pairs = self.face_normals[self.face_adjacency]
# find the angle between the pairs of vectors
angles = geometry.vector_angle(pairs)
return angles
@caching.cache_decorator
def face_adjacency_projections(self) -> NDArray[float64]:
"""
The projection of the non-shared vertex of a triangle onto
its adjacent face
Returns
----------
projections : (len(self.face_adjacency), ) float
Dot product of vertex
onto plane of adjacent triangle.
"""
projections = convex.adjacency_projections(self)
return projections
@caching.cache_decorator
def face_adjacency_convex(self) -> NDArray[np.bool_]:
"""
Return faces which are adjacent and locally convex.
What this means is that given faces A and B, the one vertex
in B that is not shared with A, projected onto the plane of A
has a projection that is zero or negative.
Returns
----------
are_convex : (len(self.face_adjacency), ) bool
Face pairs that are locally convex
"""
return self.face_adjacency_projections < tol.merge
@caching.cache_decorator
def face_adjacency_unshared(self) -> NDArray[int64]:
"""
Return the vertex index of the two vertices not in the shared
edge between two adjacent faces
Returns
-----------
vid_unshared : (len(mesh.face_adjacency), 2) int
Indexes of mesh.vertices
"""
return graph.face_adjacency_unshared(self)
@caching.cache_decorator
def face_adjacency_radius(self) -> NDArray[float64]:
"""
The approximate radius of a cylinder that fits inside adjacent faces.
Returns
------------
radii : (len(self.face_adjacency), ) float
Approximate radius formed by triangle pair
"""
radii, self._cache["face_adjacency_span"] = graph.face_adjacency_radius(mesh=self)
return radii
@caching.cache_decorator
def face_adjacency_span(self) -> NDArray[float64]:
"""
The approximate perpendicular projection of the non-shared
vertices in a pair of adjacent faces onto the shared edge of
the two faces.
Returns
------------
span : (len(self.face_adjacency), ) float
Approximate span between the non-shared vertices
"""
_ = self.face_adjacency_radius
return self._cache["face_adjacency_span"]
@caching.cache_decorator
def integral_mean_curvature(self) -> float64:
"""
The integral mean curvature, or the surface integral of the mean curvature.
Returns
---------
area : float
Integral mean curvature of mesh
"""
edges_length = np.linalg.norm(
np.subtract(*self.vertices[self.face_adjacency_edges.T]), axis=1
)
return (self.face_adjacency_angles * edges_length).sum() * 0.5
@caching.cache_decorator
def vertex_adjacency_graph(self) -> Graph:
"""
Returns a networkx graph representing the vertices and their connections
in the mesh.
Returns
---------
graph: networkx.Graph
Graph representing vertices and edges between
them where vertices are nodes and edges are edges
Examples
----------
This is useful for getting nearby vertices for a given vertex,
potentially for some simple smoothing techniques.
mesh = trimesh.primitives.Box()
graph = mesh.vertex_adjacency_graph
graph.neighbors(0)
> [1, 2, 3, 4]
"""
return graph.vertex_adjacency_graph(mesh=self)
@caching.cache_decorator
def vertex_neighbors(self) -> List[List[int64]]:
"""
The vertex neighbors of each vertex of the mesh, determined from
the cached vertex_adjacency_graph, if already existent.
Returns
----------
vertex_neighbors : (len(self.vertices), ) int
Represents immediate neighbors of each vertex along
the edge of a triangle
Examples
----------
This is useful for getting nearby vertices for a given vertex,
potentially for some simple smoothing techniques.
>>> mesh = trimesh.primitives.Box()
>>> mesh.vertex_neighbors[0]
[1, 2, 3, 4]
"""
return graph.neighbors(edges=self.edges_unique, max_index=len(self.vertices))
@caching.cache_decorator
def is_winding_consistent(self) -> bool:
"""
Does the mesh have consistent winding or not.
A mesh with consistent winding has each shared edge
going in an opposite direction from the other in the pair.
Returns
--------
consistent : bool
Is winding is consistent or not
"""
if self.is_empty:
return False
# consistent winding check is populated into the cache by is_watertight
_ = self.is_watertight
return self._cache["is_winding_consistent"]
@caching.cache_decorator
def is_watertight(self) -> bool:
"""
Check if a mesh is watertight by making sure every edge is
included in two faces.
Returns
----------
is_watertight : bool
Is mesh watertight or not
"""
if self.is_empty:
return False
watertight, winding = graph.is_watertight(
edges=self.edges, edges_sorted=self.edges_sorted
)
self._cache["is_winding_consistent"] = winding
return watertight
@caching.cache_decorator
def is_volume(self) -> bool:
"""
Check if a mesh has all the properties required to represent
a valid volume, rather than just a surface.
These properties include being watertight, having consistent
winding and outward facing normals.
Returns
---------
valid : bool
Does the mesh represent a volume
"""
valid = bool(
self.is_watertight
and self.is_winding_consistent
and np.isfinite(self.center_mass).all()
and self.volume > 0.0
)
return valid
@property
def is_empty(self) -> bool:
"""
Does the current mesh have data defined.
