CoarseDropout

Description

CoarseDropout randomly drops out rectangular regions from the image and optionally,
    the corresponding regions in an associated mask, to simulate occlusion and
    varied object sizes found in real-world settings.

    This transformation is an evolution of CutOut and RandomErasing, offering more
    flexibility in the size, number of dropout regions, and fill values.

    Args:
        num_holes_range (tuple[int, int]): Range (min, max) for the number of rectangular
            regions to drop out. Default: (1, 1)
        hole_height_range (tuple[ScalarType, ScalarType]): Range (min, max) for the height
            of dropout regions. If int, specifies absolute pixel values. If float,
            interpreted as a fraction of the image height. Default: (8, 8)
        hole_width_range (tuple[ScalarType, ScalarType]): Range (min, max) for the width
            of dropout regions. If int, specifies absolute pixel values. If float,
            interpreted as a fraction of the image width. Default: (8, 8)
        fill_value (int | float | Literal["random"] | tuple[int | float,...]): Value for the dropped pixels. Can be:
            - int or float: all channels are filled with this value.
            - tuple: tuple of values for each channel.
            - 'random': filled with random values.
            Default: 0.
        mask_fill_value (ColorType | None): Fill value for dropout regions in the mask.
            If None, mask regions corresponding to image dropouts are unchanged. Default: None
        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - The actual number and size of dropout regions are randomly chosen within the specified ranges for each
            application.
        - When using float values for hole_height_range and hole_width_range, ensure they are between 0 and 1.
        - This implementation includes deprecation warnings for older parameter names (min_holes, max_holes, etc.).

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> mask = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
        >>> augmentation = A.CoarseDropout(num_holes_range=(3, 6),
        ...                                hole_height_range=(10, 20),
        ...                                hole_width_range=(10, 20),
        ...                                fill_value=0,
        ...                                p=1.0)
        >>> transformed = augmentation(image=image, mask=mask)
        >>> transformed_image, transformed_mask = transformed["image"], transformed["mask"]

    References:
        - CutOut: https://arxiv.org/abs/1708.04552
        - Random Erasing: https://arxiv.org/abs/1708.04896
    

Parameters

• fill_value: float | Sequence[float] | Literal['random'] (default: 0)
• mask_fill_value: float | Sequence[float] | None (default: null)
• num_holes_range: tuple[int, int] (default: (1, 1))
• hole_height_range: tuple[int | float, int | float] (default: (8, 8))
• hole_width_range: tuple[int | float, int | float] (default: (8, 8))
• p: float (default: 0.5)

Targets

• Image
• Mask
• BBoxes
• Keypoints