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city_retrofit/venv/lib/python3.7/site-packages/trimesh/base.py

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Add virtual environment to the git repository, this isn't 100% right but it's practical at this development point
2020-06-16 10:34:17 -04:00
# flake8: noqa
"""
github.com/mikedh/trimesh
----------------------------
Library for importing, exporting and doing simple operations on triangular meshes.
"""
from . import ray
from . import util
from . import units
from . import poses
from . import graph
from . import sample
from . import repair
from . import convex
from . import remesh
from . import bounds
from . import caching
from . import inertia
from . import nsphere
from . import boolean
from . import grouping
from . import geometry
from . import permutate
from . import proximity
from . import triangles
from . import collision
from . import curvature
from . import smoothing
from . import comparison
from . import registration
from . import decomposition
from . import intersections
from . import transformations
from .visual import create_visual
from .exchange.export import export_mesh
from .constants import log, log_time, tol
from .scene import Scene
from .parent import Geometry
import copy
import numpy as np
class Trimesh(Geometry):
def __init__(self,
vertices=None,
faces=None,
face_normals=None,
vertex_normals=None,
face_colors=None,
vertex_colors=None,
face_attributes=None,
vertex_attributes=None,
metadata=None,
process=True,
validate=False,
use_embree=True,
initial_cache=None,
visual=None,
**kwargs):
"""
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
"""
if initial_cache is None:
initial_cache = {}
# 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 md5 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.crc() changes
self._cache = caching.Cache(
id_function=self._data.fast_hash,
force_immutable=True)
self._cache.update(initial_cache)
# check for None only to avoid warning messages in subclasses
if vertices is not None:
# (n, 3) float, set of vertices
self.vertices = vertices
if faces is not None:
# (m, 3) int of triangle faces, 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)
# store metadata about the mesh in a dictionary
self.metadata = dict()
# update the mesh metadata with passed metadata
if isinstance(metadata, dict):
self.metadata.update(metadata)
elif metadata is not None:
raise ValueError(
'metadata should be a dict or None, got %s' % str(metadata))
# Set the default center of mass and density
self._density = 1.0
self._center_mass = None
# 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, **kwargs)
# save reference to kwargs
self._kwargs = kwargs
def process(self, validate=False, **kwargs):
"""
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 rectangular 2D
oriented bounding box shorter than tol.merge
4) remove duplicated triangles
Parameters
------------
validate : bool
If True, 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:
self.remove_infinite_values()
self.merge_vertices(**kwargs)
# if we're cleaning remove duplicate
# and degenerate faces
if validate:
self.remove_duplicate_faces()
self.remove_degenerate_faces()
# 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._cache.clear(exclude=['face_normals',
'vertex_normals'])
self.metadata['processed'] = True
return self
def md5(self):
"""
An MD5 of the core geometry information for the mesh,
faces and vertices.
Generated from TrackedArray which subclasses np.ndarray to
monitor array for changes and returns a correct lazily
evaluated md5 so it only has to recalculate the hash
occasionally, rather than on every call.
Returns
----------
md5 : string
MD5 of everything in the DataStore
"""
md5 = self._data.md5()
return md5
def crc(self):
"""
A zlib.adler32 checksum for the current mesh data.
This is about 5x faster than an MD5, and the checksum is
checked every time something is requested from the cache so
it gets called a lot.
Returns
----------
crc : int
Checksum of current mesh data
"""
return self._data.fast_hash()
@property
def faces(self):
"""
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 if this is altered.
Returns
----------
faces : (n, 3) int
Representing triangles which reference self.vertices
"""
return self._data.get('faces', np.empty(shape=(0, 3), dtype=np.int64))
@faces.setter
def faces(self, values):
"""
Set the vertex indexes that make up triangular faces.
Parameters
--------------
values : (n, 3) int
Indexes of self.vertices
"""
if values is None or len(values) == 0:
if 'faces' in self._data:
del self._data['faces']
return
values = np.asanyarray(values, dtype=np.int64)
# automatically triangulate quad faces
if util.is_shape(values, (-1, 4)):
log.info('triangulating quad faces')
values = geometry.triangulate_quads(values)
self._data['faces'] = values
@caching.cache_decorator
def faces_sparse(self):
"""
A sparse matrix representation of the faces.