Returns
--------
empty : bool
If True, no data is set on the current mesh
"""
return self._data.is_empty()
@caching.cache_decorator
def is_convex(self) -> bool:
"""
Check if a mesh is convex or not.
Returns
----------
is_convex: bool
Is mesh convex or not
"""
if self.is_empty:
return False
is_convex = bool(convex.is_convex(self))
return is_convex
@caching.cache_decorator
def kdtree(self) -> cKDTree:
"""
Return a scipy.spatial.cKDTree of the vertices of the mesh.
Not cached as this lead to observed memory issues and segfaults.
Returns
---------
tree : scipy.spatial.cKDTree
Contains mesh.vertices
"""
return cKDTree(self.vertices.view(np.ndarray))
[docs]
def remove_degenerate_faces(self, height: float = tol.merge) -> None:
"""
DERECATED MARCH 2024 REPLACE WITH:
`self.update_faces(self.nondegenerate_faces(height=height))`
"""
warnings.warn(
"`remove_degenerate_faces` is deprecated "
+ "and will be removed in March 2024 replace with "
+ "`self.update_faces(self.nondegenerate_faces(height=height))`",
category=DeprecationWarning,
stacklevel=2,
)
self.update_faces(self.nondegenerate_faces(height=height))
[docs]
def nondegenerate_faces(self, height: float = tol.merge) -> NDArray[np.bool_]:
"""
Identify degenerate faces (faces without 3 unique vertex indices)
in the current mesh.
Usage example for removing them:
`mesh.update_faces(mesh.nondegenerate_faces())`
If a height is specified, it will identify any face with a 2D oriented
bounding box with one edge shorter than that height.
If not specified, it will identify any face with a zero normal.
Parameters
------------
height : float
If specified identifies faces with an oriented bounding
box shorter than this on one side.
Returns
-------------
nondegenerate : (len(self.faces), ) bool
Mask that can be used to remove faces
"""
return triangles.nondegenerate(
self.triangles, areas=self.area_faces, height=height
)
@caching.cache_decorator
def facets(self) -> List[NDArray[int64]]:
"""
Return a list of face indices for coplanar adjacent faces.
Returns
---------
facets : (n, ) sequence of (m, ) int
Groups of indexes of self.faces
"""
facets = graph.facets(self)
return facets
@caching.cache_decorator
def facets_area(self) -> NDArray[float64]:
"""
Return an array containing the area of each facet.
Returns
---------
area : (len(self.facets), ) float
Total area of each facet (group of faces)
"""
# avoid thrashing the cache inside a loop
area_faces = self.area_faces
# sum the area of each group of faces represented by facets
# use native python sum in tight loop as opposed to array.sum()
# as in this case the lower function call overhead of
# native sum provides roughly a 50% speedup
areas = np.array([sum(area_faces[i]) for i in self.facets], dtype=float64)
return areas
@caching.cache_decorator
def facets_normal(self) -> NDArray[float64]:
"""
Return the normal of each facet
Returns
---------
normals: (len(self.facets), 3) float
A unit normal vector for each facet
"""
if len(self.facets) == 0:
return np.array([])
area_faces = self.area_faces
# the face index of the largest face in each facet
index = np.array([i[area_faces[i].argmax()] for i in self.facets])
# (n, 3) float, unit normal vectors of facet plane
normals = self.face_normals[index]
# (n, 3) float, points on facet plane
origins = self.vertices[self.faces[:, 0][index]]
# save origins in cache
self._cache["facets_origin"] = origins
return normals
@caching.cache_decorator
def facets_origin(self) -> NDArray[float64]:
"""
Return a point on the facet plane.
Returns
------------
origins : (len(self.facets), 3) float
A point on each facet plane
"""
_ = self.facets_normal
return self._cache["facets_origin"]
@caching.cache_decorator
def facets_boundary(self) -> List[NDArray[int64]]:
"""
Return the edges which represent the boundary of each facet
Returns
---------
edges_boundary : sequence of (n, 2) int
Indices of self.vertices
"""
# make each row correspond to a single face
edges = self.edges_sorted.reshape((-1, 6))
# get the edges for each facet
edges_facet = [edges[i].reshape((-1, 2)) for i in self.facets]
edges_boundary = [i[grouping.group_rows(i, require_count=1)] for i in edges_facet]
return edges_boundary
@caching.cache_decorator
def facets_on_hull(self) -> NDArray[np.bool_]:
"""
Find which facets of the mesh are on the convex hull.
Returns
---------
on_hull : (len(mesh.facets), ) bool
is A facet on the meshes convex hull or not
"""
# if no facets exit early
if len(self.facets) == 0:
return np.array([], dtype=bool)
# facets plane, origin and normal
normals = self.facets_normal
origins = self.facets_origin
# (n, 3) convex hull vertices
convex = self.convex_hull.vertices.view(np.ndarray).copy()
# boolean mask for which facets are on convex hull
on_hull = np.zeros(len(self.facets), dtype=bool)
for i, normal, origin in zip(range(len(normals)), normals, origins):
# a facet plane is on the convex hull if every vertex
# of the convex hull is behind that plane
# which we are checking with dot products
dot = np.dot(normal, (convex - origin).T)
on_hull[i] = (dot < tol.merge).all()
return on_hull
[docs]
def fix_normals(self, multibody: Optional[bool] = None) -> None:
"""
Find and fix problems with self.face_normals and self.faces
winding direction.
