def __init__(self,
model_id,
edge_length_threshold=0.1,
filled=False,
voxel_config=None):
self._model_id = model_id
self._edge_length_threshold = edge_length_threshold
self._filled = filled
self._voxel_config = VoxelConfig() if voxel_config is None else \
voxel_config
def check_archive(cat_desc, voxel_dim, example_ids=None):
from shapenet.core import cat_desc_to_id, get_example_ids
from shapenet.core.voxels.config import VoxelConfig
cat_id = cat_desc_to_id(cat_desc)
if example_ids is None or len(example_ids) == 0:
example_ids = get_example_ids(cat_id)
config = VoxelConfig(voxel_dim=voxel_dim)
n_absent = 0
with config.get_zip_file(cat_id) as zf:
namelist = set(zf.namelist())
for example_id in example_ids:
if config.get_binvox_subpath(cat_id, example_id) not in namelist:
n_absent += 1
if n_absent == 0:
print('All %d %s voxels present' % (len(example_ids), cat_desc))
else:
print('%d / %d voxel files missing from %s' %
(n_absent, len(example_ids), cat_desc))
def create_archive(cat_desc, voxel_dim, example_ids=None, overwrite=False):
import os
import zipfile
from shapenet.core.voxels.config import VoxelConfig
from shapenet.core import get_example_ids, cat_desc_to_id
cat_id = cat_desc_to_id(cat_desc)
if example_ids is None or len(example_ids) == 0:
example_ids = get_example_ids(cat_id)
config = VoxelConfig(voxel_dim)
with zipfile.ZipFile(config.get_zip_path(cat_id), 'a') as zf:
if not overwrite:
namelist = set(zf.namelist())
for example_id in example_ids:
dst = config.get_binvox_subpath(cat_id, example_id)
if not overwrite and dst in namelist:
continue
src = config.get_binvox_path(cat_id, example_id)
if os.path.isfile(src):
zf.write(src, dst)
else:
print('No file at %s for %s/%s: skipping' %
(src, cat_id, example_id))
import os
from path import get_inference_subdir
import util3d.voxel.dataset as bvd
import util3d.voxel.convert as bio
from shapenet.core.voxels.config import VoxelConfig
from meshes import get_inferred_mesh_dataset
_default_config = VoxelConfig()
def get_voxel_subdir(model_id,
edge_length_threshold=0.1,
voxel_config=None,
filled=False):
if voxel_config is None:
voxel_config = _default_config
es = 'base' if edge_length_threshold is None else \
str(edge_length_threshold)
fs = 'filled' if filled else 'unfilled'
args = ['voxels', es, voxel_config.voxel_id, model_id, fs]
return get_inference_subdir(*args)
def _get_base_voxel_dataset(model_id,
edge_length_threshold=0.1,
voxel_config=None,
filled=False,
auto_save=True):
kwargs = dict(model_id=model_id,
edge_length_threshold=edge_length_threshold,
voxel_config=voxel_config,
def convert_multi(cat_id, example_ids, overwrite=False, **kwargs):
from shapenet.core.voxels.config import VoxelConfig
config = VoxelConfig(**kwargs)
config.create_voxel_data(cat_id, example_ids, overwrite=overwrite)
def get_unfilled_gt_voxel_dataset(cat_id):
config = VoxelConfig()
return config.get_dataset(cat_id)
def __init__(self, model_id, filled=True, voxel_config=None):
self._model_id = model_id
self._filled = filled
self._voxel_config = VoxelConfig() if voxel_config is None else \
voxel_config
from mayavi import mlab
from shapenet.core.voxels.config import VoxelConfig
from shapenet.core import cat_desc_to_id, get_example_ids
from util3d.mayavi_vis import vis_voxels
cat_desc = 'plane'
cat_id = cat_desc_to_id(cat_desc)
example_ids = get_example_ids(cat_id)
config = VoxelConfig()
with config.get_dataset(cat_id) as dataset:
for example_id in example_ids:
voxels = dataset[example_id]
vis_voxels(voxels.dense_data(), color=(0, 0, 1))
mlab.show()
#!/usr/bin/python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from shapenet.core.voxels.config import VoxelConfig
from shapenet.core import cat_desc_to_id
from util3d.mayavi_vis import vis_sliced
from mayavi import mlab
import numpy as np
cat_desc = 'watercraft'
cat_id = cat_desc_to_id(cat_desc)
base = VoxelConfig(voxel_dim=128)
filled = base.filled('orthographic')
with base.get_dataset(cat_id) as bds, filled.get_dataset(cat_id) as fds:
for example_id in bds:
base_data = bds[example_id].dense_data()
filled_data = fds[example_id].dense_data()
for dense_data in (base_data, filled_data):
mlab.figure()
vis_sliced(dense_data.astype(np.float32), axis_order='xyz')
mlab.show()
break