Properties

• deployment_id UUID of Deployment where this PredictedAsset is

• data_id UUID of Data of this PredictedAsset

• object_name Object name of this PredictedAsset

• filename Filename of this PredictedAsset

• large If true, this Asset file is considered large

• type Type of this PredictedAsset

• width Width of this PredictedAsset.

• height Height of this PredictedAsset.

• metadata Metadata of this Data. Can be None

Methods

reset_url

reset_url()

Description

Reset url of this object

reset_url

reset_url()

Description

Reset url property of this Asset by calling platform.

Returns

A url as str of this Asset.

get_data

get_data()

Description

Retrieve data of this asset

data = asset.get_data()
assert data.id == asset.data_id
assert data.filename == asset.filename

Returns

A Data object

delete

delete()

Description

Delete this predicted asset from its deployment

⚠️ DANGER ZONE: Be very careful here!

Remove this predicted asset

Examples

one_asset.delete()

add_review

add_review(
   rectangles: Optional[List[Tuple[int, int, int, int, str]]] = None,
   polygons: Optional[List[Tuple[List[List[int]], str]]] = None,
   classifications: Optional[List[str]] = None
)

Description

Add a review to your predicted-asset.
It will then be used along the prediction for the monitoring.

⚠️ DANGER ZONE: You will not be able to change it afterward and
the monitoring metrics will not be able to be computed again with another review.

Examples

one_annotation.overwrite(rectangles=[(10, 20, 30, 40, label_cat), (50, 60, 20, 30, label_dog)])

Arguments

• rectangles (List[Tuple[int, int, int, int, str]], optional) : List of rectangles of this review. Defaults to None.

• polygons (List[Tuple[List[List[int]], str]], optional) : List of polygons of this review. Defaults to None.

• classifications (List[str], optional) : List of classifications of this review. Defaults to None.