Returns
----------
sparse : scipy.sparse.coo_matrix
Has properties:
dtype : bool
shape : (len(self.vertices), len(self.faces))
"""
sparse = geometry.index_sparse(
columns=len(self.vertices),
indices=self.faces)
return sparse
@property
def face_normals(self):
"""
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) np.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=np.int64).reshape((0, 3))
# if the shape of cached normals equals the shape of faces return
if np.shape(cached) == np.shape(faces):
return cached
log.debug('generating face normals')
# 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=np.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):
"""
Assign values to face normals.
Parameters
-------------
values : (len(self.faces), 3) float
Unit face 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=np.float64)
# face normals need to correspond to faces
if len(values) == 0 or values.shape != self.faces.shape:
log.warning('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.warning('face_normals contain NaN, ignoring!')
return
if ptp < tol.merge:
log.warning('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):
"""
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
"""
return self._data.get('vertices', np.empty(shape=(0, 3), dtype=np.float64))
@vertices.setter
def vertices(self, values):
"""
Assign vertex values to the mesh.
Parameters
--------------
values : (n, 3) float
Points in space
"""
self._data['vertices'] = np.asanyarray(
values, order='C', dtype=np.float64)
@caching.cache_decorator
def vertex_normals(self):
"""
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')
vertex_normals = geometry.weighted_vertex_normals(
vertex_count=len(self.vertices),
faces=self.faces,
face_normals=self.face_normals,
face_angles=self.face_angles)
return vertex_normals
@vertex_normals.setter
def vertex_normals(self, values):
"""
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=np.float64)
if values.shape == self.vertices.shape:
# check to see if they assigned all zeros
if values.ptp() < tol.merge:
log.warning(
'vertex_normals are all set to zero!')
self._cache['vertex_normals'] = values
@caching.cache_decorator
def vertex_faces(self):
"""
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):
"""
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
mesh_bounds = np.array([in_mesh.min(axis=0),
in_mesh.max(axis=0)])
return mesh_bounds
@caching.cache_decorator
def extents(self):
"""
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 scale(self):
"""
A metric for the overall scale of the mesh, the length of the
diagonal of the axis aligned bounding box of the mesh.
Returns
----------
scale : float
The length of the meshes AABB diagonal
"""
# if mesh is empty just return no scale
if self.extents is None:
return 1.0
# make sure we are returning python floats
scale = float((self.extents ** 2).sum() ** .5)
return scale
@caching.cache_decorator
def centroid(self):
"""
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):
"""
The point in space which is the center of mass/volume.
If the current mesh is not watertight this is meaningless
garbage unless it was explicitly set.
Returns
-----------
center_mass : (3, ) float
Volumetric center of mass of the mesh
"""
center_mass = self.mass_properties['center_mass']
return center_mass
@center_mass.setter
def center_mass(self, cm):
self._center_mass = cm
self._cache.delete('mass_properties')
@property
def density(self):
"""
The density of the mesh.
Returns
-----------
density : float
The density of the mesh.
"""
density = self.mass_properties['density']
return density
@density.setter
def density(self, value):
"""
Set the density of the mesh.
Parameters
-------------
density : float
Specify the density of the mesh to be used in inertia calculations
"""
self._density = float(value)
self._cache.delete('mass_properties')
@property
def volume(self):
"""
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
"""
volume = self.mass_properties['volume']
return volume
@property
def mass(self):
"""
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
"""
mass = self.mass_properties['mass']
return mass
@property
def moment_inertia(self):
"""
Return the moment of inertia matrix of the current mesh.
If mesh isn't watertight this is garbage.
