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Zero
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"""
Semantic KITTI dataset
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""
import os
import numpy as np
from .builder import DATASETS
from .defaults import DefaultDataset
@DATASETS.register_module()
class SemanticKITTIDataset(DefaultDataset):
def __init__(self, ignore_index=-1, **kwargs):
self.ignore_index = ignore_index
self.learning_map = self.get_learning_map(ignore_index)
self.learning_map_inv = self.get_learning_map_inv(ignore_index)
super().__init__(ignore_index=ignore_index, **kwargs)
def get_data_list(self):
split2seq = dict(
train=[0, 1, 2, 3, 4, 5, 6, 7, 9, 10],
val=[8],
test=[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
)
if isinstance(self.split, str):
seq_list = split2seq[self.split]
elif isinstance(self.split, list):
seq_list = []
for split in self.split:
seq_list += split2seq[split]
else:
raise NotImplementedError
data_list = []
for seq in seq_list:
seq = str(seq).zfill(2)
seq_folder = os.path.join(self.data_root, "dataset", "sequences", seq)
seq_files = sorted(os.listdir(os.path.join(seq_folder, "velodyne")))
data_list += [
os.path.join(seq_folder, "velodyne", file) for file in seq_files
]
return data_list
def get_data(self, idx):
data_path = self.data_list[idx % len(self.data_list)]
with open(data_path, "rb") as b:
scan = np.fromfile(b, dtype=np.float32).reshape(-1, 4)
coord = scan[:, :3]
strength = scan[:, -1].reshape([-1, 1])
label_file = data_path.replace("velodyne", "labels").replace(".bin", ".label")
if os.path.exists(label_file):
with open(label_file, "rb") as a:
segment = np.fromfile(a, dtype=np.int32).reshape(-1)
segment = np.vectorize(self.learning_map.__getitem__)(
segment & 0xFFFF
).astype(np.int32)
else:
segment = np.zeros(scan.shape[0]).astype(np.int32)
data_dict = dict(
coord=coord,
strength=strength,
segment=segment,
name=self.get_data_name(idx),
)
return data_dict
def get_data_name(self, idx):
file_path = self.data_list[idx % len(self.data_list)]
dir_path, file_name = os.path.split(file_path)
sequence_name = os.path.basename(os.path.dirname(dir_path))
frame_name = os.path.splitext(file_name)[0]
data_name = f"{sequence_name}_{frame_name}"
return data_name
@staticmethod
def get_learning_map(ignore_index):
learning_map = {
0: ignore_index, # "unlabeled"
1: ignore_index, # "outlier" mapped to "unlabeled" --------------------------mapped
10: 0, # "car"
11: 1, # "bicycle"
13: 4, # "bus" mapped to "other-vehicle" --------------------------mapped
15: 2, # "motorcycle"
16: 4, # "on-rails" mapped to "other-vehicle" ---------------------mapped
18: 3, # "truck"
20: 4, # "other-vehicle"
30: 5, # "person"
31: 6, # "bicyclist"
32: 7, # "motorcyclist"
40: 8, # "road"
44: 9, # "parking"
48: 10, # "sidewalk"
49: 11, # "other-ground"
50: 12, # "building"
51: 13, # "fence"
52: ignore_index, # "other-structure" mapped to "unlabeled" ------------------mapped
60: 8, # "lane-marking" to "road" ---------------------------------mapped
70: 14, # "vegetation"
71: 15, # "trunk"
72: 16, # "terrain"
80: 17, # "pole"
81: 18, # "traffic-sign"
99: ignore_index, # "other-object" to "unlabeled" ----------------------------mapped
252: 0, # "moving-car" to "car" ------------------------------------mapped
253: 6, # "moving-bicyclist" to "bicyclist" ------------------------mapped
254: 5, # "moving-person" to "person" ------------------------------mapped
255: 7, # "moving-motorcyclist" to "motorcyclist" ------------------mapped
256: 4, # "moving-on-rails" mapped to "other-vehicle" --------------mapped
257: 4, # "moving-bus" mapped to "other-vehicle" -------------------mapped
258: 3, # "moving-truck" to "truck" --------------------------------mapped
259: 4, # "moving-other"-vehicle to "other-vehicle" ----------------mapped
}
return learning_map
@staticmethod
def get_learning_map_inv(ignore_index):
learning_map_inv = {
ignore_index: ignore_index, # "unlabeled"
0: 10, # "car"
1: 11, # "bicycle"
2: 15, # "motorcycle"
3: 18, # "truck"
4: 20, # "other-vehicle"
5: 30, # "person"
6: 31, # "bicyclist"
7: 32, # "motorcyclist"
8: 40, # "road"
9: 44, # "parking"
10: 48, # "sidewalk"
11: 49, # "other-ground"
12: 50, # "building"
13: 51, # "fence"
14: 70, # "vegetation"
15: 71, # "trunk"
16: 72, # "terrain"
17: 80, # "pole"
18: 81, # "traffic-sign"
}
return learning_map_inv
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