def test_eq(self):
translation = [1, 2, 3]
rotation = quaternion(1, 2, 3, 4)
box3d1 = LabeledBox3D(
size=[1, 2, 3],
translation=translation,
rotation=rotation,
category="cat",
attributes={"gender": "male"},
)
box3d2 = LabeledBox3D(
size=[1, 2, 3],
translation=translation,
rotation=rotation,
category="cat",
attributes={"gender": "male"},
)
box3d3 = LabeledBox3D(
size=[1, 2, 5],
translation=translation,
rotation=rotation,
category="cat",
attributes={"gender": "male"},
)
assert box3d1 == box3d2
assert box3d1 != box3d3
def test_rmul(self):
size = [1, 2, 3]
translation = [1, 2, 3]
rotation = quaternion(0, 1, 0, 0)
transform = Transform3D(translation, rotation)
quaternion_1 = quaternion(1, 2, 3, 4)
labeledbox3d = LabeledBox3D(
size=size,
translation=translation,
rotation=rotation,
category="cat",
attributes={"gender": "male"},
instance="12345",
)
assert labeledbox3d.__rmul__(transform) == LabeledBox3D(
size=size,
translation=[2, 0, 0],
rotation=[-1, 0, 0, 0],
category="cat",
attributes={"gender": "male"},
instance="12345",
)
assert labeledbox3d.__rmul__(quaternion_1) == LabeledBox3D(
size=size,
translation=[1.7999999999999996, 2, 2.6],
rotation=[-2, 1, 4, -3],
category="cat",
attributes={"gender": "male"},
instance="12345",
)
assert labeledbox3d.__rmul__(1) == NotImplemented
def _load_labels(boxes: List[Dict[str, Any]]) -> List[LabeledBox3D]:
labels = []
for box in boxes:
dimension = box["dimensions"]
position = box["position"]
attributes = box["attributes"]
attributes["stationary"] = box["stationary"]
attributes["camera_used"] = box["camera_used"]
attributes["points_count"] = box["points_count"]
label = LabeledBox3D(
size=(
dimension[
"y"], # The "y" dimension is the width from front to back.
dimension[
"x"], # The "x" dimension is the width from left to right.
dimension["z"],
),
translation=(
position[
"x"], # "x" axis points to the forward facing direction of the object.
position[
"y"], # "y" axis points to the left direction of the object.
position["z"],
),
rotation=quaternion.from_rotation_vector((0, 0, box["yaw"])),
category=box["label"],
attributes=attributes,
instance=box["uuid"],
)
labels.append(label)
return labels
def test_loads(self):
contents = {
"box3d": {
"translation": {
"x": 1,
"y": 2,
"z": 3
},
"rotation": {
"w": 1,
"x": 2,
"y": 3,
"z": 4
},
"size": {
"x": 1,
"y": 2,
"z": 3
},
},
"category": "cat",
"attributes": {
"gender": "male"
},
"instance": 12345,
}
labeledbox3d = LabeledBox3D.loads(contents)
assert labeledbox3d.category == "cat"
assert labeledbox3d.attributes == {"gender": "male"}
assert labeledbox3d.instance == 12345
assert labeledbox3d.translation == Vector3D(1, 2, 3)
assert labeledbox3d.rotation == quaternion(1, 2, 3, 4)
assert labeledbox3d.size == Vector3D(1, 2, 3)
def _get_labeled_box(
instance_annotation: Dict[str, Any],
annotation_info: Dict[str, Any],
) -> LabeledBox3D:
instance = instance_annotation["instance_token"]
category = annotation_info["category"][annotation_info["instance"][instance]["category_token"]][
"name"
]
attributes = {}
for attribute_token in instance_annotation["attribute_tokens"]:
attribute_full_name = annotation_info["attribute"][attribute_token]["name"]
key, value = attribute_full_name.rsplit(".", 1)
attributes[_ATTRIBUTE_KEYS[key]] = value
attributes["visibility"] = annotation_info["visibility"][
instance_annotation["visibility_token"]
]["level"]
width, length, height = instance_annotation["size"]
return LabeledBox3D(
translation=instance_annotation["translation"],
rotation=instance_annotation["rotation"],
size=(length, width, height),
category=category,
instance=instance,
attributes=attributes,
)
def DeepRoute(path: str) -> Dataset:
"""`DeepRoute <https://gas.graviti.cn/dataset/graviti-open-dataset\
/DeepRoute>`_ dataset.
