mediapipe_model_maker.gesture_recognizer.Dataset

Dataset library for hand gesture recognizer.

Inherits From: ClassificationDataset, Dataset

mediapipe_model_maker.gesture_recognizer.Dataset( dataset: tf.data.Dataset, label_names: List[str], size: Optional[int] = None )

tf_dataset A tf.data.Dataset object that contains a potentially large set of elements, where each element is a pair of (input_data, target). The input_data means the raw input data, like an image, a text etc., while the target means the ground truth of the raw input data, e.g. the classification label of the image etc.
size The size of the dataset. tf.data.Dataset donesn't support a function to get the length directly since it's lazy-loaded and may be infinite.

label_names

num_classes

size Returns the size of the dataset.

Same functionality as calling len. See the len method definition for more information.

Methods

from_folder

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@classmethod from_folder( dirname: str, hparams: Optional[mediapipe_model_maker.gesture_recognizer.HandDataPreprocessingParams] = None ) -> mediapipe_model_maker.face_stylizer.dataset.classification_dataset.ClassificationDataset

Loads images and labels from the given directory.

Directory contents are expected to be in the format: //*.jpg". One of the gesture_name must be none (case insensitive). The none sub-directory is expected to contain images of hands that don't belong to other gesture classes in . Assumes the image data of the same label are in the same subdirectory.

Args
dirname Name of the directory containing the data files.
hparams Optional hyperparameters for processing input hand gesture images.

Returns
Dataset containing landmarks, labels, and other related info.

Raises
ValueError if the input data directory is empty or the label set does not contain label 'none' (case insensitive).

gen_tf_dataset

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gen_tf_dataset( batch_size: int = 1, is_training: bool = False, shuffle: bool = False, preprocess: Optional[Callable[..., Any]] = None, drop_remainder: bool = False ) -> tf.data.Dataset

Generates a batched tf.data.Dataset for training/evaluation.

Args
batch_size An integer, the returned dataset will be batched by this size.
is_training A boolean, when True, the returned dataset will be optionally shuffled and repeated as an endless dataset.
shuffle A boolean, when True, the returned dataset will be shuffled to create randomness during model training.
preprocess A function taking three arguments in order, feature, label and boolean is_training.
drop_remainder boolean, whether the finally batch drops remainder.

Returns
A TF dataset ready to be consumed by Keras model.

split

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split( fraction: float ) -> Tuple[ds._DatasetT, ds._DatasetT]

Splits dataset into two sub-datasets with the given fraction.

Primarily used for splitting the data set into training and testing sets.

Args
fraction float, demonstrates the fraction of the first returned subdataset in the original data.

Returns
The splitted two sub datasets.

__len__

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__len__() -> int

Returns the number of element of the dataset.

If size is not set, this method will fallback to using the len method of the tf.data.Dataset in self._dataset. Calling len on a tf.data.Dataset instance may throw a TypeError because the dataset may be lazy-loaded with an unknown size or have infinite size.

In most cases, however, when an instance of this class is created by helper functions like 'from_folder', the size of the dataset will be preprocessed, and the _size instance variable will be already set.

Raises
TypeError if self._size is not set and the cardinality of self._dataset is INFINITE_CARDINALITY or UNKNOWN_CARDINALITY.