Pipeline

class CVAugmentor.assets.main.pipeline.Pipeline

A class for handling data augmentation pipelines for images and videos.

augment(input_path: str, output_path: str, target: str, process_type: str, mode: str, augmentations: dict[str, Callable[[...], None]], verbose: bool | None = False, aug_verbose: bool | None = False, warn_verbose: bool | None = True, random_state: bool | None = False) → None

Augments the input data.

Parameters:

  • input_path (str) – Path to the input data.

  • output_path (str) – Path to the output data.

  • target (str) –

    Target of the augmentation.
    The options are:
    ”image”

    Specifies that the target of the augmentation is an image.

    ”video”

    Specifies that the target of the augmentation is a video.

  • process_type (str) –

    Type of the augmentation.
    The options are:
    ”single”

    Single type means that the input and output paths are files.

    ”batch”

    Batch type means that the input and output paths are directories.

  • mode (str) –

    Mode of the augmentation.
    The options are:
    ”sequential”

    Sequential mode means that the augmentations will be applied one by one. This means that the data will be saved after each augmentation.

    ”singular”

    Singular mode means that the augmentations will be applied all at once. This means that the data will be saved only after all augmentations were applied.

  • augmentations (dict) – A dictionary of augmentations to apply. Keys are augmentation names, and values are the corresponding functions.

  • verbose (bool, optional) – If True, prints the overall progress of the augmentation process. The default value is False. This is only applicable with the process_type=”batch”. In process_type=”single”, aug_verbose must be used.

  • aug_verbose (bool, optional) – If True, prints the progress of the augmentation process. The default value is False.

  • warn_verbose (bool, optional) – If True, prints the warnings. The default value is True.

  • random_state (bool, optional) – When set to True, it resets the states of the augmentations. The default value is False. This means that for any parameters that were randomly selected, new random values will be assigned. This functionality is particularly useful when augmenting a dataset where you want each sample to undergo the same augmentation process but with varying random behavior. However, you should only enable this option (True) if your augmentation dictionary contains unspecified parameters and this functionality is absolutely necessary. Otherwise, setting it to True may introduce unnecessary overhead.

Return type:

None.