Returns
---------
inertia : (3, 3) float
Moment of inertia of the current mesh
"""
inertia = self.mass_properties['inertia']
return inertia
@caching.cache_decorator
def principal_inertia_components(self):
"""
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):
"""
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
"""
populate = self.principal_inertia_components
return self._cache['principal_inertia_vectors']
@caching.cache_decorator
def principal_inertia_transform(self):
"""
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):
"""
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):
"""
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 not None:
return self._cache['symmetry_axis']
@property
def symmetry_section(self):
"""
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 not None:
return self._cache['symmetry_section']
@caching.cache_decorator
def triangles(self):
"""
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 MD5 will have to be
# recomputed. We can escape this check by viewing the array.
triangles = self.vertices.view(np.ndarray)[self.faces]
return triangles
@caching.cache_decorator
def triangles_tree(self):
"""
An R-tree containing each face of the mesh.
Returns
----------
tree : rtree.index
Each triangle in self.faces has a rectangular cell
"""
tree = triangles.bounds_tree(self.triangles)
return tree
@caching.cache_decorator
def triangles_center(self):
"""
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
"""
triangles_center = self.triangles.mean(axis=1)
return triangles_center
@caching.cache_decorator
def triangles_cross(self):
"""
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):
"""
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):
"""
Which face does each edge belong to.
Returns
---------
edges_face : (n, ) int
Index of self.faces
"""
populate = self.edges
return self._cache['edges_face']
@caching.cache_decorator
def edges_unique(self):
"""
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):
"""
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):
"""
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
"""
populate = self.edges_unique
return self._cache['edges_unique_inverse']
@caching.cache_decorator
def edges_sorted(self):
"""
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):
"""
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
"""
from scipy.spatial import cKDTree
return cKDTree(self.edges_sorted)
@caching.cache_decorator
def edges_sparse(self):
"""
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):
"""
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):
"""
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[0:2]
Out[0]:
TrackedArray([[ 1, 6946, 24224],
[ 6946, 1727, 24225]])
In [1]: mesh.edges_unique[mesh.faces_unique_edges[0:2]]
Out[1]:
array([[[ 1, 6946],
[ 6946, 24224],
[ 1, 24224]],
[[ 1727, 6946],
[ 1727, 24225],
[ 6946, 24225]]])
"""
# make sure we have populated unique edges
populate = 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):
"""
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
"""
euler = int(self.referenced_vertices.sum() -
len(self.edges_unique) +
len(self.faces))
return euler
@caching.cache_decorator
def referenced_vertices(self):
"""
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=np.bool)
referenced[self.faces] = True
return referenced
@property
def units(self):
"""
Definition of units for the mesh.
Returns
----------
units : str
Unit system mesh is in, or None if not defined
"""
if 'units' in self.metadata:
return self.metadata['units']
else:
return None
@units.setter
def units(self, value):
value = str(value).lower()
self.metadata['units'] = value
def convert_units(self, desired, guess=False):
"""
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
def merge_vertices(self, **kwargs):
"""
If a mesh has vertices that are closer than
trimesh.constants.tol.merge reindex faces to reference
the same index for both vertices.
Parameters
-------------
mesh : Trimesh object
Mesh to merge vertices on
use_tex : bool
If True for textured meshes merge vertices
with identical positions AND UV coordinates.
use_norm : bool
If True meshes with vertex normals defined will
only have vertices merged with identical normal
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
"""
if 'textured' in kwargs:
kwargs['merge_tex'] = not kwargs.pop('textured')
log.warning(
'merge_vertices depreciation: `not textured`->`merge_tex`')
grouping.merge_vertices(self, **kwargs)
def update_vertices(self, mask, inverse=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=np.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
def update_faces(self, mask):
"""
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]
def remove_infinite_values(self):
"""
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)
def remove_duplicate_faces(self):
"""
On the current mesh remove any faces which are duplicates.
Alters `self.faces` to remove duplicate faces
"""
unique, inverse = grouping.unique_rows(np.sort(self.faces, axis=1))
self.update_faces(unique)
def rezero(self):
"""
Translate the mesh so that all vertex vertices are positive.
Alters `self.vertices`.
"""
self.apply_translation(self.bounds[0] * -1.0)
@log_time
def split(self, **kwargs):
"""
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):
"""
Find faces that share an edge, which we call here 'adjacent'.
