tensorbay.label.label_box
LabeledBox2D ,LabeledBox3D, Box2DSubcatalog, Box3DSubcatalog.
Box2DSubcatalog
defines the subcatalog for 2D box type of labels.
LabeledBox2D
is the 2D bounding box type of label,
which is often used for CV tasks such as object detection.
Box3DSubcatalog
defines the subcatalog for 3D box type of labels.
LabeledBox3D
is the 3D bounding box type of label,
which is often used for object detection in 3D point cloud.
- class tensorbay.label.label_box.Box2DSubcatalog(is_tracking=False)[source]
Bases:
tensorbay.label.basic.SubcatalogBase
,tensorbay.label.supports.IsTrackingMixin
,tensorbay.label.supports.CategoriesMixin
,tensorbay.label.supports.AttributesMixin
This class defines the subcatalog for 2D box type of labels.
- Parameters
is_tracking (bool) – A boolean value indicates whether the corresponding subcatalog contains tracking information.
- Return type
None
- description
The description of the entire 2D box subcatalog.
- Type
str
- categories
All the possible categories in the corresponding dataset stored in a
NameList
with the category names as keys and theCategoryInfo
as values.
- category_delimiter
The delimiter in category values indicating parent-child relationship.
- Type
str
- attributes
All the possible attributes in the corresponding dataset stored in a
NameList
with the attribute names as keys and theAttributeInfo
as values.
- is_tracking
Whether the Subcatalog contains tracking information.
- Type
bool
Examples
Initialization Method 1: Init from
Box2DSubcatalog.loads()
method.>>> catalog = { ... "BOX2D": { ... "isTracking": True, ... "categoryDelimiter": ".", ... "categories": [{"name": "0"}, {"name": "1"}], ... "attributes": [{"name": "gender", "enum": ["male", "female"]}], ... } ... } >>> Box2DSubcatalog.loads(catalog["BOX2D"]) Box2DSubcatalog( (is_tracking): True, (category_delimiter): '.', (categories): NameList [...], (attributes): NameList [...] )
Initialization Method 2: Init an empty Box2DSubcatalog and then add the attributes.
>>> from tensorbay.utility import NameList >>> from tensorbay.label import CategoryInfo, AttributeInfo >>> categories = NameList() >>> categories.append(CategoryInfo("a")) >>> attributes = NameList() >>> attributes.append(AttributeInfo("gender", enum=["female", "male"])) >>> box2d_subcatalog = Box2DSubcatalog() >>> box2d_subcatalog.is_tracking = True >>> box2d_subcatalog.category_delimiter = "." >>> box2d_subcatalog.categories = categories >>> box2d_subcatalog.attributes = attributes >>> box2d_subcatalog Box2DSubcatalog( (is_tracking): True, (category_delimiter): '.', (categories): NameList [...], (attributes): NameList [...] )
- class tensorbay.label.label_box.LabeledBox2D(xmin, ymin, xmax, ymax, *, category=None, attributes=None, instance=None)[source]
Bases:
tensorbay.label.basic._LabelBase
,tensorbay.geometry.box.Box2D
This class defines the concept of 2D bounding box label.
LabeledBox2D
is the 2D bounding box type of label, which is often used for CV tasks such as object detection.- Parameters
xmin – The x coordinate of the top-left vertex of the labeled 2D box.
ymin – The y coordinate of the top-left vertex of the labeled 2D box.
xmax – The x coordinate of the bottom-right vertex of the labeled 2D box.
ymax – The y coordinate of the bottom-right vertex of the labeled 2D box.
category – The category of the label.
attributes – The attributs of the label.
instance – The instance id of the label.
- category
The category of the label.
- Type
str
- attributes
The attributes of the label.
- Type
Dict[str, Union[str, int, float, bool, List[Union[str, int, float, bool]]]]
- instance
The instance id of the label.
