def nuImages(path: str) -> FusionDataset:
"""`nuImages <https://www.nuscenes.org/nuimages>`_ dataset.
The file structure should be like::
<path>
nuimages-v1.0-all-metadata/
v1.0-mini/
attribute.json
calibrated_sensor.json
category.json
ego_pose.json
instance.json
log.json
object_ann.json
sample_data.json
sample.json
sensor.json
surface_ann.json
v1.0-test/
...
v1.0-train/
...
v1.0-val/
...
samples/
CAM_BACK/
CAM_BACK_LEFT/
CAM_BACK_RIGHT/
CAM_FRONT/
CAM_FRONT_LEFT/
CAM_FRONT_RIGHT/
sweeps/
CAM_BACK/
CAM_BACK_LEFT/
CAM_BACK_RIGHT/
CAM_FRONT/
CAM_FRONT_LEFT/
CAM_FRONT_RIGHT/
nuimages-v1.0-mini/
samples/
...
sweeps/
...
v1.0-mini/
...
Arguments:
path: The root directory of the dataset.
Returns:
Loaded :class:`~tensorbay.dataset.dataset.Dataset` instance.
"""
root_path = os.path.abspath(os.path.expanduser(path))
dataset = FusionDataset(DATASET_NAME)
dataset.load_catalog(
os.path.join(os.path.dirname(__file__), "catalog.json"))
metadata_path = os.path.join(root_path, "nuimages-v1.0-all-metadata")
for subset in os.listdir(metadata_path):
dataset.add_segment(_get_segment(root_path, subset, metadata_path))
return dataset
def CADC(path: str) -> FusionDataset:
"""`CADC <http://cadcd.uwaterloo.ca/index.html>`_ dataset.
The file structure should be like::
<path>
2018_03_06/
0001/
3d_ann.json
labeled/
image_00/
data/
0000000000.png
0000000001.png
...
timestamps.txt
...
image_07/
data/
timestamps.txt
lidar_points/
data/
timestamps.txt
novatel/
data/
dataformat.txt
timestamps.txt
...
0018/
calib/
00.yaml
01.yaml
02.yaml
03.yaml
04.yaml
05.yaml
06.yaml
07.yaml
extrinsics.yaml
README.txt
2018_03_07/
2019_02_27/
Arguments:
path: The root directory of the dataset.
Returns:
Loaded `~tensorbay.dataset.dataset.FusionDataset` instance.
"""
root_path = os.path.abspath(os.path.expanduser(path))
dataset = FusionDataset(DATASET_NAME)
dataset.notes.is_continuous = True
dataset.load_catalog(
os.path.join(os.path.dirname(__file__), "catalog.json"))
for date in os.listdir(root_path):
date_path = os.path.join(root_path, date)
sensors = _load_sensors(os.path.join(date_path, "calib"))
for index in os.listdir(date_path):
if index == "calib":
continue
segment = dataset.create_segment(f"{date}-{index}")
segment.sensors = sensors
segment_path = os.path.join(root_path, date, index)
data_path = os.path.join(segment_path, "labeled")
with open(os.path.join(segment_path, "3d_ann.json"),
encoding="utf-8") as fp:
# The first line of the json file is the json body.
annotations = json.loads(fp.readline())
timestamps = _load_timestamps(sensors, data_path)
for frame_index, annotation in enumerate(annotations):
segment.append(
_load_frame(sensors, data_path, frame_index, annotation,
timestamps))
return dataset
def nuScenes(path: str) -> FusionDataset:
"""`nuScenes <https://www.nuscenes.org/>`_ dataset.
The file structure should be like::
<path>
v1.0-mini/
maps/
36092f0b03a857c6a3403e25b4b7aab3.png
...
samples/
CAM_BACK/
CAM_BACK_LEFT/
CAM_BACK_RIGHT/
CAM_FRONT/
CAM_FRONT_LEFT/
CAM_FRONT_RIGHT/
LIDAR_TOP/
RADAR_BACK_LEFT/
RADAR_BACK_RIGHT/
RADAR_FRONT/
RADAR_FRONT_LEFT/
RADAR_FRONT_RIGHT/
sweeps/
CAM_BACK/
CAM_BACK_LEFT/
CAM_BACK_RIGHT/
CAM_FRONT/
CAM_FRONT_LEFT/
CAM_FRONT_RIGHT/
LIDAR_TOP/
RADAR_BACK_LEFT/
RADAR_BACK_RIGHT/
RADAR_FRONT/
RADAR_FRONT_LEFT/
RADAR_FRONT_RIGHT/
v1.0-mini/
attribute.json
calibrated_sensor.json
category.json
ego_pose.json
instance.json
log.json
map.json
sample_annotation.json
sample_data.json
sample.json
scene.json
sensor.json
visibility.json
v1.0-test/
maps/
samples/
sweeps/
v1.0-test/
v1.0-trainval/
maps/
samples/
sweeps/
v1.0-trainval/
Arguments:
path: The root directory of the dataset.
Returns:
Loaded :class:`~tensorbay.dataset.dataset.FusionDataset` instance.
"""
root_path = os.path.abspath(os.path.expanduser(path))
dataset = FusionDataset(DATASET_NAME)
dataset.notes.is_continuous = True
dataset.notes.bin_point_cloud_fields = ["X", "Y", "Z", "Intensity", "Ring"]
dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog.json"))
for subset in os.listdir(root_path):
for segment in _generate_segments(root_path, subset):
dataset.add_segment(segment)
return dataset