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_adjacency_edges(self):
"""
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
populate = self.face_adjacency
return self._cache['face_adjacency_edges']
@caching.cache_decorator
def face_adjacency_edges_tree(self):
"""
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
"""
from scipy.spatial import cKDTree
return cKDTree(self.face_adjacency_edges)
@caching.cache_decorator
def face_adjacency_angles(self):
"""
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):
"""
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):
"""
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
"""
are_convex = self.face_adjacency_projections < tol.merge
return are_convex
@caching.cache_decorator
def face_adjacency_unshared(self):
"""
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
"""
vid_unshared = graph.face_adjacency_unshared(self)
return vid_unshared
@caching.cache_decorator
def face_adjacency_radius(self):
"""
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, span = graph.face_adjacency_radius(mesh=self)
self._cache['face_adjacency_span'] = span
return radii
@caching.cache_decorator
def face_adjacency_span(self):
"""
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
"""
populate = self.face_adjacency_radius
return self._cache['face_adjacency_span']
@caching.cache_decorator
def vertex_adjacency_graph(self):
"""
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]
"""
adjacency_g = graph.vertex_adjacency_graph(mesh=self)
return adjacency_g
@caching.cache_decorator
def vertex_neighbors(self):
"""
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):
"""
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
populate = self.is_watertight
return self._cache['is_winding_consistent']
@caching.cache_decorator
def is_watertight(self):
"""
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):
"""
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):
"""
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):
"""
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):
"""
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
"""
from scipy.spatial import cKDTree
tree = cKDTree(self.vertices.view(np.ndarray))
return tree
def remove_degenerate_faces(self, height=tol.merge):
"""
Remove degenerate faces (faces without 3 unique vertex indices)
from the current mesh.
If a height is specified, it will remove any face with a 2D oriented
bounding box with one edge shorter than that height.
If not specified, it will remove any face with a zero normal.
Parameters
------------
height : float
If specified removes faces with an oriented bounding
box shorter than this on one side.
Returns
-------------
nondegenerate : (len(self.faces), ) bool
Mask used to remove faces
"""
nondegenerate = triangles.nondegenerate(
self.triangles,
areas=self.area_faces,
height=height)
self.update_faces(nondegenerate)
return nondegenerate
@caching.cache_decorator
def facets(self):
"""
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):
"""
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=np.float64)
return areas
@caching.cache_decorator
def facets_normal(self):
"""
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):
"""
Return a point on the facet plane.
Returns
------------
origins : (len(self.facets), 3) float
A point on each facet plane
"""
populate = self.facets_normal
return self._cache['facets_origin']
@caching.cache_decorator
def facets_boundary(self):
"""
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 = np.array([i[grouping.group_rows(i, require_count=1)]
for i in edges_facet])
return edges_boundary
@caching.cache_decorator
def facets_on_hull(self):
"""
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=np.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=np.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
@log_time
def fix_normals(self, multibody=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)
def fill_holes(self):
"""
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)
def register(self, other, **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
------------
mesh : trimesh.Trimesh object
Mesh to align with other
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
def compute_stable_poses(self,
center_mass=None,
sigma=0.0,
n_samples=1,
threshold=0.0):
"""
Computes stable orientations of a mesh and their quasi-static probabilites.
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 probabilties 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)
def subdivide(self, face_index=None):
"""
Subdivide a mesh, with each subdivided face replaced with four
smaller 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
"""
# subdivide vertex attributes
vertex_attributes = {}
if (hasattr(self.visual, 'uv') and
np.shape(self.visual.uv) == (len(self.vertices), 2)):
# only subdivide if
vertex_attributes['uv'] = self.visual.uv
# perform the subdivision with vertex attributes
vertices, faces, attr = remesh.subdivide(
vertices=self.vertices,
faces=self.faces,
face_index=face_index,
vertex_attributes=vertex_attributes)
# if we had texture reconstruct it here
visual = None
if 'uv' in attr:
# get a copy of the current visuals
visual = self.visual.copy()
# assign the subdivided UV's and remove them
visual.uv = attr.pop('uv')
# create a new mesh
result = Trimesh(
vertices=vertices,
faces=faces,
visual=visual,
vertex_attributes=attr,
process=False)
return result
@log_time
def smoothed(self, **kwargs):
"""
Return a version of the current mesh which will render
nicely, without changing source mesh.
