chrischoy/binvox_rw.py

## binvox_rw.py
#  Copyright (C) 2012 Daniel Maturana
#  This file is part of binvox-rw-py.
#
#  binvox-rw-py is free software: you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation, either version 3 of the License, or
#  (at your option) any later version.
#
#  binvox-rw-py is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.
#
#  You should have received a copy of the GNU General Public License
#  along with binvox-rw-py. If not, see <http://www.gnu.org/licenses/>.
#
#  Modified by Christopher B. Choy <chrischoy at ai dot stanford dot edu>
#  for python 3 support

"""
Binvox to Numpy and back.


>>> import numpy as np
>>> import binvox_rw
>>> with open('chair.binvox', 'rb') as f:
...     m1 = binvox_rw.read_as_3d_array(f)
...
>>> m1.dims
[32, 32, 32]
>>> m1.scale
41.133000000000003
>>> m1.translate
[0.0, 0.0, 0.0]
>>> with open('chair_out.binvox', 'wb') as f:
...     m1.write(f)
...
>>> with open('chair_out.binvox', 'rb') as f:
...     m2 = binvox_rw.read_as_3d_array(f)
...
>>> m1.dims==m2.dims
True
>>> m1.scale==m2.scale
True
>>> m1.translate==m2.translate
True
>>> np.all(m1.data==m2.data)
True

>>> with open('chair.binvox', 'rb') as f:
...     md = binvox_rw.read_as_3d_array(f)
...
>>> with open('chair.binvox', 'rb') as f:
...     ms = binvox_rw.read_as_coord_array(f)
...
>>> data_ds = binvox_rw.dense_to_sparse(md.data)
>>> data_sd = binvox_rw.sparse_to_dense(ms.data, 32)
>>> np.all(data_sd==md.data)
True
>>> # the ordering of elements returned by numpy.nonzero changes with axis
>>> # ordering, so to compare for equality we first lexically sort the voxels.
>>> np.all(ms.data[:, np.lexsort(ms.data)] == data_ds[:, np.lexsort(data_ds)])
True
"""

import numpy as np

class Voxels(object):
    """ Holds a binvox model.
    data is either a three-dimensional numpy boolean array (dense representation)
    or a two-dimensional numpy float array (coordinate representation).

    dims, translate and scale are the model metadata.

    dims are the voxel dimensions, e.g. [32, 32, 32] for a 32x32x32 model.

    scale and translate relate the voxels to the original model coordinates.

    To translate voxel coordinates i, j, k to original coordinates x, y, z:

    x_n = (i+.5)/dims[0]
    y_n = (j+.5)/dims[1]
    z_n = (k+.5)/dims[2]
    x = scale*x_n + translate[0]
    y = scale*y_n + translate[1]
    z = scale*z_n + translate[2]

    """

    def __init__(self, data, dims, translate, scale, axis_order):
        self.data = data
        self.dims = dims
        self.translate = translate
        self.scale = scale
        assert (axis_order in ('xzy', 'xyz'))
        self.axis_order = axis_order

    def clone(self):
        data = self.data.copy()
        dims = self.dims[:]
        translate = self.translate[:]
        return Voxels(data, dims, translate, self.scale, self.axis_order)

    def write(self, fp):
        write(self, fp)

def read_header(fp):
    """ Read binvox header. Mostly meant for internal use.
    """
    line = fp.readline().strip()
    if not line.startswith(b'#binvox'):
        raise IOError('Not a binvox file')
    dims = [int(i) for i in fp.readline().strip().split(b' ')[1:]]
    translate = [float(i) for i in fp.readline().strip().split(b' ')[1:]]
    scale = [float(i) for i in fp.readline().strip().split(b' ')[1:]][0]
    line = fp.readline()
    return dims, translate, scale

def read_as_3d_array(fp, fix_coords=True):
    """ Read binary binvox format as array.

    Returns the model with accompanying metadata.

    Voxels are stored in a three-dimensional numpy array, which is simple and
    direct, but may use a lot of memory for large models. (Storage requirements
    are 8*(d^3) bytes, where d is the dimensions of the binvox model. Numpy
    boolean arrays use a byte per element).