- Type
str
Examples
>>> xmin, ymin, xmax, ymax = 1, 2, 4, 4 >>> LabeledBox2D( ... xmin, ... ymin, ... xmax, ... ymax, ... category="example", ... attributes={"attr": "a"}, ... instance="12345", ... ) LabeledBox2D(1, 2, 4, 4)( (category): 'example', (attributes): {...}, (instance): '12345' )
- classmethod from_xywh(x, y, width, height, *, category=None, attributes=None, instance=None)[source]
Create a
LabeledBox2D
instance from the top-left vertex, the width and height.- Parameters
x (float) – X coordinate of the top left vertex of the box.
y (float) – Y coordinate of the top left vertex of the box.
width (float) – Length of the box along the x axis.
height (float) – Length of the box along the y axis.
category (Optional[str]) – The category of the label.
attributes (Optional[Dict[str, Any]]) – The attributs of the label.
instance (Optional[str]) – The instance id of the label.
- Returns
The created
LabeledBox2D
instance.- Return type
tensorbay.label.label_box._T
Examples
>>> x, y, width, height = 1, 2, 3, 4 >>> LabeledBox2D.from_xywh( ... x, ... y, ... width, ... height, ... category="example", ... attributes={"key": "value"}, ... instance="12345", ... ) LabeledBox2D(1, 2, 4, 6)( (category): 'example', (attributes): {...}, (instance): '12345' )
- classmethod loads(contents)[source]
Loads a LabeledBox2D from a dict containing the information of the label.
- Parameters
contents (Dict[str, Any]) – A dict containing the information of the 2D bounding box label.
- Returns
The loaded
LabeledBox2D
object.- Return type
tensorbay.label.label_box._T
Examples
>>> contents = { ... "box2d": {"xmin": 1, "ymin": 2, "xmax": 5, "ymax": 8}, ... "category": "example", ... "attributes": {"key": "value"}, ... "instance": "12345", ... } >>> LabeledBox2D.loads(contents) LabeledBox2D(1, 2, 5, 8)( (category): 'example', (attributes): {...}, (instance): '12345' )
- dumps()[source]
Dumps the current 2D bounding box label into a dict.
- Returns
A dict containing all the information of the 2D box label.
- Return type
Dict[str, Any]
Examples
>>> xmin, ymin, xmax, ymax = 1, 2, 4, 4 >>> labelbox2d = LabeledBox2D( ... xmin, ... ymin, ... xmax, ... ymax, ... category="example", ... attributes={"attr": "a"}, ... instance="12345", ... ) >>> labelbox2d.dumps() { 'category': 'example', 'attributes': {'attr': 'a'}, 'instance': '12345', 'box2d': {'xmin': 1, 'ymin': 2, 'xmax': 4, 'ymax': 4}, }
- class tensorbay.label.label_box.Box3DSubcatalog(is_tracking=False)[source]
Bases:
tensorbay.label.basic.SubcatalogBase
,tensorbay.label.supports.IsTrackingMixin
,tensorbay.label.supports.CategoriesMixin
,tensorbay.label.supports.AttributesMixin
This class defines the subcatalog for 3D box type of labels.
- Parameters
is_tracking (bool) – A boolean value indicates whether the corresponding subcatalog contains tracking information.
- Return type
None
- description
The description of the entire 3D box subcatalog.
- Type
str
- categories
All the possible categories in the corresponding dataset stored in a
NameList
with the category names as keys and theCategoryInfo
as values.
- category_delimiter
The delimiter in category values indicating parent-child relationship.
- Type
str
- attributes
All the possible attributes in the corresponding dataset stored in a
NameList
with the attribute names as keys and theAttributeInfo
as values.
- is_tracking
Whether the Subcatalog contains tracking information.
- Type
bool
Examples
Initialization Method 1: Init from
Box3DSubcatalog.loads()
method.>>> catalog = { ... "BOX3D": { ... "isTracking": True, ... "categoryDelimiter": ".", ... "categories": [{"name": "0"}, {"name": "1"}], ... "attributes": [{"name": "gender", "enum": ["male", "female"]}], ... } ... } >>> Box3DSubcatalog.loads(catalog["BOX3D"]) Box3DSubcatalog( (is_tracking): True, (category_delimiter): '.', (categories): NameList [...], (attributes): NameList [...] )
Initialization Method 2: Init an empty Box3DSubcatalog and then add the attributes.
