577 lines
17 KiB
Python
577 lines
17 KiB
Python
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"""Parsing functions for Binvox files.
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https://www.patrickmin.com/binvox/binvox.html
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Exporting meshes as binvox files requires binvox CL tool to be on your path.
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"""
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import os
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import subprocess
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import numpy as np
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import collections
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from distutils.spawn import find_executable
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from .. import util
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from ..base import Trimesh
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# find the executable
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binvox_encoder = find_executable('binvox')
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Binvox = collections.namedtuple(
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'Binvox', ['rle_data', 'shape', 'translate', 'scale'])
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def parse_binvox_header(fp):
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"""
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Read the header from a binvox file.
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Spec available:
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https://www.patrickmin.com/binvox/binvox.html
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Parameters
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------------
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fp: file-object
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File like object with binvox file
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Returns
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----------
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shape : tuple
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Shape of binvox according to binvox spec
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translate : tuple
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Translation
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scale : float
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Scale of voxels
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Raises
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------------
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IOError
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If invalid binvox file.
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"""
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line = fp.readline().strip()
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if hasattr(line, 'decode'):
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binvox = b'#binvox'
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space = b' '
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else:
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binvox = '#binvox'
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space = ' '
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if not line.startswith(binvox):
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raise IOError('Not a binvox file')
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shape = tuple(
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int(s) for s in fp.readline().strip().split(space)[1:])
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translate = tuple(
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float(s) for s in fp.readline().strip().split(space)[1:])
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scale = float(fp.readline().strip().split(space)[1])
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fp.readline()
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return shape, translate, scale
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def parse_binvox(fp, writeable=False):
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"""
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Read a binvox file, spec at
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https://www.patrickmin.com/binvox/binvox.html
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Parameters
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------------
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fp: file-object
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File like object with binvox file
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Returns
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----------
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binvox : namedtuple
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Containing data
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rle : numpy array
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Run length encoded data
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Raises
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------------
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IOError
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If invalid binvox file
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"""
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# get the header info
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shape, translate, scale = parse_binvox_header(fp)
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# get the rest of the file
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data = fp.read()
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# convert to numpy array
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rle_data = np.frombuffer(data, dtype=np.uint8)
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if writeable:
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rle_data = rle_data.copy()
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return Binvox(rle_data, shape, translate, scale)
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_binvox_header = '''#binvox 1
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dim {sx} {sy} {sz}
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translate {tx} {ty} {tz}
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scale {scale}
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data
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'''
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def binvox_header(shape, translate, scale):
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"""
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Get a binvox header string.
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Parameters
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--------
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shape: length 3 iterable of ints denoting shape of voxel grid.
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translate: length 3 iterable of floats denoting translation.
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scale: num length of entire voxel grid.
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Returns
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--------
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string including "data\n" line.
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"""
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sx, sy, sz = (int(s) for s in shape)
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tx, ty, tz = translate
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return _binvox_header.format(
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sx=sx, sy=sy, sz=sz, tx=tx, ty=ty, tz=tz, scale=scale)
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def binvox_bytes(rle_data, shape, translate=(0, 0, 0), scale=1):
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"""Get a binary representation of binvox data.
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Parameters
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--------
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rle_data : numpy array
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Run-length encoded numpy array.
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shape : (3,) int
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Shape of voxel grid.
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translate : (3,) float
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Translation of voxels
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scale : float
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Length of entire voxel grid.
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Returns
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--------
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data : bytes
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Suitable for writing to binary file
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"""
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if rle_data.dtype != np.uint8:
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raise ValueError(
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"rle_data.dtype must be np.uint8, got %s" % rle_data.dtype)
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header = binvox_header(shape, translate, scale).encode()
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return header + rle_data.tostring()
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def voxel_from_binvox(
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rle_data, shape, translate=None, scale=1.0, axis_order='xzy'):
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"""
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Factory for building from data associated with binvox files.
