tfds.features.BBoxFeature

See Stable See Nightly

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FeatureConnector for a normalized bounding box.

Inherits From: Tensor, FeatureConnector

tfds.features.BBoxFeature()

Input:

• tfds.features.BBox tuple.

Output:

• bbox: tf.Tensor of type tf.float32 and shape [4,] which contains the normalized coordinates of the bounding box [ymin, xmin, ymax, xmax]

Example:

• In the DatasetInfo object:

features=features.FeatureDict({
    'bbox': features.BBox(shape=(None, 64, 64, 3)),
})

• During generation:

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

Attributes
dtype	Return the dtype (or dict of dtype) of this FeatureConnector.
shape	Return the shape (or dict of shape) of this FeatureConnector.

Methods

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
)

Decode the feature dict to TF compatible input.

Args
tfexample_data	Data or dictionary of data, as read by the tf-example reader. It correspond to the tf.Tensor() (or dict of tf.Tensor()) extracted from the tf.train.Example, matching the info defined in get_serialized_info().

Returns
tensor_data	Tensor or dictionary of tensor, output of the tf.data.Dataset object

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/features.json')

Args
root_dir	Directory containing to the features.json file.

Returns
The reconstructed feature instance.

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)

Args
value	dict(type=, content=) containing the feature to restore. Match dict returned by to_json.

Returns
The reconstructed FeatureConnector.

from_json_content

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@classmethod
from_json_content(
    value: tfds.typing.Json
) -> "BBoxFeature"

FeatureConnector factory (to overwrite).

Subclasses should overwritte this method. importing the feature connector from the config.

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

This function See existing FeatureConnector for example of implementation.

Args
value	FeatureConnector information. Match the dict returned by to_json_content.

Returns
The reconstructed FeatureConnector.

get_serialized_info

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get_serialized_info()

Return the shape/dtype of features after encoding (for the adapter).

The FileAdapter then use those information to write data on disk.

This function indicates how this feature is encoded on file internally. The DatasetBuilder are written on disk as tf.train.Example proto.

Ex:

return {
    'image': tfds.features.TensorInfo(shape=(None,), dtype=tf.uint8),
    'height': tfds.features.TensorInfo(shape=(), dtype=tf.int32),
    'width': tfds.features.TensorInfo(shape=(), dtype=tf.int32),
}

FeatureConnector which are not containers should return the feature proto directly:

return tfds.features.TensorInfo(shape=(64, 64), tf.uint8)

If not defined, the retuned values are automatically deduced from the get_tensor_info function.

Returns
features	Either a dict of feature proto object, or a feature proto object

get_tensor_info

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get_tensor_info()

See base class for details.

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.

Args
data_dir	str, path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name	str, the name of the feature (from the FeaturesDict key)

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.

Args
root_dir	path/to/dir containing the features.json

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.

Args
data_dir	str, path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name	str, the name of the feature (from the FeaturesDict key)

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",
            "num_classes": 10
        }
    }
}

Returns
A dict(type=, content=). Will be forwarded to from_json when reconstructing the feature.

to_json_content

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to_json_content() -> tfds.typing.Json

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.

Returns
Dict containing the FeatureConnector metadata. Will be forwarded to from_json_content when reconstructing the feature.