For face normals ensure that vectors are consistently pointed
outwards, and that self.faces is wound in the correct direction
for all connected components.
Parameters
-------------
multibody : None or bool
Fix normals across multiple bodies
if None automatically pick from body_count
"""
if multibody is None:
multibody = self.body_count > 1
repair.fix_normals(self, multibody=multibody)
[docs]
def fill_holes(self) -> bool:
"""
Fill single triangle and single quad holes in the current mesh.
Returns
----------
watertight : bool
Is the mesh watertight after the function completes
"""
return repair.fill_holes(self)
[docs]
def register(self, other: Union[Geometry3D, NDArray], **kwargs):
"""
Align a mesh with another mesh or a PointCloud using
the principal axes of inertia as a starting point which
is refined by iterative closest point.
Parameters
------------
other : trimesh.Trimesh or (n, 3) float
Mesh or points in space
samples : int
Number of samples from mesh surface to align
icp_first : int
How many ICP iterations for the 9 possible
combinations of
icp_final : int
How many ICP itertations for the closest
candidate from the wider search
Returns
-----------
mesh_to_other : (4, 4) float
Transform to align mesh to the other object
cost : float
Average square distance per point
"""
mesh_to_other, cost = registration.mesh_other(mesh=self, other=other, **kwargs)
return mesh_to_other, cost
[docs]
def compute_stable_poses(
self,
center_mass: Optional[NDArray[float64]] = None,
sigma: float = 0.0,
n_samples: int = 1,
threshold: float = 0.0,
):
"""
Computes stable orientations of a mesh and their quasi-static probabilities.
This method samples the location of the center of mass from a multivariate
gaussian (mean at com, cov equal to identity times sigma) over n_samples.
For each sample, it computes the stable resting poses of the mesh on a
a planar workspace and evaluates the probabilities of landing in
each pose if the object is dropped onto the table randomly.
This method returns the 4x4 homogeneous transform matrices that place
the shape against the planar surface with the z-axis pointing upwards
and a list of the probabilities for each pose.
The transforms and probabilities that are returned are sorted, with the
most probable pose first.
Parameters
------------
center_mass : (3, ) float
The object center of mass (if None, this method
assumes uniform density and watertightness and
computes a center of mass explicitly)
sigma : float
The covariance for the multivariate gaussian used
to sample center of mass locations
n_samples : int
The number of samples of the center of mass location
threshold : float
The probability value at which to threshold
returned stable poses
Returns
-------
transforms : (n, 4, 4) float
The homogeneous matrices that transform the
object to rest in a stable pose, with the
new z-axis pointing upwards from the table
and the object just touching the table.
probs : (n, ) float
A probability ranging from 0.0 to 1.0 for each pose
"""
return poses.compute_stable_poses(
mesh=self,
center_mass=center_mass,
sigma=sigma,
n_samples=n_samples,
threshold=threshold,
)
[docs]
def subdivide(self, face_index: Optional[ArrayLike] = None) -> "Trimesh":
"""
Subdivide a mesh with each subdivided face replaced
with four smaller faces. Will return a copy of current
mesh with subdivided faces.
Parameters
------------
face_index : (m, ) int or None
If None all faces of mesh will be subdivided
If (m, ) int array of indices: only specified faces will be
subdivided. Note that in this case the mesh will generally
no longer be manifold, as the additional vertex on the midpoint
will not be used by the adjacent faces to the faces specified,
and an additional postprocessing step will be required to
make resulting mesh watertight
"""
visual = None
if hasattr(self.visual, "uv") and np.shape(self.visual.uv) == (
len(self.vertices),
2,
):
# uv coords divided along with vertices
vertices, faces, attr = remesh.subdivide(
vertices=np.hstack((self.vertices, self.visual.uv)),
faces=self.faces,
face_index=face_index,
vertex_attributes=self.vertex_attributes,
)
# get a copy of the current visuals
visual = self.visual.copy()
# separate uv coords and vertices
vertices, visual.uv = vertices[:, :3], vertices[:, 3:]
else:
# perform the subdivision with vertex attributes
vertices, faces, attr = remesh.subdivide(
vertices=self.vertices,
faces=self.faces,
face_index=face_index,
vertex_attributes=self.vertex_attributes,
)
# create a new mesh
result = Trimesh(
vertices=vertices,
faces=faces,
visual=visual,
vertex_attributes=attr,
process=False,
)
return result
[docs]
def subdivide_to_size(self, max_edge, max_iter=10, return_index=False):
"""
Subdivide a mesh until every edge is shorter than a
specified length.
Will return a triangle soup, not a nicely structured mesh.
