tfds.features.LabeledImage

Image with additional label metadata.

Inherits From: Image, FeatureConnector

tfds.features.LabeledImage(
    *,
    labels: _LabelArg,
    shape: Optional[type_utils.Shape] = None,
    dtype: Optional[type_utils.TfdsDType] = None,
    encoding_format: Optional[str] = None,
    doc: feature_lib.DocArg = None
)

This feature connector is similar to tfds.features.Image, but expose additional attributes from tfds.features.ClassLabel to access label metadata.

This can be used for segmentation tasks were each pixel is labeled.

builder = tfds.builder('my_dataset')
builder.info.features['label_image'].names == ['background', 'car', ...]

labels Labels metadata (see docstring above)
shape Image shape (see tfds.features.Image.init)
dtype Image dtype (see tfds.features.Image.init)
encoding_format 'jpeg' or 'png' (see tfds.features.Image.init)
doc Documentation of this feature (e.g. description).

doc

dtype Return the dtype (or dict of dtype) of this FeatureConnector.
encoding_format

names

np_dtype

num_classes

numpy_dtype

shape Return the shape (or dict of shape) of this FeatureConnector.
tf_dtype

use_colormap

Methods

catalog_documentation

View source

catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

cls_from_name

View source

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

Returns the feature class for the given Python class.

decode_batch_example

View source

decode_batch_example(
    tfexample_data
)

Decode multiple features batched in a single tf.Tensor.

This function is used to decode features wrapped in tfds.features.Sequence(). By default, this function apply decode_example on each individual elements using tf.map_fn. However, for optimization, features can overwrite this method to apply a custom batch decoding.

Args
tfexample_data Same tf.Tensor inputs as decode_example, but with and additional first dimension for the sequence length.

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

decode_example

View source

decode_example(
    example
)

Reconstruct the image with TensorFlow from the tf example.

decode_example_np

View source

decode_example_np(
    example: bytes
) -> np.ndarray

Reconstruct the image with OpenCV from bytes, or default to PIL.

decode_example_np_with_opencv

View source

decode_example_np_with_opencv(
    example: bytes, num_channels: int
) -> np.ndarray

Reconstruct the image with OpenCV from bytes.

decode_example_np_with_pil

View source

decode_example_np_with_pil(
    example: bytes, num_channels: int
) -> np.ndarray

decode_ragged_example

View source

decode_ragged_example(
    tfexample_data
)

Decode nested features from a tf.RaggedTensor.

This function is used to decode features wrapped in nested tfds.features.Sequence(). By default, this function apply decode_batch_example on the flat values of the ragged tensor. For optimization, features can overwrite this method to apply a custom batch decoding.

Args
tfexample_data tf.RaggedTensor inputs containing the nested encoded examples.

Returns
tensor_data The decoded tf.RaggedTensor or dictionary of tensor, output of the tf.data.Dataset object

encode_example

View source

encode_example(
    image_or_path_or_fobj
)

Convert the given image into a dict convertible to tf example.

from_config

View source

@classmethod
from_config(
    root_dir: str
) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

Usage:

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

Args
root_dir Directory containing the features.json file.

Returns
The reconstructed feature instance.

from_json

View source

@classmethod
from_json(
    value: 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

View source

@classmethod
from_json_content(
    value: Union[Json, feature_pb2.ImageFeature]
) -> 'LabeledImage'

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.

Args
value FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by to_json_content.
doc Documentation of this feature (e.g. description).

Returns
The reconstructed FeatureConnector.

from_proto

View source

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

Instantiates a feature from its proto representation.

get_serialized_info

View source

get_serialized_info()

get_tensor_info

View source

get_tensor_info()

get_tensor_spec

View source

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

View source

load_metadata(
    data_dir, feature_name=None
) -> None

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

repr_html

View source

repr_html(
    ex: np.ndarray
) -> str

Images are displayed as thumbnail.

repr_html_batch

View source

repr_html_batch(
    ex: np.ndarray
) -> str

Sequence(Image()) are displayed as <video>.

repr_html_ragged

View source

repr_html_ragged(
    ex: np.ndarray
) -> str

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

save_config

View source

save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

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

save_metadata

View source

save_metadata(
    data_dir, feature_name=None
) -> None

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

to_json

View source

to_json() -> 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
            }
        }
    }
}

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

to_json_content

View source

to_json_content() -> feature_pb2.ImageFeature

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
The FeatureConnector metadata in either a dict, or a Feature proto. This output is used in from_json_content when reconstructing the feature.

to_proto

View source

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.

Returns
The feature proto describing this feature.

ALIASES []