Parameters
-------------
angle : float or None
Angle in radians face pairs with angles
smaller than this will appear smoothed
facet_minarea : float or None
Minimum area fraction to consider
IE for `facets_minarea=25` only facets larger
than `mesh.area / 25` will be considered.
Returns
---------
smoothed : trimesh.Trimesh
Non watertight version of current mesh
which will render nicely with smooth shading
"""
# smooth should be recomputed if visuals change
self.visual._verify_crc()
cached = self.visual._cache['smoothed']
if cached is not None:
return cached
# run smoothing
smoothed = graph.smoothed(
self, **kwargs)
self.visual._cache['smoothed'] = smoothed
return smoothed
@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
def section(self,
plane_normal,
plane_origin,
**kwargs):
"""
Returns a 3D cross section of the current mesh and a plane
defined by origin and normal.
Parameters
------------
plane_normal: (3) vector for plane normal
Normal vector of section plane
plane_origin : (3, ) float
Point on the cross section plane
Returns
---------
intersections: Path3D or None
Curve of intersection
"""
# 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
def section_multiplane(self,
plane_origin,
plane_normal,
heights):
"""
Return multiple parallel cross sections of the current
mesh in 2D.
Parameters
------------
plane_normal: (3) vector for plane normal
Normal vector of section plane
plane_origin : (3, ) float
Point on the cross 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 index, L, T in zip(range(len(lines)),
lines,
transforms):
if len(L) > 0:
paths[index] = load_path(
L, metadata={'to_3D': T})
return paths
def slice_plane(self,
plane_origin,
plane_normal,
**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
Parameters
------------
plane_normal: (3) vector for plane normal
Normal vector of slicing plane
plane_origin : (3, ) float
Point on the slicing plane
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,
**kwargs)
return new_mesh
@caching.cache_decorator
def convex_hull(self):
"""
Returns a Trimesh object representing the convex hull of
the current mesh.
Returns
--------
convex : trimesh.Trimesh
Mesh of convex hull of current mesh
"""
hull = convex.convex_hull(self)
return hull
def sample(self, count, return_index=False):
"""
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.
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)
if return_index:
return samples, index
return samples
def remove_unreferenced_vertices(self):
"""
Remove all vertices in the current mesh which are not
referenced by a face.
"""
referenced = np.zeros(len(self.vertices), dtype=np.bool)
referenced[self.faces] = True
inverse = np.zeros(len(self.vertices), dtype=np.int64)
inverse[referenced] = np.arange(referenced.sum())
self.update_vertices(mask=referenced, inverse=inverse)
def unmerge_vertices(self):
"""
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=np.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'])
def apply_transform(self, matrix):
"""
Transform mesh by a homogeneous transformation matrix.
Does the bookkeeping to avoid recomputing things so this function
should be used rather than directly modifying self.vertices
if possible.
Parameters
------------
matrix : (4, 4) float
Homogeneous transformation matrix
"""
# get c-order float64 matrix
matrix = np.asanyarray(
matrix, order='C', dtype=np.float64)
# only support homogeneous transformations
if matrix.shape != (4, 4):
raise ValueError('Transformation matrix must be (4, 4)!')