Parameters
------------
max_edge : float
Maximum length of any edge in the result
max_iter : int
The maximum number of times to run subdivision
return_index : bool
If True, return index of original face for new faces
"""
# subdivide vertex attributes
visual = None
if hasattr(self.visual, "uv") and np.shape(self.visual.uv) == (
len(self.vertices),
2,
):
# uv coords divided along with vertices
vertices_faces = remesh.subdivide_to_size(
vertices=np.hstack((self.vertices, self.visual.uv)),
faces=self.faces,
max_edge=max_edge,
max_iter=max_iter,
return_index=return_index,
)
# unpack result
if return_index:
vertices, faces, final_index = vertices_faces
else:
vertices, faces = vertices_faces
# get a copy of the current visuals
visual = self.visual.copy()
# separate uv coords and vertices
vertices, visual.uv = vertices[:, :3], vertices[:, 3:]
else:
# uv coords divided along with vertices
vertices_faces = remesh.subdivide_to_size(
vertices=self.vertices,
faces=self.faces,
max_edge=max_edge,
max_iter=max_iter,
return_index=return_index,
)
# unpack result
if return_index:
vertices, faces, final_index = vertices_faces
else:
vertices, faces = vertices_faces
# create a new mesh
result = Trimesh(vertices=vertices, faces=faces, visual=visual, process=False)
if return_index:
return result, final_index
return result
[docs]
def subdivide_loop(self, iterations=None):
"""
Subdivide a mesh by dividing each triangle into four
triangles and approximating their smoothed surface
using loop subdivision. Loop subdivision often looks
better on triangular meshes than catmul-clark, which
operates primarily on quads.
Parameters
------------
iterations : int
Number of iterations to run subdivision.
multibody : bool
If True will try to subdivide for each submesh
"""
# perform subdivision for one mesh
new_vertices, new_faces = remesh.subdivide_loop(
vertices=self.vertices, faces=self.faces, iterations=iterations
)
# create new mesh
result = Trimesh(vertices=new_vertices, faces=new_faces, process=False)
return result
[docs]
def smoothed(self, **kwargs):
"""
DEPRECATED: use `mesh.smooth_shaded` or `trimesh.graph.smooth_shade(mesh)`
"""
warnings.warn(
"`mesh.smoothed()` is deprecated and will be removed in March 2024: "
+ "use `mesh.smooth_shaded` or `trimesh.graph.smooth_shade(mesh)`",
category=DeprecationWarning,
stacklevel=2,
)
# run smoothing
return self.smooth_shaded
@property
def smooth_shaded(self):
"""
Smooth shading in OpenGL relies on which vertices are shared,
this function will disconnect regions above an angle threshold
and return a non-watertight version which will look better
in an OpenGL rendering context.
If you would like to use non-default arguments see `graph.smooth_shade`.
Returns
---------
smooth_shaded : trimesh.Trimesh
Non watertight version of current mesh.
"""
# key this also by the visual properties
# but store it in the mesh cache
self.visual._verify_hash()
cache = self.visual._cache
# needs to be dumped whenever visual or mesh changes
key = f"smooth_shaded_{hash(self.visual)}_{hash(self)}"
if key in cache:
return cache[key]
smooth = graph.smooth_shade(self)
# store it in the mesh cache which dumps when vertices change
cache[key] = smooth
return smooth
@property
def visual(self):
"""
Get the stored visuals for the current mesh.
Returns
-------------
visual : ColorVisuals or TextureVisuals
Contains visual information about the mesh
"""
if hasattr(self, "_visual"):
return self._visual
return None
@visual.setter
def visual(self, value):
"""
When setting a visual object, always make sure
that `visual.mesh` points back to the source mesh.
Parameters
--------------
visual : ColorVisuals or TextureVisuals
Contains visual information about the mesh
"""
value.mesh = self
self._visual = value
[docs]
def section(
self, plane_normal: ArrayLike, plane_origin: ArrayLike, **kwargs
) -> Optional[Path3D]:
"""
Returns a 3D cross section of the current mesh and a plane
defined by origin and normal.
Parameters
------------
plane_normal : (3,) float
Normal vector of section plane.
plane_origin : (3, ) float
Point on the cross section plane.
Returns
---------
intersections
Curve of intersection or None if it was not hit by plane.
"""
# turn line segments into Path2D/Path3D objects
from .exchange.load import load_path
# return a single cross section in 3D
lines, face_index = intersections.mesh_plane(
mesh=self,
plane_normal=plane_normal,
plane_origin=plane_origin,
return_faces=True,
**kwargs,
)
# if the section didn't hit the mesh return None
if len(lines) == 0:
return None
# otherwise load the line segments into a Path3D object
path = load_path(lines)
# add the face index info into metadata
path.metadata["face_index"] = face_index
return path
[docs]
def section_multiplane(
self,
plane_origin: ArrayLike,
plane_normal: ArrayLike,
heights: ArrayLike,
) -> List[Optional[Path2D]]:
"""
Return multiple parallel cross sections of the current
mesh in 2D.
Parameters
------------
plane_origin : (3, ) float
Point on the cross section plane
plane_normal : (3) float
Normal vector of section plane
heights : (n, ) float
Each section is offset by height along
the plane normal.
Returns
---------
paths : (n, ) Path2D or None
2D cross sections at specified heights.
path.metadata['to_3D'] contains transform
to return 2D section back into 3D space.
