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'),
}

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>

ValueError If the shape is invalid

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

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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

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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

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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

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encode_example(
    video_or_path_or_fobj
)

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

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()

See base class for details.

load_metadata

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load_metadata(
    *args, **kwargs
)

See base class for details.

save_metadata

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save_metadata(
    *args, **kwargs
)

See base class for details.

__getitem__

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__getitem__(
    key
)

Convenience method to access the underlying features.