    Doesn't do any checks on input except for the '#binvox' line.
    """
    dims, translate, scale = read_header(fp)
    raw_data = np.frombuffer(fp.read(), dtype=np.uint8)
    # if just using reshape() on the raw data:
    # indexing the array as array[i,j,k], the indices map into the
    # coords as:
    # i -> x
    # j -> z
    # k -> y
    # if fix_coords is true, then data is rearranged so that
    # mapping is
    # i -> x
    # j -> y
    # k -> z
    values, counts = raw_data[::2], raw_data[1::2]
    data = np.repeat(values, counts).astype(np.bool)
    data = data.reshape(dims)
    if fix_coords:
        # xzy to xyz TODO the right thing
        data = np.transpose(data, (0, 2, 1))
        axis_order = 'xyz'
    else:
        axis_order = 'xzy'
    return Voxels(data, dims, translate, scale, axis_order)

def read_as_coord_array(fp, fix_coords=True):
    """ Read binary binvox format as coordinates.

    Returns binvox model with voxels in a "coordinate" representation, i.e.  an
    3 x N array where N is the number of nonzero voxels. Each column
    corresponds to a nonzero voxel and the 3 rows are the (x, z, y) coordinates
    of the voxel.  (The odd ordering is due to the way binvox format lays out
    data).  Note that coordinates refer to the binvox voxels, without any
    scaling or translation.

    Use this to save memory if your model is very sparse (mostly empty).

    Doesn't do any checks on input except for the '#binvox' line.
    """
    dims, translate, scale = read_header(fp)
    raw_data = np.frombuffer(fp.read(), dtype=np.uint8)

    values, counts = raw_data[::2], raw_data[1::2]

    sz = np.prod(dims)
    index, end_index = 0, 0
    end_indices = np.cumsum(counts)
    indices = np.concatenate(([0], end_indices[:-1])).astype(end_indices.dtype)

    values = values.astype(np.bool)
    indices = indices[values]
    end_indices = end_indices[values]

    nz_voxels = []
    for index, end_index in zip(indices, end_indices):
        nz_voxels.extend(range(index, end_index))
    nz_voxels = np.array(nz_voxels)
    # TODO are these dims correct?
    # according to docs,
    # index = x * wxh + z * width + y; // wxh = width * height = d * d

    x = nz_voxels / (dims[0]*dims[1])
    zwpy = nz_voxels % (dims[0]*dims[1]) # z*w + y
    z = zwpy / dims[0]
    y = zwpy % dims[0]
    if fix_coords:
        data = np.vstack((x, y, z))
        axis_order = 'xyz'
    else:
        data = np.vstack((x, z, y))
        axis_order = 'xzy'

    #return Voxels(data, dims, translate, scale, axis_order)
    return Voxels(np.ascontiguousarray(data), dims, translate, scale, axis_order)

def dense_to_sparse(voxel_data, dtype=np.int):
    """ From dense representation to sparse (coordinate) representation.
    No coordinate reordering.
    """
    if voxel_data.ndim!=3:
        raise ValueError('voxel_data is wrong shape; should be 3D array.')
    return np.asarray(np.nonzero(voxel_data), dtype)

def sparse_to_dense(voxel_data, dims, dtype=np.bool):
    if voxel_data.ndim!=2 or voxel_data.shape[0]!=3:
        raise ValueError('voxel_data is wrong shape; should be 3xN array.')
    if np.isscalar(dims):
        dims = [dims]*3
    dims = np.atleast_2d(dims).T
    # truncate to integers
    xyz = voxel_data.astype(np.int)
    # discard voxels that fall outside dims
    valid_ix = ~np.any((xyz < 0) | (xyz >= dims), 0)
    xyz = xyz[:,valid_ix]
    out = np.zeros(dims.flatten(), dtype=dtype)
    out[tuple(xyz)] = True
    return out

#def get_linear_index(x, y, z, dims):
    #""" Assuming xzy order. (y increasing fastest.
    #TODO ensure this is right when dims are not all same
    #"""
    #return x*(dims[1]*dims[2]) + z*dims[1] + y

def write(voxel_model, fp):
    """ Write binary binvox format.

    Note that when saving a model in sparse (coordinate) format, it is first
    converted to dense format.