"""
# turn line segments into Path2D/Path3D objects
from .exchange.load import load_path
# do a multiplane intersection
lines, transforms, faces = intersections.mesh_multiplane(
mesh=self,
plane_normal=plane_normal,
plane_origin=plane_origin,
heights=heights,
)
# turn the line segments into Path2D objects
paths = [None] * len(lines)
for i, f, segments, T in zip(range(len(lines)), faces, lines, transforms):
if len(segments) > 0:
paths[i] = load_path(segments, metadata={"to_3D": T, "face_index": f})
return paths
[docs]
def slice_plane(
self,
plane_origin,
plane_normal,
cap=False,
face_index=None,
cached_dots=None,
**kwargs,
):
"""
Slice the mesh with a plane, returning a new mesh that is the
portion of the original mesh to the positive normal side of the plane
plane_origin : (3,) float
Point on plane to intersect with mesh
plane_normal : (3,) float
Normal vector of plane to intersect with mesh
cap : bool
If True, cap the result with a triangulated polygon
face_index : ((m,) int)
Indexes of mesh.faces to slice. When no mask is
provided, the default is to slice all faces.
cached_dots : (n, 3) float
If an external function has stored dot
products pass them here to avoid recomputing
Returns
---------
new_mesh: trimesh.Trimesh or None
Subset of current mesh that intersects the half plane
to the positive normal side of the plane
"""
# return a new mesh
new_mesh = intersections.slice_mesh_plane(
mesh=self,
plane_normal=plane_normal,
plane_origin=plane_origin,
cap=cap,
face_index=face_index,
cached_dots=cached_dots,
**kwargs,
)
return new_mesh
[docs]
def unwrap(self, image=None):
"""
Returns a Trimesh object equivalent to the current mesh where
the vertices have been assigned uv texture coordinates. Vertices
may be split into as many as necessary by the unwrapping
algorithm, depending on how many uv maps they appear in.
Requires `pip install xatlas`
Parameters
------------
image : None or PIL.Image
Image to assign to the material
Returns
--------
unwrapped : trimesh.Trimesh
Mesh with unwrapped uv coordinates
"""
import xatlas
vmap, faces, uv = xatlas.parametrize(self.vertices, self.faces)
result = Trimesh(
vertices=self.vertices[vmap],
faces=faces,
visual=TextureVisuals(uv=uv, image=image),
process=False,
)
# run additional checks for unwrapping
if tol.strict:
# check the export object to make sure we didn't
# move the indices around on creation
assert np.allclose(result.visual.uv, uv)
assert np.allclose(result.faces, faces)
assert np.allclose(result.vertices, self.vertices[vmap])
# check to make sure indices are still the
# same order after we've exported to OBJ
export = result.export(file_type="obj")
uv_recon = np.array(
[L[3:].split() for L in str.splitlines(export) if L.startswith("vt ")],
dtype=float64,
)
assert np.allclose(uv_recon, uv)
v_recon = np.array(
[L[2:].split() for L in str.splitlines(export) if L.startswith("v ")],
dtype=float64,
)
assert np.allclose(v_recon, self.vertices[vmap])
return result
@caching.cache_decorator
def convex_hull(self) -> "Trimesh":
"""
Returns a Trimesh object representing the convex hull of
the current mesh.
Returns
--------
convex : trimesh.Trimesh
Mesh of convex hull of current mesh
"""
return convex.convex_hull(self)
[docs]
def sample(
self,
count: int,
return_index: bool = False,
face_weight: Optional[NDArray[float64]] = None,
):
"""
Return random samples distributed across the
surface of the mesh
Parameters
------------
count : int
Number of points to sample
return_index : bool
If True will also return the index of which face each
sample was taken from.
face_weight : None or len(mesh.faces) float
Weight faces by a factor other than face area.
If None will be the same as face_weight=mesh.area
Returns
---------
samples : (count, 3) float
Points on surface of mesh
face_index : (count, ) int
Index of self.faces
"""
samples, index = sample.sample_surface(
mesh=self, count=count, face_weight=face_weight
)
if return_index:
return samples, index
return samples
[docs]
def remove_unreferenced_vertices(self) -> None:
"""
Remove all vertices in the current mesh which are not
referenced by a face.
"""
referenced = np.zeros(len(self.vertices), dtype=bool)
referenced[self.faces] = True
inverse = np.zeros(len(self.vertices), dtype=int64)
inverse[referenced] = np.arange(referenced.sum())
self.update_vertices(mask=referenced, inverse=inverse)
[docs]
def unmerge_vertices(self) -> None:
"""
Removes all face references so that every face contains
three unique vertex indices and no faces are adjacent.
"""
# new faces are incrementing so every vertex is unique
faces = np.arange(len(self.faces) * 3, dtype=int64).reshape((-1, 3))
# use update_vertices to apply mask to
# all properties that are per-vertex
self.update_vertices(self.faces.reshape(-1))
# set faces to incrementing indexes
self.faces = faces
# keep face normals as the haven't changed
self._cache.clear(exclude=["face_normals"])
[docs]
def voxelized(self, pitch, method="subdivide", **kwargs):
"""
Return a VoxelGrid object representing the current mesh
discretized into voxels at the specified pitch
Parameters
------------
pitch : float
The edge length of a single voxel
method: implementation key. See `trimesh.voxel.creation.voxelizers`
**kwargs: additional kwargs passed to the specified implementation.
Returns
----------
voxelized : VoxelGrid object
Representing the current mesh
"""
from .voxel import creation
return creation.voxelize(mesh=self, pitch=pitch, method=method, **kwargs)
@caching.cache_decorator
def as_open3d(self):
"""
Return an `open3d.geometry.TriangleMesh` version of
the current mesh.
