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import os
import re
import shutil
from pathlib import Path
import numpy as np
from compressai.datasets.cache import CacheDataset
from compressai.datasets.utils import download_url, hash_file
from compressai.registry import register_dataset
[docs]
@register_dataset("SemanticKittiDataset")
class SemanticKittiDataset(CacheDataset):
"""SemanticKITTI dataset.
The KITTI dataset, introduced by [Geiger2012]_, contains 3D point
clouds sequences (i.e. video) of LiDAR sensor data from the
perspective of a driving vehicle.
The SemanticKITTI dataset, introduced by [Behley2019]_ and
[Behley2021]_, provides semantic annotation of all 22 sequences from
the odometry task [Odometry_KITTI]_ of KITTI.
See the [ProjectPage_SemanticKITTI]_ for a visualization.
Note that the test set is unlabelled, and must be evaluated on the
server, as mentioned at [ProjectPageTasks_SemanticKITTI]_.
The ``semantic_index`` is a number between 0 and 33 (inclusive),
which can be used as the semantic label for each point.
See also: [PapersWithCode_SemanticKITTI]_.
References:
.. [Geiger2012] `"Are we ready for Autonomous Driving? The KITTI
Vision Benchmark Suite,"
<https://www.cvlibs.net/publications/Geiger2012CVPR.pdf>`_,
by Andreas Geiger, Philip Lenz, and Raquel Urtasun,
CVPR 2012.
.. [Behley2019] `"SemanticKITTI: A Dataset for Semantic Scene
Understanding of LiDAR Sequences,"
<https://arxiv.org/abs/1904.01416>`_,
by Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel,
Sven Behnke, Cyrill Stachniss, and Juergen Gall, ICCV 2019.
.. [Behley2021] `"Towards 3D LiDAR-based semantic scene
understanding of 3D point cloud sequences: The SemanticKITTI
Dataset,"
<https://journals.sagepub.com/doi/10.1177/02783649211006735>`_,
by Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel,
Sven Behnke, Jürgen Gall, and Cyrill Stachniss, IJRR 2021.
.. [ProjectPage_SemanticKITTI] `Project page (SemanticKITTI)
<http://www.semantic-kitti.org/>`_
.. [ProjectPageTasks_SemanticKITTI] `Project page: Tasks
(SemanticKITTI)
<http://www.semantic-kitti.org/tasks.html>`_
.. [Odometry_KITTI] `"Visual Odometry / SLAM Evaluation 2012"
<https://www.cvlibs.net/datasets/kitti/eval_odometry.php>`_
.. [PapersWithCode_SemanticKITTI] `PapersWithCode: SemanticKITTI
<https://paperswithcode.com/dataset/semantickitti>`_
"""
URLS = [
"https://s3.eu-central-1.amazonaws.com/avg-kitti/data_odometry_calib.zip",
"https://s3.eu-central-1.amazonaws.com/avg-kitti/data_odometry_velodyne.zip",
"http://www.semantic-kitti.org/assets/data_odometry_labels.zip",
"http://www.semantic-kitti.org/assets/data_odometry_voxels_all.zip",
"http://www.semantic-kitti.org/assets/data_odometry_voxels.zip",
]
HASHES = [
"fa45d2bbff828776e6df689b161415fb7cd719345454b6d3567c2ff81fa4d075", # data_odometry_calib.zip
"062a45667bec6874ac27f733bd6809919f077265e7ac0bb25ac885798fa85ab5", # data_odometry_velodyne.zip
"408ec524636a393bae0288a0b2f48bf5418a1af988e82dee8496f89ddb7e6dda", # data_odometry_labels.zip
"10f333faa63426a519a573fbf0b4e3b56513511af30583473fa6a5782e037f3a", # data_odometry_voxels_all.zip
"d92c253e88e5e30c0a0b88f028510760e1db83b7e262d75c5931bf9b8d6dd51b", # data_odometry_voxels.zip
]
# Suggested splits:
SEQUENCES = {
"train": (0, 1, 2, 3, 4, 5, 6, 7, 9, 10),
"valid": (8,),
"infer": (8,),
"test": (11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21), # Unlabelled.
