fiftyone.core.aggregations.Schema

Extracts the names and types of the attributes of a specified embedded document field across all samples in a collection.

Schema aggregations are useful for detecting the presence and types of dynamic attributes of fiftyone.core.labels.Label fields across a collection.

Examples:

import fiftyone as fo

dataset = fo.Dataset()

sample1 = fo.Sample(
    filepath="image1.png",
    ground_truth=fo.Detections(
        detections=[
            fo.Detection(
                label="cat",
                bounding_box=[0.1, 0.1, 0.4, 0.4],
                foo="bar",
                hello=True,
            ),
            fo.Detection(
                label="dog",
                bounding_box=[0.5, 0.5, 0.4, 0.4],
                hello=None,
            )
        ]
    )
)

sample2 = fo.Sample(
    filepath="image2.png",
    ground_truth=fo.Detections(
        detections=[
            fo.Detection(
                label="rabbit",
                bounding_box=[0.1, 0.1, 0.4, 0.4],
                foo=None,
            ),
            fo.Detection(
                label="squirrel",
                bounding_box=[0.5, 0.5, 0.4, 0.4],
                hello="there",
            ),
        ]
    )
)

dataset.add_samples([sample1, sample2])

#
# Get schema of all dynamic attributes on the detections in a
# `Detections` field
#

aggregation = fo.Schema("ground_truth.detections", dynamic_only=True)
print(dataset.aggregate(aggregation))
# {'foo': StringField, 'hello': [BooleanField, StringField]}