def make_dataset_by_class(self, dir, class_to_idx, phase, dataset_mode):
meshes = []
dir = os.path.expanduser(dir)
for target in sorted(os.listdir(dir)):
d = os.path.join(dir, target)
if not os.path.isdir(d):
continue
for root, _, fnames in sorted(os.walk(d)):
for fname in sorted(fnames):
if self.opt.verbose:
print("DEBUG fname in meshretrieve ", fname)
if is_mesh_file(fname) and (root.count(phase) == 1):
path = os.path.join(root, fname)
if self.opt.verbose:
print("DEBUG meshretrieve path ", path)
if dataset_mode == 'regression':
# Retrieves additional info from metadata file as labels - use filename as key
# filename format CC00839XX23_23710.obj
filename_key = fname[:-4]
# for the specific case where we are using both halves in the same dataset the file will end in L or R
if filename_key[-1] in ('L', 'R'):
filename_key = filename_key[:-2]
item = (path, class_to_idx[filename_key])
else:
item = (path, class_to_idx[target])
meshes.append(item)
return meshes
def make_dataset(path):
meshes = []
assert os.path.isdir(path), '%s is not a valid directory' % path
for root, _, fnames in sorted(os.walk(path)):
for fname in fnames:
if is_mesh_file(fname):
path = os.path.join(root, fname)
meshes.append(path)
return meshes
def make_target_dataset(dir, subroot, phase):
meshes = []
if subroot not in os.listdir(dir):
print('Error: No class %s in %s' % (subroot, dir))
exit()
dir = os.path.join(dir, subroot)
for root, _, fnames in sorted(os.walk(dir)):
for fname in sorted(fnames):
if is_mesh_file(fname) and (root.count(phase) == 1):
path = os.path.join(root, fname)
meshes.append(path)
return meshes
def make_dataset_by_class(dir, class_to_idx, phase):
meshes = []
dir = os.path.expanduser(dir)
for target in sorted(os.listdir(dir)):
d = os.path.join(dir, target)
if not os.path.isdir(d):
continue
for root, _, fnames in sorted(os.walk(d)):
for fname in sorted(fnames):
if is_mesh_file(fname) and (root.count(phase)==1):
path = os.path.join(root, fname)
item = (path, class_to_idx[target])
meshes.append(item)
return meshes
train_dir = os.path.join(converter.dataset, 'train')
train_graph_dir = os.path.join(converter.hybrid_graphs, 'train')
mkdir(train_graph_dir)
elif converter.portion == 'test':
test_npz_dir = os.path.join(converter.dataset, 'test', 'cache')
test_dir = os.path.join(converter.dataset, 'test')
test_graph_dir = os.path.join(converter.hybrid_graphs, 'test')
mkdir(test_graph_dir)
meshes = []
classes = np.loadtxt(classes_file)
if converter.portion == 'train':
for root, _, file_names in os.walk(train_dir):
for fname in file_names:
if is_mesh_file(fname):
mesh_file = os.path.join(root, fname)
npz_file = os.path.join(train_npz_dir,
fname.split('.')[0] + '_000.npz')
seg_file = os.path.join(segmentation_dir,
fname.split('.')[0] + '.eseg')
soft_seg_file = os.path.join(
soft_segmentation_dir,
fname.split('.')[0] + '.seseg')
face_seg_file = os.path.join(face_segmentation_dir,
fname.split('.')[0] + '.eseg')
node_seg_file = os.path.join(node_segmentation_dir,
fname.split('.')[0] + '.eseg')
mesh_files_dict = {
'mesh': mesh_file,
'npz': npz_file,