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Parameters
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---------
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rle_data : numpy
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Run-length-encoded of flat voxel
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values, or a `trimesh.rle.RunLengthEncoding` object.
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See `trimesh.rle` documentation for description of encoding
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shape : (3,) int
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Shape of voxel grid.
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translate : (3,) float
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Translation of voxels
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scale : float
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Length of entire voxel grid.
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encoded_axes : iterable
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With values in ('x', 'y', 'z', 0, 1, 2),
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where x => 0, y => 1, z => 2
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denoting the order of axes in the encoded data. binvox by
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default saves in xzy order, but using `xyz` (or (0, 1, 2)) will
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be faster in some circumstances.
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Returns
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---------
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result : VoxelGrid
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Loaded voxels
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"""
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# shape must be uniform else scale is ambiguous
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from ..voxel import encoding as enc
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from ..voxel.base import VoxelGrid
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from .. import transformations
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if isinstance(rle_data, enc.RunLengthEncoding):
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encoding = rle_data
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else:
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encoding = enc.RunLengthEncoding(rle_data, dtype=bool)
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# translate = np.asanyarray(translate) * scale)
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# translate = [0, 0, 0]
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transform = transformations.scale_and_translate(
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scale=scale / (np.array(shape) - 1),
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translate=translate)
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if axis_order == 'xzy':
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perm = (0, 2, 1)
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shape = tuple(shape[p] for p in perm)
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encoding = encoding.reshape(shape).transpose(perm)
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elif axis_order is None or axis_order == 'xyz':
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encoding = encoding.reshape(shape)
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else:
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raise ValueError(
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"Invalid axis_order '%s': must be None, 'xyz' or 'xzy'")
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assert(encoding.shape == shape)
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return VoxelGrid(encoding, transform)
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def load_binvox(file_obj,
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resolver=None,
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axis_order='xzy',
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file_type=None):
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"""
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Load trimesh `VoxelGrid` instance from file.
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Parameters
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-----------
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file_obj : file-like object
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Contains binvox data
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resolver : unused
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axis_order : str
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Order of axes in encoded data.
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Binvox default is 'xzy', but 'xyz' may be faster
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where this is not relevant.
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Returns
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---------
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result : trimesh.voxel.VoxelGrid
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Loaded voxel data
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"""
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if file_type is not None and file_type != 'binvox':
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raise ValueError(
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'file_type must be None or binvox, got %s' % file_type)
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data = parse_binvox(file_obj, writeable=True)
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return voxel_from_binvox(
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rle_data=data.rle_data,
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shape=data.shape,
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translate=data.translate,
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scale=data.scale,
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axis_order=axis_order)
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def export_binvox(voxel, axis_order='xzy'):
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"""
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Export `trimesh.voxel.VoxelGrid` instance to bytes
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Parameters
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------------
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voxel : `trimesh.voxel.VoxelGrid`
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Assumes axis ordering of `xyz` and encodes
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in binvox default `xzy` ordering.
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axis_order : str
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Eements in ('x', 'y', 'z', 0, 1, 2), the order
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of axes to encode data (standard is 'xzy' for binvox). `voxel`
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data is assumed to be in order 'xyz'.
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Returns
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-----------
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result : bytes
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Representation according to binvox spec
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"""
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translate = voxel.translation
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scale = voxel.scale * ((np.array(voxel.shape) - 1))
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neg_scale, = np.where(scale < 0)
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encoding = voxel.encoding.flip(neg_scale)
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scale = np.abs(scale)
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if not util.allclose(scale[0], scale[1:], 1e-6 * scale[0] + 1e-8):
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raise ValueError('Can only export binvox with uniform scale')
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scale = scale[0]
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if axis_order == 'xzy':
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encoding = encoding.transpose((0, 2, 1))
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elif axis_order != 'xyz':
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raise ValueError('Invalid axis_order: must be one of ("xyz", "xzy")')
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rle_data = encoding.flat.run_length_data(dtype=np.uint8)
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return binvox_bytes(
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rle_data, shape=voxel.shape, translate=translate, scale=scale)
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class Binvoxer(object):
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"""
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Interface for binvox CL tool.