    Doesn't check if the model is 'sane'.

    """
    if voxel_model.data.ndim==2:
        # TODO avoid conversion to dense
        dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims)
    else:
        dense_voxel_data = voxel_model.data

    fp.write('#binvox 1\n')
    fp.write('dim '+' '.join(map(str, voxel_model.dims))+'\n')
    fp.write('translate '+' '.join(map(str, voxel_model.translate))+'\n')
    fp.write('scale '+str(voxel_model.scale)+'\n')
    fp.write('data\n')
    if not voxel_model.axis_order in ('xzy', 'xyz'):
        raise ValueError('Unsupported voxel model axis order')

    if voxel_model.axis_order=='xzy':
        voxels_flat = dense_voxel_data.flatten()
    elif voxel_model.axis_order=='xyz':
        voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()

    # keep a sort of state machine for writing run length encoding
    state = voxels_flat[0]
    ctr = 0
    for c in voxels_flat:
        if c==state:
            ctr += 1
            # if ctr hits max, dump
            if ctr==255:
                fp.write(chr(state))
                fp.write(chr(ctr))
                ctr = 0
        else:
            # if switch state, dump
            fp.write(chr(state))
            fp.write(chr(ctr))
            state = c
            ctr = 1
    # flush out remainders
    if ctr > 0:
        fp.write(chr(state))
        fp.write(chr(ctr))

if __name__ == '__main__':
    import doctest
    doctest.testmod()

## voxel.py
"""
Written by Christopher B. Choy <chrischoy@ai.stanford.edu>

Choy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, ECCV 2016
"""
import _init_paths
import os
import numpy as np

from lib.utils import stdout_redirected
from tempfile import TemporaryFile

import tools.binvox_rw as binvox_rw


def evaluate_voxel_prediction(preds, gt, thresh):
    preds_occupy = preds[:, 1, :, :] >= thresh
    diff = np.sum(np.logical_xor(preds_occupy, gt[:, 1, :, :]))
    intersection = np.sum(np.logical_and(preds_occupy, gt[:, 1, :, :]))
    union = np.sum(np.logical_or(preds_occupy, gt[:, 1, :, :]))
    num_fp = np.sum(np.logical_and(preds_occupy, gt[:, 0, :, :]))  # false positive
    num_fn = np.sum(np.logical_and(np.logical_not(preds_occupy), gt[:, 1, :, :]))  # false negative
    return np.array([diff, intersection, union, num_fp, num_fn])


def voxelize_model_binvox(obj, n_vox, return_voxel=True, binvox_add_param=''):

    cmd = "./tools/binvox -d %d -cb -dc -aw -pb %s -t binvox %s" % (
            n_vox, binvox_add_param, obj)

    if not os.path.exists(obj):
        raise ValueError('No obj found : %s' % obj)

    # Stop printing command line output
    with TemporaryFile() as f, stdout_redirected(f):
        os.system(cmd)

    # load voxelized model
    if return_voxel:
        with open('%s.binvox' % obj[:-4], 'rb') as f:
            vox = binvox_rw.read_as_3d_array(f)

        return vox.data

## voxelize_models.py
"""
Written by Christopher B. Choy <chrischoy@ai.stanford.edu>

Choy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, ECCV 2016
"""
import _init_paths

import sys
import os
import json
import traceback
from multiprocessing import Process

from lib.data_io import get_model_file, get_voxel_file
from lib.config import cfg
from lib.voxel import voxelize_model_binvox

USE_XVFP_SERVER = False
USE_SUBPROCESS = True
N_VOX = 64
OVERWRITE = True


def voxelize_model_subprocess(category_id, model_list_path, n_vox=N_VOX):

    model_ids = [line.rstrip('\n') for line in
                   open(os.path.join(model_list_path, 'models.txt'), 'r')]

    for i, model_id in enumerate(model_ids):
        model_fn = get_model_file(category_id, model_id)
        print('voxelizing %d/%d: %s' % (i+1, len(model_ids), model_fn))
        if not OVERWRITE and os.path.exists(model_fn):
            print('Already voxelized, skipping')
            continue

        sys.stdout.flush()  # To push print while running inside a Process
        voxelize_model_binvox(model_fn, n_vox, return_voxel=False)


def main():
    cats = json.load(open(cfg.DATASET))

    pl = []
    # Use binvox server

    if USE_XVFP_SERVER:
        os.system('Xvfb :99 -screen 0 640x480x24 &')
        os.system('export DISPLAY=:99')

