396 lines
12 KiB
Python
396 lines
12 KiB
Python
"""
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voxel.py
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-----------
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Convert meshes to a simple voxel data structure and back again.
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"""
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import numpy as np
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from . import ops
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from . import transforms
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from . import morphology
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from . import encoding as enc
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from .. import bounds as bounds_module
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from .. import caching
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from .. import transformations as tr
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from ..parent import Geometry
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from ..constants import log
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class VoxelGrid(Geometry):
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"""
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Store 3D voxels.
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"""
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def __init__(self, encoding, transform=None, metadata=None):
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if transform is None:
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transform = np.eye(4)
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if isinstance(encoding, np.ndarray):
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encoding = enc.DenseEncoding(encoding.astype(bool))
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if encoding.dtype != bool:
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raise ValueError('encoding must have dtype bool')
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self._data = caching.DataStore()
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self.encoding = encoding
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self._data['transform'] = transforms.Transform(transform)
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self._cache = caching.Cache(id_function=self._data.crc)
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self.metadata = dict()
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# update the mesh metadata with passed metadata
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if isinstance(metadata, dict):
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self.metadata.update(metadata)
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elif metadata is not None:
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raise ValueError(
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'metadata should be a dict or None, got %s' % str(metadata))
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def md5(self):
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return self._data.md5()
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def crc(self):
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return self._data.crc()
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@property
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def encoding(self):
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"""
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`Encoding` object providing the occupancy grid.
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See `trimesh.voxel.encoding` for implementations.
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"""
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return self._data['encoding']
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@encoding.setter
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def encoding(self, encoding):
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if isinstance(encoding, np.ndarray):
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encoding = enc.DenseEncoding(encoding)
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elif not isinstance(encoding, enc.Encoding):
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raise ValueError(
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'encoding must be an Encoding, got %s' % str(encoding))
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if len(encoding.shape) != 3:
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raise ValueError(
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'encoding must be rank 3, got shape %s' % str(encoding.shape))
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if encoding.dtype != bool:
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raise ValueError(
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'encoding must be binary, got %s' % encoding.dtype)
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self._data['encoding'] = encoding
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@property
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def _transform(self):
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return self._data['transform']
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@property
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def transform(self):
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"""4x4 homogeneous transformation matrix."""
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return self._transform.matrix
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@transform.setter
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def transform(self, matrix):
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"""4x4 homogeneous transformation matrix."""
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self._transform.matrix = matrix
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@property
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def translation(self):
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"""Location of voxel at [0, 0, 0]."""
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return self._transform.translation
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@property
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def origin(self):
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"""Deprecated. Use `self.translation`."""
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# DEPRECATED. Use translation instead
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return self.translation
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@property
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def scale(self):
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"""
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3-element float representing per-axis scale.
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Raises a `RuntimeError` if `self.transform` has rotation or
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shear components.
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"""
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return self._transform.scale
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@property
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def pitch(self):
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"""
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Uniform scaling factor representing the side length of
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each voxel.
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Returns
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-----------
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pitch : float
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Pitch of the voxels.
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Raises
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------------
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`RuntimeError`
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If `self.transformation` has rotation or shear
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components of has non-uniform scaling.
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"""
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return self._transform.pitch
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@property
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def element_volume(self):
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return self._transform.unit_volume
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def apply_transform(self, matrix):
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self._transform.apply_transform(matrix)
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return self
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def strip(self):
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"""
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Mutate self by stripping leading/trailing planes of zeros.
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Returns
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--------
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self after mutation occurs in-place
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"""
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encoding, padding = self.encoding.stripped
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self.encoding = encoding
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self._transform.matrix[:3, 3] = self.indices_to_points(padding[:, 0])
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return self
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@caching.cache_decorator
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def bounds(self):
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indices = self.sparse_indices
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# get all 8 corners of the AABB
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corners = bounds_module.corners([indices.min(axis=0) - 0.5,
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indices.max(axis=0) + 0.5])
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# transform these corners to a new frame
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corners = self._transform.transform_points(corners)
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# get the AABB of corners in-frame
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bounds = np.array([corners.min(axis=0), corners.max(axis=0)])
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bounds.flags.writeable = False
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return bounds
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@caching.cache_decorator
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def extents(self):
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bounds = self.bounds
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extents = bounds[1] - bounds[0]
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extents.flags.writeable = False
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return extents
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@caching.cache_decorator
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def is_empty(self):
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return self.encoding.is_empty
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@property
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def shape(self):
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"""3-tuple of ints denoting shape of occupancy grid."""
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return self.encoding.shape
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@caching.cache_decorator
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def filled_count(self):
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"""int, number of occupied voxels in the grid."""
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return self.encoding.sum.item()
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def is_filled(self, point):
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"""
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Query points to see if the voxel cells they lie in are
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filled or not.