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This class is responsible purely for making calls to the CL tool. It
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makes no attempt to integrate with the rest of trimesh at all.
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Constructor args configure command line options.
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`Binvoxer.__call__` operates on the path to a mode file.
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If using this interface in published works, please cite the references
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below.
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See CL tool website for further details.
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https://www.patrickmin.com/binvox/
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@article{nooruddin03,
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author = {Fakir S. Nooruddin and Greg Turk},
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title = {Simplification and Repair of Polygonal Models Using Volumetric
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Techniques},
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journal = {IEEE Transactions on Visualization and Computer Graphics},
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volume = {9},
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number = {2},
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pages = {191--205},
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year = {2003}
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}
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@Misc{binvox,
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author = {Patrick Min},
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title = {binvox},
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howpublished = {{\tt http://www.patrickmin.com/binvox} or
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{\tt https://www.google.com/search?q=binvox}},
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year = {2004 - 2019},
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note = {Accessed: yyyy-mm-dd}
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}
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"""
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SUPPORTED_INPUT_TYPES = (
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'ug',
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'obj',
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'off',
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'dfx',
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'xgl',
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'pov',
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'brep',
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'ply',
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'jot',
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)
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SUPPORTED_OUTPUT_TYPES = (
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'binvox',
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'hips',
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'mira',
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'vtk',
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'raw',
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'schematic',
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'msh',
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)
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def __init__(
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self,
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dimension=32,
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file_type='binvox',
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z_buffer_carving=True,
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z_buffer_voting=True,
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dilated_carving=False,
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exact=False,
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bounding_box=None,
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remove_internal=False,
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center=False,
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rotate_x=0,
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rotate_z=0,
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wireframe=False,
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fit=False,
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block_id=None,
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use_material_block_id=False,
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use_offscreen_pbuffer=True,
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downsample_factor=None,
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downsample_threshold=None,
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verbose=False,
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binvox_path=binvox_encoder,
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):
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"""
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Configure the voxelizer.
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Parameters
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------------
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dimension: voxel grid size (max 1024 when not using exact)
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file_type: str
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Output file type, supported types are:
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'binvox'
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'hips'
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'mira'
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'vtk'
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'raw'
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'schematic'
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'msh'
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z_buffer_carving : use z buffer based carving. At least one of
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`z_buffer_carving` and `z_buffer_voting` must be True.
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z_buffer_voting: use z-buffer based parity voting method.
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dilated_carving: stop carving 1 voxel before intersection.
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exact: any voxel with part of a triangle gets set. Does not use
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graphics card.
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bounding_box: 6-element float list/tuple of min, max values,
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(minx, miny, minz, maxx, maxy, maxz)
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remove_internal: remove internal voxels if True. Note there is some odd
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behaviour if boundary voxels are occupied.
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center: center model inside unit cube.
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rotate_x: number of 90 degree ccw rotations around x-axis before
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voxelizing.
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rotate_z: number of 90 degree cw rotations around z-axis before
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voxelizing.
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wireframe: also render the model in wireframe (helps with thin parts).
|
||
|
|
fit: only write voxels in the voxel bounding box.
|
||
|
|
block_id: when converting to schematic, use this as the block ID.
|
||
|
|
use_matrial_block_id: when converting from obj to schematic, parse
|
||
|
|
block ID from material spec "usemtl blockid_<id>" (ids 1-255 only).
|
||
|
|
use_offscreen_pbuffer: use offscreen pbuffer instead of onscreen
|
||
|
|
window.
|
||
|
|
downsample_factor: downsample voxels by this factor in each dimension.
|
||
|
|
Must be a power of 2 or None. If not None/1 and `core dumped`
|
||
|
|
errors occur, try slightly adjusting dimensions.
|
||
|
|
downsample_threshold: when downsampling, destination voxel is on if
|
||
|
|
more than this number of voxels are on.
