def __init__(self, root, sample_points=8192, block_size=10, num_classes=20, split='train', with_remission=False,
config_file='semantic-kitti.yaml', should_map=True, padding=0.01, random_sample=False, random_rate=0.1):
self.root = root
assert split in splits
self.split = split
self.padding = padding
self.block_size = block_size
self.sample_points = sample_points
self.random_sample = random_sample
self.with_remission = with_remission
self.should_map = should_map
self.config = yaml.safe_load(open(config_file, 'r'))
self.scan = laserscan.SemLaserScan(nclasses=num_classes, sem_color_dict=self.config['color_map'])
sequences = self.config['split'][split]
self.points_name = []
self.label_name = []
for sequence in sequences:
sequence = '{0:02d}'.format(int(sequence))
points_path = os.path.join(self.root, 'sequences', sequence, 'velodyne')
label_path = os.path.join(self.root, 'sequences', sequence, 'labels')
seq_points_name = [os.path.join(points_path, pn) for pn in os.listdir(points_path) if pn.endswith('.bin')]
seq_label_name = [os.path.join(label_path, ln) for ln in os.listdir(label_path) if ln.endswith('.label')]
assert len(seq_points_name) == len(seq_label_name)
seq_points_name.sort()
seq_label_name.sort()
self.points_name.extend(seq_points_name)
self.label_name.extend(seq_label_name)
if self.random_sample:
random.Random(seed).shuffle(self.points_name)
random.Random(seed).shuffle(self.label_name)
total_length = len(self.points_name)
self.points_name = self.points_name[:int(total_length * random_rate)]
self.label_name = self.label_name[:int(total_length * random_rate)]
label_weights_dict = mapped_content
num_keys = len(label_weights_dict.keys())
self.label_weights_lut = np.zeros((num_keys), dtype=np.float32)
self.label_weights_lut[list(label_weights_dict.keys())] = list(label_weights_dict.values())
self.label_weights_lut = np.power(np.amax(self.label_weights_lut[1:]) / self.label_weights_lut, 1 / 3.0)
if should_map:
remapdict = self.config["learning_map"]
# make lookup table for mapping
maxkey = max(remapdict.keys())
# +100 hack making lut bigger just in case there are unknown labels
self.remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
self.remap_lut[list(remapdict.keys())] = list(remapdict.values())
def __init__(self, root, sample_points=8192, block_size=10, stride=3.3, num_classes=20, split='test', with_remission=False,
config_file='semantic-kitti.yaml', should_map=True):
self.root = root
assert split in splits
self.split = split
self.stride = stride
self.block_size = block_size
self.block_points = sample_points
self.should_map = should_map
self.with_remission = with_remission
self.config = yaml.safe_load(open(config_file, 'r'))
self.scan = laserscan.SemLaserScan(
nclasses=num_classes, sem_color_dict=self.config['color_map'])
sequences = self.config['split'][split]
color = []
for values in self.config['learning_map_inv'].values():
color.append(self.config['color_map'][values])
self.color_map = np.array(color)
self.points_name = []
self.label_name = []
for sequence in sequences:
sequence = '{0:02d}'.format(int(sequence))
points_path = os.path.join(
self.root, 'sequences', sequence, 'velodyne')
label_path = os.path.join(self.root, 'sequences', sequence, 'labels')
seq_points_name = [os.path.join(points_path, pn) for pn in os.listdir(points_path) if pn.endswith('.bin')]
seq_points_name.sort()
self.points_name.extend(seq_points_name)
if split != 'test':
seq_label_name = [os.path.join(label_path, ln) for ln in os.listdir(label_path) if ln.endswith('.label')]
seq_label_name.sort()
self.label_name.extend(seq_label_name)
if should_map:
remapdict = self.config["learning_map"]
# make lookup table for mapping
maxkey = max(remapdict.keys())
# +100 hack making lut bigger just in case there are unknown labels
self.remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
self.remap_lut[list(remapdict.keys())] = list(remapdict.values())