    # setup
    for category_id, cat in cats.items():
        model_list_path = cat['dir']
        if USE_SUBPROCESS:
            p = Process(target=voxelize_model_subprocess,
                        args=(category_id, model_list_path))
            p.start()
            pl.append(p)
        else:
            voxelize_model_subprocess(category_id, model_list_path)

    if USE_SUBPROCESS:
        for p in pl:
            p.join()


if __name__ == '__main__':
    main()
	# Copyright (C) 2012 Daniel Maturana
	# This file is part of binvox-rw-py.
	#
	# binvox-rw-py is free software: you can redistribute it and/or modify
	# it under the terms of the GNU General Public License as published by
	# the Free Software Foundation, either version 3 of the License, or
	# (at your option) any later version.
	#
	# binvox-rw-py is distributed in the hope that it will be useful,
	# but WITHOUT ANY WARRANTY; without even the implied warranty of
	# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	# GNU General Public License for more details.
	#
	# You should have received a copy of the GNU General Public License
	# along with binvox-rw-py. If not, see <http://www.gnu.org/licenses/>.
	#
	# Modified by Christopher B. Choy <chrischoy at ai dot stanford dot edu>
	# for python 3 support

	"""
	Binvox to Numpy and back.


	>>> import numpy as np
	>>> import binvox_rw
	>>> with open('chair.binvox', 'rb') as f:
	... m1 = binvox_rw.read_as_3d_array(f)
	...
	>>> m1.dims
	[32, 32, 32]
	>>> m1.scale
	41.133000000000003
	>>> m1.translate
	[0.0, 0.0, 0.0]
	>>> with open('chair_out.binvox', 'wb') as f:
	... m1.write(f)
	...
	>>> with open('chair_out.binvox', 'rb') as f:
	... m2 = binvox_rw.read_as_3d_array(f)
	...
	>>> m1.dims==m2.dims
	True
	>>> m1.scale==m2.scale
	True
	>>> m1.translate==m2.translate
	True
	>>> np.all(m1.data==m2.data)
	True

	>>> with open('chair.binvox', 'rb') as f:
	... md = binvox_rw.read_as_3d_array(f)
	...
	>>> with open('chair.binvox', 'rb') as f:
	... ms = binvox_rw.read_as_coord_array(f)
	...
	>>> data_ds = binvox_rw.dense_to_sparse(md.data)
	>>> data_sd = binvox_rw.sparse_to_dense(ms.data, 32)
	>>> np.all(data_sd==md.data)
	True
	>>> # the ordering of elements returned by numpy.nonzero changes with axis
	>>> # ordering, so to compare for equality we first lexically sort the voxels.
	>>> np.all(ms.data[:, np.lexsort(ms.data)] == data_ds[:, np.lexsort(data_ds)])
	True
	"""

	import numpy as np

	class Voxels(object):
	""" Holds a binvox model.
	data is either a three-dimensional numpy boolean array (dense representation)
	or a two-dimensional numpy float array (coordinate representation).

	dims, translate and scale are the model metadata.

	dims are the voxel dimensions, e.g. [32, 32, 32] for a 32x32x32 model.

	scale and translate relate the voxels to the original model coordinates.

	To translate voxel coordinates i, j, k to original coordinates x, y, z:

	x_n = (i+.5)/dims[0]
	y_n = (j+.5)/dims[1]
	z_n = (k+.5)/dims[2]
	x = scale*x_n + translate[0]
	y = scale*y_n + translate[1]
	z = scale*z_n + translate[2]