Returns
---------
open3d : open3d.geometry.TriangleMesh
Current mesh as an open3d object.
"""
import open3d
# create from numpy arrays
return open3d.geometry.TriangleMesh(
vertices=open3d.utility.Vector3dVector(self.vertices.copy()),
triangles=open3d.utility.Vector3iVector(self.faces.copy()),
)
[docs]
def simplify_quadratic_decimation(self, *args, **kwargs):
"""
DERECATED MARCH 2024 REPLACE WITH:
`mesh.simplify_quadric_decimation`
"""
warnings.warn(
"`simplify_quadratic_decimation` is deprecated "
+ "as it was a typo and will be removed in March 2024: "
+ "replace with `simplify_quadric_decimation`",
category=DeprecationWarning,
stacklevel=2,
)
return self.simplify_quadric_decimation(*args, **kwargs)
[docs]
def simplify_quadric_decimation(self, face_count: int) -> "Trimesh":
"""
A thin wrapper around the `open3d` implementation of this:
`open3d.geometry.TriangleMesh.simplify_quadric_decimation`
Parameters
-----------
face_count : int
Number of faces desired in the resulting mesh.
Returns
---------
simple : trimesh.Trimesh
Simplified version of mesh.
"""
simple = self.as_open3d.simplify_quadric_decimation(int(face_count))
return Trimesh(vertices=simple.vertices, faces=simple.triangles)
[docs]
def outline(self, face_ids: Optional[NDArray[int64]] = None, **kwargs) -> Path3D:
"""
Given a list of face indexes find the outline of those
faces and return it as a Path3D.
The outline is defined here as every edge which is only
included by a single triangle.
Note that this implies a non-watertight mesh as the
outline of a watertight mesh is an empty path.
Parameters
------------
face_ids : (n, ) int
Indices to compute the outline of.
If None, outline of full mesh will be computed.
**kwargs: passed to Path3D constructor
Returns
----------
path : Path3D
Curve in 3D of the outline
"""
from .path.exchange.misc import faces_to_path
return Path3D(**faces_to_path(self, face_ids, **kwargs))
[docs]
def projected(self, normal, **kwargs) -> Path2D:
"""
Project a mesh onto a plane and then extract the
polygon that outlines the mesh projection on that
plane.
Parameters
----------
mesh : trimesh.Trimesh
Source geometry
check : bool
If True make sure is flat
normal : (3,) float
Normal to extract flat pattern along
origin : None or (3,) float
Origin of plane to project mesh onto
pad : float
Proportion to pad polygons by before unioning
and then de-padding result by to avoid zero-width gaps.
tol_dot : float
Tolerance for discarding on-edge triangles.
max_regions : int
Raise an exception if the mesh has more than this
number of disconnected regions to fail quickly before unioning.
Returns
----------
projected : trimesh.path.Path2D
Outline of source mesh
"""
from .exchange.load import load_path
from .path import Path2D
from .path.polygons import projected
projection = projected(mesh=self, normal=normal, **kwargs)
if projection is None:
return Path2D()
return load_path(projection)
@caching.cache_decorator
def area(self) -> float64:
"""
Summed area of all triangles in the current mesh.
Returns
---------
area : float
Surface area of mesh
"""
area = self.area_faces.sum()
return area
@caching.cache_decorator
def area_faces(self) -> NDArray[float64]:
"""
The area of each face in the mesh.
Returns
---------
area_faces : (n, ) float
Area of each face
"""
return triangles.area(crosses=self.triangles_cross, sum=False)
@caching.cache_decorator
def mass_properties(self) -> MassProperties:
"""
Returns the mass properties of the current mesh.
Assumes uniform density, and result is probably garbage if mesh
isn't watertight.
Returns
----------
properties : dict
With keys:
'volume' : in global units^3
'mass' : From specified density
'density' : Included again for convenience (same as kwarg density)
'inertia' : Taken at the center of mass and aligned with global
coordinate system
'center_mass' : Center of mass location, in global coordinate system
"""
# if the density or center of mass was overridden they will be put into data
density = self._data.data.get("density", None)
center_mass = self._data.data.get("center_mass", None)
return triangles.mass_properties(
triangles=self.triangles,
crosses=self.triangles_cross,
density=density,
center_mass=center_mass,
skip_inertia=False,
)
[docs]
def invert(self) -> None:
"""
Invert the mesh in-place by reversing the winding of every
face and negating normals without dumping the cache.
Alters `self.faces` by reversing columns, and negating
`self.face_normals` and `self.vertex_normals`.
"""
with self._cache:
if "face_normals" in self._cache:
self.face_normals = self._cache["face_normals"] * -1.0
if "vertex_normals" in self._cache:
self.vertex_normals = self._cache["vertex_normals"] * -1.0
# fliplr makes array non-contiguous so cache checks slow
self.faces = np.ascontiguousarray(np.fliplr(self.faces))
# save our normals
self._cache.clear(exclude=["face_normals", "vertex_normals"])
[docs]
def scene(self, **kwargs) -> Scene:
"""
Returns a Scene object containing the current mesh.
