tfds.features.Image

FeatureConnector for images.

Inherits From: FeatureConnector

tfds.features.Image(
    *, shape=None, dtype=None, encoding_format=None
)

During _generate_examples, the feature connector accept as input any of:

str: path to a {bmp,gif,jpeg,png} image (ex: /path/to/img.png).
np.array: 3d np.uint8 array representing an image.
A file object containing the png or jpeg encoded image string (ex: io.BytesIO(encoded_img_bytes))

Output:

tf.Tensor of type tf.uint8 and shape [height, width, num_channels] for BMP, JPEG, and PNG images and shape [num_frames, height, width, 3] for GIF images.

Example:

In the tfds.core.DatasetInfo object:

features=features.FeaturesDict({
    'input': features.Image(),
    'target': features.Image(shape=(None, None, 1),
                               encoding_format='png'),
})

During generation:

yield {
    'input': 'path/to/img.jpg',
    'target': np.ones(shape=(64, 64, 1), dtype=np.uint8),
}

Args
`shape`	tuple of ints or None, the shape of decoded image. For GIF images: (num_frames, height, width, channels=3). num_frames, height and width can be None. For other images: (height, width, channels). height and width can be None. See `tf.image.encode_*` for doc on channels parameter. Defaults to (None, None, 3).
`dtype`	tf.uint16 or tf.uint8 (default). tf.uint16 can be used only with png encoding_format
`encoding_format`	'jpeg' or 'png'. Format to serialize `np.ndarray` images on disk. If None, encode images as PNG. If image is loaded from {bmg,gif,jpeg,png} file, this parameter is ignored, and file original encoding is used.

Raises
`ValueError`	If the shape is invalid

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`

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 from the tf example.

`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/features.json')

Args
`root_dir`	Directory containing to the features.json file.

Returns
The reconstructed feature instance.

`from_json`

View source

@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`

View source

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

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`

View source

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`

View source

get_tensor_info()

Return the tf.Tensor dtype/shape of the feature.

This returns the tensor dtype/shape, as returned by .as_dataset by the tf.data.Dataset object.

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=(256, 256), dtype=tf.uint8)

Returns
`tensor_info`	Either a dict of `tfds.features.TensorInfo` object, or a `tfds.features.TensorInfo`

`load_metadata`

View source

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`

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

Returns the HTML str representation of the object (Sequence).

`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
)

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`

View source

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`

View source

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.

tfds.features.Image

Output:

Example:

Args

Raises

Attributes

Methods

decode_batch_example

decode_example

decode_ragged_example

encode_example

from_config

Usage:

from_json

Example:

from_json_content

get_serialized_info

Ex:

get_tensor_info

Ex:

load_metadata

repr_html

repr_html_batch

repr_html_ragged

save_config

save_metadata

to_json

For example:

Is serialized as:

to_json_content

`decode_batch_example`

`decode_example`

`decode_ragged_example`

`encode_example`

`from_config`

`from_json`

`from_json_content`

`get_serialized_info`

`get_tensor_info`

`load_metadata`

`repr_html`

`repr_html_batch`

`repr_html_ragged`

`save_config`

`save_metadata`

`to_json`

`to_json_content`