}
# fmt: off
NUM_SAMPLES_PER_SEQUENCE = [
4541, 1101, 4661, 801, 271, 2761, 1101, 1101, 4071, 1591, 1201,
921, 1061, 3281, 631, 1901, 1731, 491, 1801, 4981, 831, 2721
]
# fmt: on
RAW_SEMANTIC_INDEX_TO_LABEL = {
0: "unlabeled",
1: "outlier",
10: "car",
11: "bicycle",
13: "bus",
15: "motorcycle",
16: "on-rails",
18: "truck",
20: "other-vehicle",
30: "person",
31: "bicyclist",
32: "motorcyclist",
40: "road",
44: "parking",
48: "sidewalk",
49: "other-ground",
50: "building",
51: "fence",
52: "other-structure",
60: "lane-marking",
70: "vegetation",
71: "trunk",
72: "terrain",
80: "pole",
81: "traffic-sign",
99: "other-object",
252: "moving-car",
253: "moving-bicyclist",
254: "moving-person",
255: "moving-motorcyclist",
256: "moving-on-rails",
257: "moving-bus",
258: "moving-truck",
259: "moving-other-vehicle",
}
RAW_SEMANTIC_INDEX_TO_SEMANTIC_INDEX = {
idx: i for i, idx in enumerate(RAW_SEMANTIC_INDEX_TO_LABEL)
}
def __init__(
self,
root=None,
cache_root=None,
split="train",
split_name=None,
sequences=SEQUENCES["train"],
pre_transform=None,
transform=None,
download=True,
):
if cache_root is None:
assert root is not None
cache_root = f"{str(root).rstrip('/')}_cache"
self.root = Path(root) if root else None
self.cache_root = Path(cache_root)
self.split = split
self.split_name = split if split_name is None else split_name
self.sequences = sequences
if download and self.root:
self.download()
super().__init__(
cache_root=self.cache_root / self.split_name,
pre_transform=pre_transform,
transform=transform,
)
self._ensure_cache()
def download(self, force=False):
if not force and self.root.exists():
return
tmpdir = self.root.parent / "tmp"
os.makedirs(tmpdir, exist_ok=True)
for expected_hash, url in zip(self.HASHES, self.URLS):
filepath = download_url(
url, tmpdir, check_certificate=False, overwrite=force
)
shutil.unpack_archive(filepath, tmpdir)
assert expected_hash == hash_file(filepath, method="sha256")
shutil.move(tmpdir / "dataset", self.root)
def _get_items(self):
return sorted(
x
for i in self.sequences
for x in self.root.glob(f"**/{i:02}/velodyne/*.bin")
)
def _load_item(self, path):
path_prefix, sequence_index, file_index = self._parse_path(path)
assert str(path) == f"{path_prefix}{sequence_index}/velodyne/{file_index}.bin"
point_data = np.fromfile(path, dtype=np.float32).reshape(-1, 4)
label_data = (
np.fromfile(
f"{path_prefix}{sequence_index}/labels/{file_index}.label", dtype="<u2"
)
.reshape(-1, 2)
.astype(np.int16)
)
return {
"file_index": np.array([file_index], dtype=np.int32),
"sequence_index": np.array([sequence_index], dtype=np.int32),
"raw_semantic_index": label_data[:, 0],
"semantic_index": np_remap(
label_data[:, 0], self.RAW_SEMANTIC_INDEX_TO_SEMANTIC_INDEX
),
"instance_index": label_data[:, 1],
"pos": point_data[:, :3],
"remission": point_data[:, 3, None],
}
def _parse_path(self, path):
pattern = (
r"^(?P<path_prefix>.*?/?)"
r"(?P<sequence_index>\d+)/"
r"velodyne/"
r"(?P<file_index>\d{6})\.\w+$"
)
match = re.match(pattern, str(path))
if match is None:
raise ValueError(f"Could not parse path: {path}")
path_prefix = match.group("path_prefix")
sequence_index = match.group("sequence_index")
file_index = match.group("file_index")
return path_prefix, sequence_index, file_index
def np_remap(arr, d):
values, inverse = np.unique(arr, return_inverse=True)
values = np.array([d[x] for x in values], dtype=arr.dtype)
return values[inverse].reshape(arr.shape)