>>> from tensorbay.utility import NameList >>> from tensorbay.label import CategoryInfo, AttributeInfo >>> categories = NameList() >>> categories.append(CategoryInfo("a")) >>> attributes = NameList() >>> attributes.append(AttributeInfo("gender", enum=["female", "male"])) >>> box3d_subcatalog = Box3DSubcatalog() >>> box3d_subcatalog.is_tracking = True >>> box3d_subcatalog.category_delimiter = "." >>> box3d_subcatalog.categories = categories >>> box3d_subcatalog.attributes = attributes >>> box3d_subcatalog Box3DSubcatalog( (is_tracking): True, (category_delimiter): '.', (categories): NameList [...], (attributes): NameList [...] )
- class tensorbay.label.label_box.LabeledBox3D(size, translation=(0, 0, 0), rotation=(1, 0, 0, 0), *, transform_matrix=None, category=None, attributes=None, instance=None)[source]
Bases:
tensorbay.label.basic._LabelBase
,tensorbay.geometry.box.Box3D
This class defines the concept of 3D bounding box label.
LabeledBox3D
is the 3D bounding box type of label, which is often used for object detection in 3D point cloud.- Parameters
size (Iterable[float]) – Size of the 3D bounding box label in a sequence of [x, y, z].
translation (Iterable[float]) – Translation of the 3D bounding box label in a sequence of [x, y, z].
rotation (Union[Iterable[float], quaternion.quaternion]) – Rotation of the 3D bounding box label in a sequence of [w, x, y, z] or a numpy quaternion object.
transform_matrix (Optional[Union[Sequence[Sequence[float]], numpy.ndarray]]) – A 4x4 or 3x4 transformation matrix.
category (str) – Category of the 3D bounding box label.
attributes (Dict[str, Union[str, int, float, bool, List[Union[str, int, float, bool]]]]) – Attributs of the 3D bounding box label.
instance (str) – The instance id of the 3D bounding box label.
- category
The category of the label.
- Type
str
- attributes
The attributes of the label.
- Type
Dict[str, Union[str, int, float, bool, List[Union[str, int, float, bool]]]]
- instance
The instance id of the label.
- Type
str
- size
The size of the 3D bounding box.
- transform
The transform of the 3D bounding box.
Examples
>>> LabeledBox3D( ... size=[1, 2, 3], ... translation=(1, 2, 3), ... rotation=(0, 1, 0, 0), ... category="example", ... attributes={"key": "value"}, ... instance="12345", ... ) LabeledBox3D( (size): Vector3D(1, 2, 3), (translation): Vector3D(1, 2, 3), (rotation): quaternion(0, 1, 0, 0), (category): 'example', (attributes): {...}, (instance): '12345' )
- classmethod loads(contents)[source]
Loads a LabeledBox3D from a dict containing the information of the label.
- Parameters
contents (Dict[str, Any]) – A dict containing the information of the 3D bounding box label.
- Returns
The loaded
LabeledBox3D
object.- Return type
tensorbay.label.label_box._T
Examples
>>> contents = { ... "box3d": { ... "size": {"x": 1, "y": 2, "z": 3}, ... "translation": {"x": 1, "y": 2, "z": 3}, ... "rotation": {"w": 1, "x": 0, "y": 0, "z": 0}, ... }, ... "category": "test", ... "attributes": {"key": "value"}, ... "instance": "12345", ... } >>> LabeledBox3D.loads(contents) LabeledBox3D( (size): Vector3D(1, 2, 3), (translation): Vector3D(1, 2, 3), (rotation): quaternion(1, 0, 0, 0), (category): 'test', (attributes): {...}, (instance): '12345' )
- dumps()[source]
Dumps the current 3D bounding box label into a dict.
- Returns
A dict containing all the information of the 3D bounding box label.
- Return type
Dict[str, Any]
Examples
>>> labeledbox3d = LabeledBox3D( ... size=[1, 2, 3], ... translation=(1, 2, 3), ... rotation=(0, 1, 0, 0), ... category="example", ... attributes={"key": "value"}, ... instance="12345", ... ) >>> labeledbox3d.dumps() { 'category': 'example', 'attributes': {'key': 'value'}, 'instance': '12345', 'box3d': { 'translation': {'x': 1, 'y': 2, 'z': 3}, 'rotation': {'w': 0.0, 'x': 1.0, 'y': 0.0, 'z': 0.0}, 'size': {'x': 1, 'y': 2, 'z': 3}, }, }