Returns
---------
scene : trimesh.scene.scene.Scene
Contains just the current mesh
"""
return Scene(self, **kwargs)
[docs]
def show(self, **kwargs):
"""
Render the mesh in an opengl window. Requires pyglet.
Parameters
------------
smooth : bool
Run smooth shading on mesh or not,
large meshes will be slow
Returns
-----------
scene : trimesh.scene.Scene
Scene with current mesh in it
"""
scene = self.scene()
return scene.show(**kwargs)
[docs]
def submesh(
self, faces_sequence: Sequence[ArrayLike], **kwargs
) -> Union["Trimesh", List["Trimesh"]]:
"""
Return a subset of the mesh.
Parameters
------------
faces_sequence : sequence (m, ) int
Face indices of mesh
only_watertight : bool
Only return submeshes which are watertight
append : bool
Return a single mesh which has the faces appended.
if this flag is set, only_watertight is ignored
Returns
---------
submesh : Trimesh or (n,) Trimesh
Single mesh if `append` or list of submeshes
"""
return util.submesh(mesh=self, faces_sequence=faces_sequence, **kwargs)
@caching.cache_decorator
def identifier(self) -> NDArray[float64]:
"""
Return a float vector which is unique to the mesh
and is robust to rotation and translation.
Returns
-----------
identifier : (7,) float
Identifying properties of the current mesh
"""
return comparison.identifier_simple(self)
@caching.cache_decorator
def identifier_hash(self) -> str:
"""
A hash of the rotation invariant identifier vector.
Returns
---------
hashed : str
Hex string of the SHA256 hash from
the identifier vector at hand-tuned sigfigs.
"""
return comparison.identifier_hash(self.identifier)
[docs]
def export(
self,
file_obj=None,
file_type: Optional[str] = None,
**kwargs,
):
"""
Export the current mesh to a file object.
If file_obj is a filename, file will be written there.
Supported formats are stl, off, ply, collada, json,
dict, glb, dict64, msgpack.
Parameters
------------
file_obj : open writeable file object
str, file name where to save the mesh
None, return the export blob
file_type : str
Which file type to export as, if `file_name`
is passed this is not required.
"""
return export_mesh(mesh=self, file_obj=file_obj, file_type=file_type, **kwargs)
[docs]
def to_dict(self) -> Dict[str, Union[str, List[List[float]], List[List[int]]]]:
"""
Return a dictionary representation of the current mesh
with keys that can be used as the kwargs for the
Trimesh constructor and matches the schema in:
`trimesh/resources/schema/primitive/trimesh.schema.json`
Returns
----------
result : dict
Matches schema and Trimesh constructor.
"""
return {
"kind": "trimesh",
"vertices": self.vertices.tolist(),
"faces": self.faces.tolist(),
}
[docs]
def convex_decomposition(self, **kwargs) -> List["Trimesh"]:
"""
Compute an approximate convex decomposition of a mesh
using `pip install pyVHACD`.
Returns
-------
meshes
List of convex meshes that approximate the original
**kwargs : VHACD keyword arguments
"""
return [
Trimesh(**kwargs)
for kwargs in decomposition.convex_decomposition(self, **kwargs)
]
[docs]
def union(
self,
other: "Trimesh",
engine: Optional[str] = None,
check_volume: bool = True,
**kwargs,
) -> "Trimesh":
"""
Boolean union between this mesh and other meshes.
Parameters
------------
other : Trimesh or (n, ) Trimesh
Other meshes to union
engine
Which backend to use, the default
recommendation is: `pip install manifold3d`.
check_volume
Raise an error if not all meshes are watertight
positive volumes. Advanced users may want to ignore
this check as it is expensive.
kwargs
Passed through to the `engine`.
Returns
---------
union : trimesh.Trimesh
Union of self and other Trimesh objects
"""
return boolean.union(
meshes=np.append(self, other),
engine=engine,
check_volume=check_volume,
**kwargs,
)
[docs]
def difference(
self,
other: "Trimesh",
engine: Optional[str] = None,
check_volume: bool = True,
**kwargs,
) -> "Trimesh":
"""
Boolean difference between this mesh and n other meshes
Parameters
------------
other : trimesh.Trimesh, or list of trimesh.Trimesh objects
Meshes to difference
engine
Which backend to use, the default
recommendation is: `pip install manifold3d`.
check_volume
Raise an error if not all meshes are watertight
positive volumes. Advanced users may want to ignore
this check as it is expensive.
kwargs
Passed through to the `engine`.
Returns
---------
difference : trimesh.Trimesh
Difference between self and other Trimesh objects
"""
return boolean.difference(
meshes=[self, other], engine=engine, check_volume=check_volume, **kwargs
)
[docs]
def intersection(
self,
other: "Trimesh",
engine: Optional[str] = None,
check_volume: bool = True,
**kwargs,
) -> "Trimesh":
"""
Boolean intersection between this mesh and other meshes.