|
||
|
|
verbose: if False, silences stdout/stderr from subprocess call.
|
||
|
|
binvox_path: path to binvox executable. The default looks for an
|
||
|
|
executable called `binvox` on your `PATH`.
|
||
|
|
"""
|
||
|
|
if binvox_encoder is None:
|
||
|
|
raise IOError(
|
||
|
|
'No `binvox_path` provided, and no binvox executable found '
|
||
|
|
'on PATH. \nPlease go to https://www.patrickmin.com/binvox/ and '
|
||
|
|
'download the appropriate version.')
|
||
|
|
|
||
|
|
if dimension > 1024 and not exact:
|
||
|
|
raise ValueError(
|
||
|
|
'Maximum dimension using exact is 1024, got %d' % dimension)
|
||
|
|
if file_type not in Binvoxer.SUPPORTED_OUTPUT_TYPES:
|
||
|
|
raise ValueError(
|
||
|
|
'file_type %s not in set of supported output types %s' %
|
||
|
|
(file_type, str(Binvoxer.SUPPORTED_OUTPUT_TYPES)))
|
||
|
|
args = [binvox_path, '-d', str(dimension), '-t', file_type]
|
||
|
|
if exact:
|
||
|
|
args.append('-e')
|
||
|
|
if z_buffer_carving:
|
||
|
|
if z_buffer_voting:
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
args.append('-c')
|
||
|
|
elif z_buffer_voting:
|
||
|
|
args.append('-v')
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
'At least one of `z_buffer_carving` or `z_buffer_voting` must '
|
||
|
|
'be True')
|
||
|
|
if dilated_carving:
|
||
|
|
args.append('-dc')
|
||
|
|
|
||
|
|
# Additional parameters
|
||
|
|
if bounding_box is not None:
|
||
|
|
if len(bounding_box) != 6:
|
||
|
|
raise ValueError('bounding_box must have 6 elements')
|
||
|
|
args.append('-bb')
|
||
|
|
args.extend(str(b) for b in bounding_box)
|
||
|
|
if remove_internal:
|
||
|
|
args.append('-ri')
|
||
|
|
if center:
|
||
|
|
args.append('-cb')
|
||
|
|
args.extend(('-rotx',) * rotate_x)
|
||
|
|
args.extend(('-rotz',) * rotate_z)
|
||
|
|
if wireframe:
|
||
|
|
args.append('-aw')
|
||
|
|
if fit:
|
||
|
|
args.append('-fit')
|
||
|
|
if block_id is not None:
|
||
|
|
args.extend(('-bi', block_id))
|
||
|
|
if use_material_block_id:
|
||
|
|
args.append('-mb')
|
||
|
|
if use_offscreen_pbuffer:
|
||
|
|
args.append('-pb')
|
||
|
|
if downsample_factor is not None:
|
||
|
|
times = np.log2(downsample_factor)
|
||
|
|
if int(times) != times:
|
||
|
|
raise ValueError(
|
||
|
|
'downsample_factor must be a power of 2, got %d'
|
||
|
|
% downsample_factor)
|
||
|
|
args.extend(('-down',) * int(times))
|
||
|
|
if downsample_threshold is not None:
|
||
|
|
args.extend(('-dmin', str(downsample_threshold)))
|
||
|
|
args.append('PATH')
|
||
|
|
self._args = args
|
||
|
|
self._file_type = file_type
|
||
|
|
|
||
|
|
self.verbose = verbose
|
||
|
|
|
||
|
|
@property
|
||
|
|
def file_type(self):
|
||
|
|
return self._file_type
|
||
|
|
|
||
|
|
def __call__(self, path, overwrite=False):
|
||
|
|
"""
|
||
|
|
Create an voxel file in the same directory as model at `path`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
------------
|
||
|
|
path: string path to model file. Supported types:
|
||
|
|
'ug'
|
||
|
|
'obj'
|
||
|
|
'off'
|
||
|
|
'dfx'
|
||
|
|
'xgl'
|
||
|
|
'pov'
|
||
|
|
'brep'
|
||
|
|
'ply'
|
||
|
|
'jot' (polygongs only)
|
||
|
|
overwrite: if False, checks the output path (head.file_type) is empty
|
||
|
|
before running. If True and a file exists, raises an IOError.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
------------
|
||
|
|
string path to voxel file. File type give by file_type in constructor.
