DataModels package

Submodules

DataModels.BaseModel module

class DataModels.BaseModel.BaseModel(*, id: str | None = None, dateObserved: datetime = None, type: str = 'BaseModel')[source]

Bases: BaseModel

Base class for the toolbox data models.

Attributes to override:

  • __rel_attrs__ (set): Set of attributes names that are relationships.

  • __context__ (set): Set of context URIs.

  • type (Field): Data model type name.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

class Config[source]

Bases: object

Pydantic configuration

allow_population_by_field_name = True
arbitrary_types_allowed = True
property context: Set[str]

Return a set with the context URLs of the entity.

dateObserved: datetime

Entity creation time

classmethod get_type() → str[source]

Static methods to get the data model type name.

id: str | None

Unique identifier of the entity

pretty() → str[source]

Return a pretty description of the object.

property rel_attrs: Set[str]

Return a set with the names of the attributes that are relationships.

type: str

Name of the entity type

DataModels.DataModelsCatalog module

DataModels.DataModelsCatalog.data_models_catalog: Dict[str, Type[BaseModel]] = {'Face': <class 'DataModels.Face.Face'>, 'Image': <class 'DataModels.Image.Image'>, 'InstanceSegmentation': <class 'DataModels.InstanceSegmentation.InstanceSegmentation'>, 'PersonKeyPoints': <class 'DataModels.PersonKeyPoints.PersonKeyPoints'>}

Dict storing the class responsible of each data model. The key is a string of the data model type.

DataModels.DataModelsCatalog.register_data_model(data_model)[source]

Decorator to register a data model class to the data_models_catalog.

DataModels.Face module

class DataModels.Face.Face(*, id: str | None = None, dateObserved: datetime = None, type: str = 'Face', image: str | None = None, boundingBox: BoundingBox | None = None, detectionConfidence: float | None = None, age: float | None = None, gender: Gender | None = None, genderConfidence: float | None = None, emotion: Emotion | None = None, emotionConfidence: float | None = None, features: list | None = None, featuresAlgorithm: str | None = None, recognitionDomain: str | None = None, recognized: bool | None = False, recognizedDistance: float | None = None, recognizedPerson: str | None = None)[source]

Bases: BaseModel

This data model stores information about a face, such as its estimated age, gender or identity. It is intended to be used with computer vision algorithms to infer common properties from a facial image.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

class Config[source]

Bases: object

Pydantic configuration

schema_extra = {'description': 'This entity stores information about a face, such as its estimated age, gender or identity. It is intended to be used with computer vision algorithms to infer common properties from a facial image'}
age: float | None

The estimated age of the face

bounding_box: BoundingBox | None

Bounding box of the detected face

detection_confidence: float | None

Confidence of the face detection

emotion: Emotion | None

The inferred emotion of the face

emotion_confidence: float | None

Confidence of the emotion classification

features: list | None

Facial features extracted with a computer vision algorithm used for face recognition tasks

features_algorithm: str | None

Name of the algorithm used to generate the features

gender: Gender | None

The inferred gender of the face

gender_confidence: float | None

Confidence of the gender classification

image: str | None

Id of the source image

recognition_domain: str | None

The face recognition domain. I.e. name of the group of people to recognize

recognized: bool | None

Flags whether a face recognition task has been performed

recognized_distance: float | None

Distance between the extracted features and the most similar face on the dataset. Less distance means more similarity

recognized_person: str | None

Name or id of the recognized person

type: str

The data model type (“Face”). Should not be changed.

DataModels.Image module

class DataModels.Image.Image(*, id: str | None = None, dateObserved: datetime = None, type: str = 'Image', width: int, height: int, path: str = '', url: str = '', source: str = '', purpose: str = '')[source]

Bases: BaseModel

This data model stores information about an image file that is uploaded to a server from a camera or any other source, and that is available to other services.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

class Config[source]

Bases: object

Pydantic configuration

schema_extra = {'description': 'This entity stores information about an image file that is uploaded to a server from a camera or any other source, and that is available to other services'}
height: int

Height of the image in pixels

path: str

Local file path to the image within the storage host

purpose: str

The purpose of the image, if any

source: str

The ID of the entity that created the image or a text describing the source of the image

type: str

The data model type (“Image”). Should not be changed.

url: str

URL to the image

width: int

Width of the image in pixels

DataModels.InstanceSegmentation module

class DataModels.InstanceSegmentation.InstanceSegmentation(*, id: str | None = None, dateObserved: datetime = None, type: str = 'InstanceSegmentation', image: str | None = None, mask: SegmentationMask | None = None, boundingBox: BoundingBox | None = None, label: str | None = None, labelId: int | None = None, confidence: float | None = None)[source]

Bases: BaseModel

This data model stores information about segmented objects on an image. It is intended to be used with instance segmentation algorithms to detect objects and infer a segmentation mask.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

class Config[source]

Bases: object

Pydantic configuration

schema_extra = {'description': 'This entity stores information about segmented objects on an image. It is intended to be used with instance segmentation algorithms to detect objects and infer a segmentation mask'}
bounding_box: BoundingBox | None

Bounding box of the detected instance

confidence: float | None

Confidence of the detection

image: str | None

Id of the source image

label: str | None

Name of the predicted class

label_id: int | None

Id of the label

mask: SegmentationMask | None

Segmentation mask of the detected instance

type: str

The data model type (“InstanceSegmentation”). Should not be changed.

DataModels.Notification module

class DataModels.Notification.Notification(*, id: str, type: str, subscriptionId: str, notifiedAt: datetime, data: List[dict])[source]

Bases: BaseModel

Data model for the context broker subscription notifications.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

data: List[dict]
id: str
notifiedAt: datetime
subscriptionId: str
type: str

DataModels.PersonKeyPoints module

class DataModels.PersonKeyPoints.PersonKeyPoints(*, id: str | None = None, dateObserved: datetime = None, type: str = 'PersonKeyPoints', image: str | None = None, boundingBox: BoundingBox | None = None, confidence: float | None = None, keypoints: COCOKeypoints | None = None)[source]

Bases: BaseModel

This data model stores information about a set of body-keypoints of an image of a person. It is intended to be used with computer vision algorithms to infer the position of different parts of the body from an image.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

class Config[source]

Bases: object

Pydantic configuration

schema_extra = {'description': 'This entity stores information about a set of body-keypoints of an image of a person. It is intended to be used with computer vision algorithms to infer the position of different parts of the body from an image'}
bounding_box: BoundingBox | None

Bounding box of the detected person

confidence: float | None

Confidence of the detection

image: str | None

Id of the source image

keypoints: COCOKeypoints | None

Keypoints of the detected person

type: str

The data model type (“PersonKeyPoints”). Should not be changed.

Module contents