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tfds.features.Video

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FeatureConnector for videos, encoding frames individually on disk.

Inherits From: Sequence

tfds.features.Video(
    shape, encoding_format='png', ffmpeg_extra_args=()
)

Video: The image connector accepts as input a 4 dimensional tf.uint8 array representing a video, a sequence of paths to encoded frames, or a path or a file object that can be decoded with ffmpeg. Note that not all formats in ffmpeg support reading from pipes, so providing a file object might fail. Furthermore, if a path is given that is not on the local file system, we first copy it to a temporary local file before passing it to ffmpeg.

Output:

video: tf.Tensor of type tf.uint8 and shape [num_frames, height, width, channels], where channels must be 1 or 3

Example:

In the DatasetInfo object:

features=features.FeatureDict({
    'video': features.Video(shape=(None, 64, 64, 3)),
})

During generation, you can use any of:

yield {
    'video': np.ones(shape=(128, 64, 64, 3), dtype=np.uint8),
}

or list of frames:

yield {
    'video': ['path/to/frame001.png', 'path/to/frame002.png'],
}

or path to video:

yield {
    'video': '/path/to/video.avi',
}

or file object:

yield {
    'video': tf.io.gfile.GFile('/complex/path/video.avi'),
}

Args
`shape`	tuple of ints, the shape of the video (num_frames, height, width, channels), where channels is 1 or 3.
`encoding_format`	The video is stored as a sequence of encoded images. You can use any encoding format supported by image_feature.Feature.
`ffmpeg_extra_args`	A sequence of additional args to be passed to the ffmpeg binary. Specifically, ffmpeg will be called as: `ffmpeg -i <input_file> <ffmpeg_extra_args> %010d.<encoding_format>`

Raises
`ValueError`	If the shape is invalid

Attributes
`dtype`	Return the dtype (or dict of dtype) of this FeatureConnector.
`feature`	The inner feature.
`shape`	Return the shape (or dict of shape) of this FeatureConnector.

Methods

`decode_batch_example`

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`

decode_example(
    serialized_example, decoders=None
)

Decode the serialize examples.

Args
`serialized_example`	Nested `dict` of `tf.Tensor`
`decoders`	Nested dict of `Decoder` objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.

Returns
`example`	Nested `dict` containing the decoded nested examples.

`decode_ragged_example`

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`

encode_example(
    video_or_path_or_fobj
)

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

`from_config`

@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`

@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`

@classmethod
from_json_content(
    value: Json
) -> "Video"

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`

get_serialized_info()

See base class for details.

`get_tensor_info`

get_tensor_info()

See base class for details.

`load_metadata`

load_metadata(
    *args, **kwargs
)

See base class for details.

`repr_html`

repr_html(
    ex: np.ndarray
) -> str

Video are displayed as GIFs.

`repr_html_batch`

repr_html_batch(
    ex: np.ndarray
) -> str

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

`repr_html_ragged`

repr_html_ragged(
    ex: np.ndarray
) -> str

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

`save_config`

save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

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

`save_metadata`

save_metadata(
    *args, **kwargs
)

See base class for details.

`to_json`

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",
            "num_classes": 10
        }
    }
}

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

`to_json_content`

to_json_content() -> 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.

`getitem`

__getitem__(
    key
)

Convenience method to access the underlying features.

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Last updated 2020-10-10 UTC.