	"""

	def __init__(self, data, dims, translate, scale, axis_order):
	self.data = data
	self.dims = dims
	self.translate = translate
	self.scale = scale
	assert (axis_order in ('xzy', 'xyz'))
	self.axis_order = axis_order

	def clone(self):
	data = self.data.copy()
	dims = self.dims[:]
	translate = self.translate[:]
	return Voxels(data, dims, translate, self.scale, self.axis_order)

	def write(self, fp):
	write(self, fp)

	def read_header(fp):
	""" Read binvox header. Mostly meant for internal use.
	"""
	line = fp.readline().strip()
	if not line.startswith(b'#binvox'):
	raise IOError('Not a binvox file')
	dims = [int(i) for i in fp.readline().strip().split(b' ')[1:]]
	translate = [float(i) for i in fp.readline().strip().split(b' ')[1:]]
	scale = [float(i) for i in fp.readline().strip().split(b' ')[1:]][0]
	line = fp.readline()
	return dims, translate, scale

	def read_as_3d_array(fp, fix_coords=True):
	""" Read binary binvox format as array.

	Returns the model with accompanying metadata.

	Voxels are stored in a three-dimensional numpy array, which is simple and
	direct, but may use a lot of memory for large models. (Storage requirements
	are 8*(d^3) bytes, where d is the dimensions of the binvox model. Numpy
	boolean arrays use a byte per element).

	Doesn't do any checks on input except for the '#binvox' line.
	"""
	dims, translate, scale = read_header(fp)
	raw_data = np.frombuffer(fp.read(), dtype=np.uint8)
	# if just using reshape() on the raw data:
	# indexing the array as array[i,j,k], the indices map into the
	# coords as:
	# i -> x
	# j -> z
	# k -> y
	# if fix_coords is true, then data is rearranged so that
	# mapping is
	# i -> x
	# j -> y
	# k -> z
	values, counts = raw_data[::2], raw_data[1::2]
	data = np.repeat(values, counts).astype(np.bool)
	data = data.reshape(dims)
	if fix_coords:
	# xzy to xyz TODO the right thing
	data = np.transpose(data, (0, 2, 1))
	axis_order = 'xyz'
	else:
	axis_order = 'xzy'
	return Voxels(data, dims, translate, scale, axis_order)

	def read_as_coord_array(fp, fix_coords=True):
	""" Read binary binvox format as coordinates.

	Returns binvox model with voxels in a "coordinate" representation, i.e. an
	3 x N array where N is the number of nonzero voxels. Each column
	corresponds to a nonzero voxel and the 3 rows are the (x, z, y) coordinates
	of the voxel. (The odd ordering is due to the way binvox format lays out
	data). Note that coordinates refer to the binvox voxels, without any
	scaling or translation.

	Use this to save memory if your model is very sparse (mostly empty).

	Doesn't do any checks on input except for the '#binvox' line.
	"""
	dims, translate, scale = read_header(fp)
	raw_data = np.frombuffer(fp.read(), dtype=np.uint8)

	values, counts = raw_data[::2], raw_data[1::2]

	sz = np.prod(dims)
	index, end_index = 0, 0
	end_indices = np.cumsum(counts)
	indices = np.concatenate(([0], end_indices[:-1])).astype(end_indices.dtype)

	values = values.astype(np.bool)
	indices = indices[values]
	end_indices = end_indices[values]

	nz_voxels = []
	for index, end_index in zip(indices, end_indices):
	nz_voxels.extend(range(index, end_index))
	nz_voxels = np.array(nz_voxels)
	# TODO are these dims correct?
	# according to docs,
	# index = x * wxh + z * width + y; // wxh = width * height = d * d

	x = nz_voxels / (dims[0]*dims[1])
	zwpy = nz_voxels % (dims[0]dims[1]) # zw + y
	z = zwpy / dims[0]
	y = zwpy % dims[0]
	if fix_coords:
	data = np.vstack((x, y, z))
	axis_order = 'xyz'
	else:
	data = np.vstack((x, z, y))
	axis_order = 'xzy'