|
||
|
|
"""
|
||
|
|
head, ext = os.path.splitext(path)
|
||
|
|
ext = ext[1:].lower()
|
||
|
|
if ext not in Binvoxer.SUPPORTED_INPUT_TYPES:
|
||
|
|
raise ValueError(
|
||
|
|
'file_type %s not in set of supported input types %s' %
|
||
|
|
(ext, str(Binvoxer.SUPPORTED_INPUT_TYPES)))
|
||
|
|
out_path = '%s.%s' % (head, self._file_type)
|
||
|
|
if os.path.isfile(out_path) and not overwrite:
|
||
|
|
raise IOError(
|
||
|
|
'Attempted to voxelize object a %s, but there is already a '
|
||
|
|
'file at output path %s' % (path, out_path))
|
||
|
|
self._args[-1] = path
|
||
|
|
|
||
|
|
# generalizes to python2 and python3
|
||
|
|
# will capture terminal output into variable rather than printing
|
||
|
|
verbosity = subprocess.check_output(self._args)
|
||
|
|
# if requested print ourselves
|
||
|
|
if self.verbose:
|
||
|
|
print(verbosity)
|
||
|
|
|
||
|
|
return out_path
|
||
|
|
|
||
|
|
|
||
|
|
def voxelize_mesh(mesh,
|
||
|
|
binvoxer=None,
|
||
|
|
export_type='off',
|
||
|
|
**binvoxer_kwargs):
|
||
|
|
"""
|
||
|
|
Interface for voxelizing Trimesh object via the binvox tool.
|
||
|
|
|
||
|
|
Implementation simply saved the mesh in the specified export_type then
|
||
|
|
runs the `Binvoxer.__call__` (using either the supplied `binvoxer` or
|
||
|
|
creating one via `binvoxer_kwargs`)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
------------
|
||
|
|
mesh: Trimesh object to voxelize.
|
||
|
|
binvoxer: optional Binvoxer instance.
|
||
|
|
export_type: file type to export mesh as temporarily for Binvoxer to
|
||
|
|
operate on.
|
||
|
|
**binvoxer_kwargs: kwargs for creating a new Binvoxer instance. If binvoxer
|
||
|
|
if provided, this must be empty.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
------------
|
||
|
|
`VoxelGrid` object resulting.
|
||
|
|
"""
|
||
|
|
if not isinstance(mesh, Trimesh):
|
||
|
|
raise ValueError('mesh must be Trimesh instance, got %s' % str(mesh))
|
||
|
|
if binvoxer is None:
|
||
|
|
binvoxer = Binvoxer(**binvoxer_kwargs)
|
||
|
|
elif len(binvoxer_kwargs) > 0:
|
||
|
|
raise ValueError('Cannot provide binvoxer and binvoxer_kwargs')
|
||
|
|
if binvoxer.file_type != 'binvox':
|
||
|
|
raise ValueError(
|
||
|
|
'Only "binvox" binvoxer `file_type` currently supported')
|
||
|
|
with util.TemporaryDirectory() as folder:
|
||
|
|
model_path = os.path.join(folder, 'model.%s' % export_type)
|
||
|
|
with open(model_path, 'wb') as fp:
|
||
|
|
mesh.export(fp, file_type=export_type)
|
||
|
|
out_path = binvoxer(model_path)
|
||
|
|
with open(out_path, 'rb') as fp:
|
||
|
|
out_model = load_binvox(fp)
|
||
|
|
return out_model
|
||
|
|
|
||
|
|
|
||
|
|
_binvox_loaders = {'binvox': load_binvox}
|