tfds.features.BBoxFeature

See Stable See Nightly

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FeatureConnector for a normalized bounding box.

Inherits From: Tensor, FeatureConnector

tfds.features.BBoxFeature(
    *,
    doc: tfds.features.DocArg = None
)

features=features.FeatureDict({
    'bbox': tfds.features.BBox(),
})

yield {
    'input': tfds.features.BBox(ymin=0.3, xmin=0.8, ymax=0.5, xmax=1.0),
}

Methods

catalog_documentation

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catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

cls_from_name

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@classmethod
cls_from_name(
    python_class_name: str
) -> Type['FeatureConnector']

Returns the feature class for the given Python class.

decode_batch_example

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decode_batch_example(
    example_data
)

See base class for details.

decode_example

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decode_example(
    tfexample_data
)

See base class for details.

decode_ragged_example

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decode_ragged_example(
    example_data
)

See base class for details.

encode_example

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encode_example(
    bbox
)

See base class for details.

from_config

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@classmethod
from_config(
    root_dir: str
) -> 'FeatureConnector'

Reconstructs the FeatureConnector from the config file.

Usage:

features = FeatureConnector.from_config('path/to/dir')

from_json

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@classmethod
from_json(
    value: tfds.typing.Json
) -> 'FeatureConnector'

FeatureConnector factory.

This function should be called from the tfds.features.FeatureConnector base class. Subclass should implement the from_json_content.

Example:

feature = tfds.features.FeatureConnector.from_json(
    {'type': 'Image', 'content': {'shape': [32, 32, 3], 'dtype': 'uint8'} }
)
assert isinstance(feature, tfds.features.Image)

from_json_content

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@classmethod
from_json_content(
    value: Union[tfds.typing.Json, feature_pb2.BoundingBoxFeature]
) -> 'BBoxFeature'

FeatureConnector factory (to overwrite).

Subclasses should overwrite this method. This method is used when importing the feature connector from the config.

This function should not be called directly. FeatureConnector.from_json should be called instead.

See existing FeatureConnectors for implementation examples.

from_proto

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@classmethod
from_proto(
    feature_proto: feature_pb2.Feature
) -> T

Instantiates a feature from its proto representation.

get_serialized_info

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get_serialized_info()

See base class for details.

get_tensor_info

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get_tensor_info() -> tfds.features.TensorInfo

See base class for details.

get_tensor_spec

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get_tensor_spec() -> TreeDict[tf.TensorSpec]

Returns the tf.TensorSpec of this feature (not the element spec!).

Note that the output of this method may not correspond to the element spec of the dataset. For example, currently this method does not support RaggedTensorSpec.

load_metadata

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load_metadata(
    data_dir, feature_name
)

Restore the feature metadata from disk.

If a dataset is re-loaded and generated files exists on disk, this function will restore the feature metadata from the saved file.

repr_html

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repr_html(
    ex: np.ndarray
) -> str

Returns the HTML str representation of an Image with BBoxes.

repr_html_batch

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repr_html_batch(
    ex: np.ndarray
) -> str

Returns the HTML str representation of an Image with BBoxes (Sequence).

repr_html_ragged

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repr_html_ragged(
    ex: np.ndarray
) -> str

Returns the HTML str representation of the object (Nested sequence).

save_config

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save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

save_metadata

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save_metadata(
    data_dir, feature_name
)

Save the feature metadata on disk.

This function is called after the data has been generated (by _download_and_prepare) to save the feature connector info with the generated dataset.

Some dataset/features dynamically compute info during _download_and_prepare. For instance:

• Labels are loaded from the downloaded data
• Vocabulary is created from the downloaded data
• ImageLabelFolder compute the image dtypes/shape from the manual_dir

After the info have been added to the feature, this function allow to save those additional info to be restored the next time the data is loaded.

By default, this function do not save anything, but sub-classes can overwrite the function.

to_json

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to_json() -> tfds.typing.Json

Exports the FeatureConnector to Json.

Each feature is serialized as a dict(type=..., content=...).

• type: The cannonical name of the feature (module.FeatureName).
• content: is specific to each feature connector and defined in to_json_content. Can contain nested sub-features (like for tfds.features.FeaturesDict and tfds.features.Sequence).

For example:

tfds.features.FeaturesDict({
    'input': tfds.features.Image(),
    'target': tfds.features.ClassLabel(num_classes=10),
})

Is serialized as:

{
    "type": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
    "content": {
        "input": {
            "type": "tensorflow_datasets.core.features.image_feature.Image",
            "content": {
                "shape": [null, null, 3],
                "dtype": "uint8",
                "encoding_format": "png"
            }
        },
        "target": {
            "type":
            "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
            "content": {
              "num_classes": 10
            }
        }
    }
}

to_json_content

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to_json_content() -> feature_pb2.BoundingBoxFeature

FeatureConnector factory (to overwrite).

This function should be overwritten by the subclass to allow re-importing the feature connector from the config. See existing FeatureConnector for example of implementation.

to_proto

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to_proto() -> feature_pb2.Feature

Exports the FeatureConnector to the Feature proto.

For features that have a specific schema defined in a proto, this function needs to be overriden. If there's no specific proto schema, then the feature will be represented using JSON.