# exit early if we've been passed an identity matrix
# np.allclose is surprisingly slow so do this test
elif util.allclose(matrix, np.eye(4), 1e-8):
log.debug('apply_transform passed identity matrix')
return self
# new vertex positions
new_vertices = transformations.transform_points(
self.vertices,
matrix=matrix)
# check to see if the matrix has rotation
# rather than just translation
has_rotation = not util.allclose(
matrix[:3, :3], np.eye(3), atol=1e-6)
# overridden center of mass
if self._center_mass is not None:
self._center_mass = transformations.transform_points(
np.array([self._center_mass, ]),
matrix)[0]
# preserve face normals if we have them stored
new_face_normals = None
if has_rotation and 'face_normals' in self._cache:
# transform face normals by rotation component
new_face_normals = util.unitize(
transformations.transform_points(
self.face_normals,
matrix=matrix,
translate=False))
# preserve vertex normals if we have them stored
new_vertex_normals = None
if has_rotation and 'vertex_normals' in self._cache:
new_vertex_normals = util.unitize(
transformations.transform_points(
self.vertex_normals,
matrix=matrix,
translate=False))
# if transformation flips winding of triangles
if has_rotation and transformations.flips_winding(matrix):
log.debug('transform flips winding')
# fliplr will make array non C contiguous
# which will cause hashes to be more
# expensive than necessary so wrap
self.faces = np.ascontiguousarray(
np.fliplr(self.faces))
# assign the new values
self.vertices = new_vertices
# may be None if we didn't have them previously
self.face_normals = new_face_normals
self.vertex_normals = new_vertex_normals
# preserve normals and topology in cache
# while dumping everything else
self._cache.clear(exclude=[
'face_normals', # transformed by us
'vertex_normals' # also transformed by us
'face_adjacency', # topological
'face_adjacency_edges',
'face_adjacency_unshared',
'edges',
'edges_face',
'edges_sorted',
'edges_unique',
'edges_unique_idx',
'edges_unique_inverse',
'edges_sparse',
'body_count',
'faces_unique_edges',
'euler_number', ])
# set the cache ID with the current hash value
self._cache.id_set()
log.debug('mesh transformed by matrix')
return self
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)
def outline(self, face_ids=None, **kwargs):
"""
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
from .path.exchange.load import _create_path
path = _create_path(**faces_to_path(self,
face_ids,
**kwargs))
return path
@caching.cache_decorator
def area(self):
"""
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):
"""
The area of each face in the mesh.
Returns
---------
area_faces : (n, ) float
Area of each face
"""
area_faces = triangles.area(crosses=self.triangles_cross,
sum=False)
return area_faces
@caching.cache_decorator
def mass_properties(self):
"""
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
"""
mass = triangles.mass_properties(triangles=self.triangles,
crosses=self.triangles_cross,
density=self._density,
center_mass=self._center_mass,
skip_inertia=False)
# if magical clean-up mode is enabled
# and mesh is watertight/wound correctly but with negative
# volume it means that every triangle is probably facing
# inwards, so we invert it in-place without dumping cache
if (self.is_watertight and
self.is_winding_consistent and
np.linalg.det(mass['inertia']) < 0.0 and
mass['mass'] < 0.0 and
mass['volume'] < 0.0):
# negate mass properties so we don't need to recalculate
mass['inertia'] = -mass['inertia']
mass['mass'] = -mass['mass']
mass['volume'] = -mass['volume']
return mass
def invert(self):
"""
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'])
def scene(self, **kwargs):
"""
Returns a Scene object containing the current mesh.
Returns
---------
scene : trimesh.scene.scene.Scene
Contains just the current mesh
"""
return Scene(self, **kwargs)
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)
def submesh(self, faces_sequence, **kwargs):
"""
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
---------
if append : trimesh.Trimesh object
else : list of trimesh.Trimesh objects
"""
return util.submesh(mesh=self,
faces_sequence=faces_sequence,
**kwargs)
@caching.cache_decorator
def identifier(self):
"""
Return a float vector which is unique to the mesh
and is robust to rotation and translation.
Returns
-----------
identifier : (6, ) float
Identifying properties of the current mesh
"""
identifier = comparison.identifier_simple(self)
return identifier
@caching.cache_decorator
def identifier_md5(self):
"""
An MD5 of the rotation invariant identifier vector
Returns
---------
hashed : str
MD5 hash of the identifier vector
"""
hashed = comparison.identifier_hash(self.identifier)
return hashed
def export(self, file_obj=None, file_type=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, if you would like this function to 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)
def to_dict(self):
"""
Return a dictionary representation of the current mesh, with keys
that can be used as the kwargs for the Trimesh constructor, eg:
a = Trimesh(**other_mesh.to_dict())
Returns
----------
result : dict
With keys that match trimesh constructor
"""
result = self.export(file_type='dict')
return result
def convex_decomposition(self, maxhulls=20, **kwargs):
"""
Compute an approximate convex decomposition of a mesh.