The file structure should be like::
<path>
pointcloud/
00001.bin
00002.bin
...
10000.bin
groundtruth/
00001.txt
00002.txt
...
10000.txt
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 = Dataset(DATASET_NAME)
dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog.json"))
segment = dataset.create_segment()
point_cloud_paths = glob(os.path.join(root_path, "pointcloud", "*.bin"))
for point_cloud_path in point_cloud_paths:
point_cloud_id = os.path.splitext(os.path.basename(point_cloud_path))[0]
label_path = os.path.join(root_path, "groundtruth", f"{point_cloud_id}.txt")
data = Data(point_cloud_path)
data.label.box3d = []
with open(label_path, encoding="utf-8") as fp:
annotations = json.load(fp)["objects"]
for annotation in annotations:
bounding_box = annotation["bounding_box"]
position = annotation["position"]
label = LabeledBox3D(
size=(bounding_box["length"], bounding_box["width"], bounding_box["height"]),
translation=(position["x"], position["y"], position["z"]),
rotation=from_rotation_vector((0, 0, annotation["heading"])),
category=annotation["type"],
)
data.label.box3d.append(label)
segment.append(data)
return dataset
def test_init(self):
translation = Vector3D(1, 2, 3)
rotation = quaternion(1, 2, 3, 4)
size = Vector3D(1, 2, 3)
labeledbox3d = LabeledBox3D(
size=size,
translation=translation,
rotation=rotation,
category="cat",
attributes={"gender": "male"},
instance="12345",
)
assert labeledbox3d.translation == translation
assert labeledbox3d.rotation == rotation
assert labeledbox3d.size == size
assert labeledbox3d.category == "cat"
assert labeledbox3d.attributes == {"gender": "male"}
assert labeledbox3d.instance == "12345"
def test_dumps(self):
translation = [1, 2, 3]
rotation = quaternion(1, 2, 3, 4)
size = [1, 2, 3]
labeledbox3d = LabeledBox3D(
size=size,
translation=translation,
rotation=rotation,
category="cat",
attributes={"gender": "male"},
instance="12345",
)
assert labeledbox3d.dumps() == {
"box3d": {
"translation": {
"x": 1,
"y": 2,
"z": 3
},
"rotation": {
"w": 1,
"x": 2,
"y": 3,
"z": 4
},
"size": {
"x": 1,
"y": 2,
"z": 3
},
},
"category": "cat",
"attributes": {
"gender": "male"
},
"instance": "12345",
}
def NeolixOD(path: str) -> Dataset:
"""`Neolix OD <https://gas.graviti.cn/dataset\
/graviti-open-dataset/NeolixOD>`_ dataset.
The file structure should be like::
<path>
bins/
<id>.bin
labels/
<id>.txt
...
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 = Dataset(DATASET_NAME)
dataset.load_catalog(
os.path.join(os.path.dirname(__file__), "catalog.json"))
segment = dataset.create_segment()
point_cloud_paths = glob(os.path.join(root_path, "bins", "*.bin"))
for point_cloud_path in point_cloud_paths:
data = Data(point_cloud_path)
data.label.box3d = []
point_cloud_id = os.path.basename(point_cloud_path)[:6]
label_path = os.path.join(root_path, "labels", f"{point_cloud_id}.txt")
with open(label_path, encoding="utf-8") as fp:
for label_value_raw in fp:
label_value = label_value_raw.rstrip().split()
label = LabeledBox3D(
size=[
float(label_value[10]),
float(label_value[9]),
float(label_value[8])
],
translation=[
float(label_value[11]),
float(label_value[12]),
float(label_value[13]) + 0.5 * float(label_value[8]),
],
rotation=from_rotation_vector(
(0, 0, float(label_value[14]))),
category=label_value[0],
attributes={
"Occlusion": int(label_value[1]),
"Truncation": bool(int(label_value[2])),
"Alpha": float(label_value[3]),
},
)
data.label.box3d.append(label)
segment.append(data)
return dataset