	#return Voxels(data, dims, translate, scale, axis_order)
	return Voxels(np.ascontiguousarray(data), dims, translate, scale, axis_order)

	def dense_to_sparse(voxel_data, dtype=np.int):
	""" From dense representation to sparse (coordinate) representation.
	No coordinate reordering.
	"""
	if voxel_data.ndim!=3:
	raise ValueError('voxel_data is wrong shape; should be 3D array.')
	return np.asarray(np.nonzero(voxel_data), dtype)

	def sparse_to_dense(voxel_data, dims, dtype=np.bool):
	if voxel_data.ndim!=2 or voxel_data.shape[0]!=3:
	raise ValueError('voxel_data is wrong shape; should be 3xN array.')
	if np.isscalar(dims):
	dims = [dims]*3
	dims = np.atleast_2d(dims).T
	# truncate to integers
	xyz = voxel_data.astype(np.int)
	# discard voxels that fall outside dims
	valid_ix = ~np.any((xyz < 0) \| (xyz >= dims), 0)
	xyz = xyz[:,valid_ix]
	out = np.zeros(dims.flatten(), dtype=dtype)
	out[tuple(xyz)] = True
	return out

	#def get_linear_index(x, y, z, dims):
	#""" Assuming xzy order. (y increasing fastest.
	#TODO ensure this is right when dims are not all same
	#"""
	#return x(dims[1]dims[2]) + z*dims[1] + y

	def write(voxel_model, fp):
	""" Write binary binvox format.

	Note that when saving a model in sparse (coordinate) format, it is first
	converted to dense format.

	Doesn't check if the model is 'sane'.

	"""
	if voxel_model.data.ndim==2:
	# TODO avoid conversion to dense
	dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims)
	else:
	dense_voxel_data = voxel_model.data

	fp.write('#binvox 1\n')
	fp.write('dim '+' '.join(map(str, voxel_model.dims))+'\n')
	fp.write('translate '+' '.join(map(str, voxel_model.translate))+'\n')
	fp.write('scale '+str(voxel_model.scale)+'\n')
	fp.write('data\n')
	if not voxel_model.axis_order in ('xzy', 'xyz'):
	raise ValueError('Unsupported voxel model axis order')

	if voxel_model.axis_order=='xzy':
	voxels_flat = dense_voxel_data.flatten()
	elif voxel_model.axis_order=='xyz':
	voxels_flat = np.transpose(dense_voxel_data, (0, 2, 1)).flatten()

	# keep a sort of state machine for writing run length encoding
	state = voxels_flat[0]
	ctr = 0
	for c in voxels_flat:
	if c==state:
	ctr += 1
	# if ctr hits max, dump
	if ctr==255:
	fp.write(chr(state))
	fp.write(chr(ctr))
	ctr = 0
	else:
	# if switch state, dump
	fp.write(chr(state))
	fp.write(chr(ctr))
	state = c
	ctr = 1
	# flush out remainders
	if ctr > 0:
	fp.write(chr(state))
	fp.write(chr(ctr))

	if __name__ == '__main__':
	import doctest
	doctest.testmod()
	"""
	Written by Christopher B. Choy <chrischoy@ai.stanford.edu>

	Choy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction, ECCV 2016
	"""
	import _init_paths
	import os
	import numpy as np

	from lib.utils import stdout_redirected
	from tempfile import TemporaryFile

	import tools.binvox_rw as binvox_rw


	def evaluate_voxel_prediction(preds, gt, thresh):
	preds_occupy = preds[:, 1, :, :] >= thresh
	diff = np.sum(np.logical_xor(preds_occupy, gt[:, 1, :, :]))
	intersection = np.sum(np.logical_and(preds_occupy, gt[:, 1, :, :]))
	union = np.sum(np.logical_or(preds_occupy, gt[:, 1, :, :]))
	num_fp = np.sum(np.logical_and(preds_occupy, gt[:, 0, :, :])) # false positive
	num_fn = np.sum(np.logical_and(np.logical_not(preds_occupy), gt[:, 1, :, :])) # false negative
	return np.array([diff, intersection, union, num_fp, num_fn])


	def voxelize_model_binvox(obj, n_vox, return_voxel=True, binvox_add_param=''):

	cmd = "./tools/binvox -d %d -cb -dc -aw -pb %s -t binvox %s" % (
	n_vox, binvox_add_param, obj)

	if not os.path.exists(obj):
	raise ValueError('No obj found : %s' % obj)

	# Stop printing command line output
	with TemporaryFile() as f, stdout_redirected(f):
	os.system(cmd)

	# load voxelized model
	if return_voxel:
	with open('%s.binvox' % obj[:-4], 'rb') as f:
	vox = binvox_rw.read_as_3d_array(f)

	return vox.data