tensorbay.label.label_classification

Classification.

ClassificationSubcatalog defines the subcatalog for classification type of labels.

Classification defines the concept of classification label, which can apply to different types of data, such as images and texts.

class tensorbay.label.label_classification.ClassificationSubcatalog(description='')[source]

Bases: tensorbay.label.basic.SubcatalogBase, tensorbay.label.supports.CategoriesMixin, tensorbay.label.supports.AttributesMixin

This class defines the subcatalog for classification type of labels.

Parameters: description (str) –
Return type: None

description

The description of the entire classification subcatalog.

Type: str

categories

All the possible categories in the corresponding dataset stored in a NameList with the category names as keys and the CategoryInfo as values.

Type: tensorbay.utility.name.NameList[tensorbay.label.supports.CategoryInfo]

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 the AttributeInfo as values.

Type: tensorbay.utility.name.NameList[tensorbay.label.attributes.AttributeInfo]

Examples

Initialization Method 1: Init from ClassificationSubcatalog.loads() method.

>>> catalog = {
...     "CLASSIFICATION": {
...         "categoryDelimiter": ".",
...         "categories": [
...             {"name": "a"},
...             {"name": "b"},
...         ],
...          "attributes": [{"name": "gender", "enum": ["male", "female"]}],
...     }
... }
>>> ClassificationSubcatalog.loads(catalog["CLASSIFICATION"])
ClassificationSubcatalog(
  (category_delimiter): '.',
  (categories): NameList [...],
  (attributes): NameList [...]
)

Initialization Method 2: Init an empty ClassificationSubcatalog and then add the attributes.

>>> from tensorbay.utility import NameList
>>> from tensorbay.label import CategoryInfo, AttributeInfo, KeypointsInfo
>>> categories = NameList()
>>> categories.append(CategoryInfo("a"))
>>> attributes = NameList()
>>> attributes.append(AttributeInfo("gender", enum=["female", "male"]))
>>> classification_subcatalog = ClassificationSubcatalog()
>>> classification_subcatalog.category_delimiter = "."
>>> classification_subcatalog.categories = categories
>>> classification_subcatalog.attributes = attributes
>>> classification_subcatalog
ClassificationSubcatalog(
  (category_delimiter): '.',
  (categories): NameList [...],
  (attributes): NameList [...]
)

class tensorbay.label.label_classification.Classification(category=None, attributes=None)[source]

Bases: tensorbay.label.basic._LabelBase

This class defines the concept of classification label.

Classification is the classification type of label, which applies to different types of data, such as images and texts.

Parameters

category (str) – The category of the label.
attributes (Dict[str, Union[str, int, float, bool, List[Union[str, int, float, bool]]]]) – The attributes 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]]]]

Examples

>>> Classification(category="example", attributes={"attr": "a"})
Classification(
  (category): 'example',
  (attributes): {...}
)

classmethod loads(contents)[source]

Loads a Classification label from a dict containing the label information.

Parameters: contents (Dict[str, Any]) – A dict containing the information of the classification label.
Returns: The loaded Classification object.
Return type: tensorbay.label.label_classification._T

Examples

>>> contents = {"category": "example", "attributes": {"key": "value"}}
>>> Classification.loads(contents)
Classification(
  (category): 'example',
  (attributes): {...}
)