Parameters
------------
other : trimesh.Trimesh, or list of trimesh.Trimesh objects
Meshes to calculate intersections with
engine
Which backend to use, the default
recommendation is: `pip install manifold3d`.
check_volume
Raise an error if not all meshes are watertight
positive volumes. Advanced users may want to ignore
this check as it is expensive.
kwargs
Passed through to the `engine`.
Returns
---------
intersection : trimesh.Trimesh
Mesh of the volume contained by all passed meshes
"""
return boolean.intersection(
meshes=np.append(self, other),
engine=engine,
check_volume=check_volume,
**kwargs,
)
[docs]
def contains(self, points: ArrayLike) -> NDArray[np.bool_]:
"""
Given an array of points determine whether or not they
are inside the mesh. This raises an error if called on a
non-watertight mesh.
Parameters
------------
points : (n, 3) float
Points in cartesian space
Returns
---------
contains : (n, ) bool
Whether or not each point is inside the mesh
"""
return self.ray.contains_points(points)
@caching.cache_decorator
def face_angles(self) -> NDArray[float64]:
"""
Returns the angle at each vertex of a face.
Returns
--------
angles : (len(self.faces), 3) float
Angle at each vertex of a face
"""
return triangles.angles(self.triangles)
@caching.cache_decorator
def face_angles_sparse(self) -> coo_matrix:
"""
A sparse matrix representation of the face angles.
Returns
----------
sparse : scipy.sparse.coo_matrix
Float sparse matrix with with shape:
(len(self.vertices), len(self.faces))
"""
angles = curvature.face_angles_sparse(self)
return angles
@caching.cache_decorator
def vertex_defects(self) -> NDArray[float64]:
"""
Return the vertex defects, or (2*pi) minus the sum of the angles
of every face that includes that vertex.
If a vertex is only included by coplanar triangles, this
will be zero. For convex regions this is positive, and
concave negative.
Returns
--------
vertex_defect : (len(self.vertices), ) float
Vertex defect at the every vertex
"""
defects = curvature.vertex_defects(self)
return defects
@caching.cache_decorator
def vertex_degree(self) -> NDArray[int64]:
"""
Return the number of faces each vertex is included in.
Returns
----------
degree : (len(self.vertices), ) int
Number of faces each vertex is included in
"""
# get degree through sparse matrix
degree = np.array(self.faces_sparse.sum(axis=1)).flatten()
return degree
@caching.cache_decorator
def face_adjacency_tree(self) -> Index:
"""
An R-tree of face adjacencies.
Returns
--------
tree
Where each edge in self.face_adjacency has a
rectangular cell
"""
# the (n,6) interleaved bounding box for every line segment
return util.bounds_tree(
np.column_stack(
(
self.vertices[self.face_adjacency_edges].min(axis=1),
self.vertices[self.face_adjacency_edges].max(axis=1),
)
)
)
[docs]
def copy(self, include_cache: bool = False) -> "Trimesh":
"""
Safely return a copy of the current mesh.
By default, copied meshes will have emptied cache
to avoid memory issues and so may be slow on initial
operations until caches are regenerated.
Current object will *never* have its cache cleared.
Parameters
------------
include_cache : bool
If True, will shallow copy cached data to new mesh
Returns
---------
copied : trimesh.Trimesh
Copy of current mesh
"""
# start with an empty mesh
copied = Trimesh()
# always deepcopy vertex and face data
copied._data.data = copy.deepcopy(self._data.data)
# copy visual information
copied.visual = self.visual.copy()
# get metadata
copied.metadata = copy.deepcopy(self.metadata)
# make sure cache ID is set initially
copied._cache.verify()
if include_cache:
# shallow copy cached items into the new cache
# since the data didn't change here when the
# data in the new mesh is changed these items
# will be dumped in the new mesh but preserved
# in the original mesh
copied._cache.cache.update(self._cache.cache)
return copied
def __deepcopy__(self, *args) -> "Trimesh":
# interpret deep copy as "get rid of cached data"
return self.copy(include_cache=False)
def __copy__(self, *args) -> "Trimesh":
# interpret shallow copy as "keep cached data"
return self.copy(include_cache=True)
[docs]
def eval_cached(self, statement: str, *args):
"""
Evaluate a statement and cache the result before returning.
Statements are evaluated inside the Trimesh object, and
Parameters
------------
statement : str
Statement of valid python code
*args : list
Available inside statement as args[0], etc
Returns
-----------
result : result of running eval on statement with args
Examples
-----------
r = mesh.eval_cached('np.dot(self.vertices, args[0])', [0, 0, 1])
"""
# store this by the combined hash of statement and args
hashable = [hash(statement)]
hashable.extend(hash(a) for a in args)
key = f"eval_cached_{hash(tuple(hashable))}"
if key in self._cache:
return self._cache[key]
result = eval(statement)
self._cache[key] = result
return result
[docs]
def __add__(self, other: "Trimesh") -> "Trimesh":
"""
Concatenate the mesh with another mesh.
Parameters
------------
other : trimesh.Trimesh object
Mesh to be concatenated with self
Returns
----------
concat : trimesh.Trimesh
Mesh object of combined result
"""
concat = util.concatenate(self, other)
return concat