testVHACD Parameters which can be passed as kwargs:
Name Default
-----------------------------------------------------
resolution 100000
max. concavity 0.001
plane down-sampling 4
convex-hull down-sampling 4
alpha 0.05
beta 0.05
maxhulls 10
pca 0
mode 0
max. vertices per convex-hull 64
min. volume to add vertices to convex-hulls 0.0001
convex-hull approximation 1
OpenCL acceleration 1
OpenCL platform ID 0
OpenCL device ID 0
output output.wrl
log log.txt
Parameters
------------
maxhulls : int
Maximum number of convex hulls to return
**kwargs : testVHACD keyword arguments
Returns
-------
meshes : list of trimesh.Trimesh
List of convex meshes that approximate the original
"""
result = decomposition.convex_decomposition(self,
maxhulls=maxhulls,
**kwargs)
return result
def union(self, other, engine=None, **kwargs):
"""
Boolean union between this mesh and n other meshes
Parameters
------------
other : Trimesh or (n, ) Trimesh
Other meshes to union
engine : None or str
Which backend to use
Returns
---------
union : trimesh.Trimesh
Union of self and other Trimesh objects
"""
result = boolean.union(
meshes=np.append(self, other),
engine=engine,
**kwargs)
return result
def difference(self, other, engine=None, **kwargs):
"""
Boolean difference between this mesh and n other meshes
Parameters
------------
other : trimesh.Trimesh, or list of trimesh.Trimesh objects
Meshes to difference
Returns
---------
difference : trimesh.Trimesh
Difference between self and other Trimesh objects
"""
result = boolean.difference(meshes=np.append(self, other),
engine=engine, **kwargs)
return result
def intersection(self, other, engine=None, **kwargs):
"""
Boolean intersection between this mesh and n other meshes
Parameters
------------
other : trimesh.Trimesh, or list of trimesh.Trimesh objects
Meshes to calculate intersections with
Returns
---------
intersection : trimesh.Trimesh
Mesh of the volume contained by all passed meshes
"""
result = boolean.intersection(meshes=np.append(self, other),
engine=engine, **kwargs)
return result
def contains(self, points):
"""
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
"""
if not self.is_watertight:
log.warning('Mesh is non-watertight for contained point query!')
contains = self.ray.contains_points(points)
return contains
@caching.cache_decorator
def face_angles(self):
"""
Returns the angle at each vertex of a face.
Returns
--------
angles : (len(self.faces), 3) float
Angle at each vertex of a face
"""
angles = triangles.angles(self.triangles)
return angles
@caching.cache_decorator
def face_angles_sparse(self):
"""
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):
"""
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):
"""
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):
"""
An R-tree of face adjacencies.
Returns
--------
tree: rtree.index
Where each edge in self.face_adjacency has a
rectangular cell
"""
# the (n,6) interleaved bounding box for every line segment
segment_bounds = np.column_stack((
self.vertices[self.face_adjacency_edges].min(axis=1),
self.vertices[self.face_adjacency_edges].max(axis=1)))
tree = util.bounds_tree(segment_bounds)
return tree
def copy(self, include_cache=False):
"""
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)
# get center_mass and density
if self._center_mass is not None:
copied.center_mass = self.center_mass
copied._density = self._density
# 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):
# interpret deep copy as "get rid of cached data"
return self.copy(include_cache=False)
def __copy__(self, *args):
# interpret shallow copy as "keep cached data"
return self.copy(include_cache=True)
def eval_cached(self, statement, *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])
"""
statement = str(statement)
key = 'eval_cached_' + statement
key += '_'.join(str(i) for i in args)
if key in self._cache:
return self._cache[key]
result = eval(statement)
self._cache[key] = result
return result
def __hash__(self):
"""
Return the MD5 hash of the mesh as an integer.
Returns
----------
hashed : int
MD5 of mesh data
"""
hashed = int(self.md5(), 16)
return hashed
def __add__(self, other):
"""
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
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