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Parameters
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----------
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point : (n, 3) float
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Points in space
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Returns
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---------
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is_filled : (n,) bool
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Is cell occupied or not for each point
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"""
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point = np.asanyarray(point)
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indices = self.points_to_indices(point)
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in_range = np.logical_and(
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np.all(indices < np.array(self.shape), axis=-1),
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np.all(indices >= 0, axis=-1))
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is_filled = np.zeros_like(in_range)
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is_filled[in_range] = self.encoding.gather_nd(indices[in_range])
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return is_filled
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def fill(self, method='holes', **kwargs):
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"""
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Mutates self by filling in the encoding according to `morphology.fill`.
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Parameters
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----------
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method: implementation key, one of
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`trimesh.voxel.morphology.fill.fillers` keys
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**kwargs: additional kwargs passed to the keyed implementation
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Returns
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----------
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self after replacing encoding with a filled version.
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"""
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self.encoding = morphology.fill(self.encoding, method=method, **kwargs)
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return self
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def hollow(self, structure=None):
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"""
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Mutates self by removing internal voxels leaving only surface elements.
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Surviving elements are those in encoding that are adjacent to an empty
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voxel, where adjacency is controlled by `structure`.
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Parameters
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----------
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structure: adjacency structure. If None, square connectivity is used.
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Returns
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----------
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self after replacing encoding with a surface version.
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"""
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self.encoding = morphology.surface(self.encoding)
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return self
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@caching.cache_decorator
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def marching_cubes(self):
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"""
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A marching cubes Trimesh representation of the voxels.
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No effort was made to clean or smooth the result in any way;
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it is merely the result of applying the scikit-image
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measure.marching_cubes function to self.encoding.dense.
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Returns
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---------
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meshed: Trimesh object representing the current voxel
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object, as returned by marching cubes algorithm.
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"""
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meshed = ops.matrix_to_marching_cubes(matrix=self.matrix)
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return meshed
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@property
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def matrix(self):
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"""
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Return a DENSE matrix of the current voxel encoding
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Returns
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-------------
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dense : (a, b, c) bool
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Numpy array of dense matrix
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Shortcut to voxel.encoding.dense
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"""
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return self.encoding.dense
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@caching.cache_decorator
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def volume(self):
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"""
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What is the volume of the filled cells in the current voxel object.
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Returns
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---------
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volume: float, volume of filled cells
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"""
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return self.filled_count * self.element_volume
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@caching.cache_decorator
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def points(self):
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"""
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The center of each filled cell as a list of points.
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Returns
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----------
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points: (self.filled, 3) float, list of points
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"""
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return self._transform.transform_points(
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self.sparse_indices.astype(float))
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@property
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def sparse_indices(self):
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"""(n, 3) int array of sparse indices of occupied voxels."""
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return self.encoding.sparse_indices
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def as_boxes(self, colors=None, **kwargs):
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"""
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A rough Trimesh representation of the voxels with a box
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for each filled voxel.
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Parameters
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----------
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colors : (3,) or (4,) float or uint8
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(X, Y, Z, 3) or (X, Y, Z, 4) float or uint8
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Where matrix.shape == (X, Y, Z)
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Returns
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---------
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mesh : trimesh.Trimesh
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Mesh with one box per filled cell.
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"""
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if colors is not None:
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colors = np.asanyarray(colors)
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if colors.ndim == 4:
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encoding = self.encoding
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if colors.shape[:3] == encoding.shape:
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# TODO jackd: more efficient implementation?
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# encoding.as_mask?
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colors = colors[encoding.dense]
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else:
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log.warning('colors incorrect shape!')
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colors = None
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elif colors.shape not in ((3,), (4,)):
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log.warning('colors incorrect shape!')
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colors = None
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mesh = ops.multibox(
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centers=self.sparse_indices.astype(float), colors=colors)
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mesh = mesh.apply_transform(self.transform)
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return mesh
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def points_to_indices(self, points):
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"""
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Convert points to indices in the matrix array.
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Parameters
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----------
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points: (n, 3) float, point in space
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Returns
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---------
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indices: (n, 3) int array of indices into self.encoding
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"""
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points = self._transform.inverse_transform_points(points)
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return np.round(points).astype(int)
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def indices_to_points(self, indices):
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return self._transform.transform_points(indices.astype(float))
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def show(self, *args, **kwargs):
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"""
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Convert the current set of voxels into a trimesh for visualization
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and show that via its built- in preview method.
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"""
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return self.as_boxes(kwargs.pop(
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'colors', None)).show(*args, **kwargs)
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def copy(self):
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return VoxelGrid(self.encoding.copy(),
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self._transform.matrix.copy())
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def revoxelized(self, shape):
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"""
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Create a new VoxelGrid without rotations, reflections or shearing.
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Parameters
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----------
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shape: 3-tuple of ints denoting the shape of the returned VoxelGrid.
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Returns
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----------
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VoxelGrid of the given shape with (possibly non-uniform) scale and
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translation transformation matrix.
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"""
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from .. import util
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shape = tuple(shape)
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bounds = self.bounds.copy()
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extents = self.extents
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points = util.grid_linspace(bounds, shape).reshape(shape + (3,))
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dense = self.is_filled(points)
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scale = extents / np.asanyarray(shape)
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translate = bounds[0]
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return VoxelGrid(
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dense,
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transform=tr.scale_and_translate(scale, translate))
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