Source code for dicaugment.augmentations.dropout.functional

from typing import Iterable, List, Tuple, Union

import numpy as np

from dicaugment.augmentations.utils import preserve_shape

__all__ = [
    "cutout",
    #"channel_dropout"
    ]


# @preserve_shape
# def channel_dropout(
#     img: np.ndarray, channels_to_drop: Union[int, Tuple[int, ...], np.ndarray], fill_value: Union[int, float] = 0
# ) -> np.ndarray:
#     if len(img.shape) == 2 or img.shape[2] == 1:
#         raise NotImplementedError("Only one channel. ChannelDropout is not defined.")

#     img = img.copy()
#     img[..., channels_to_drop] = fill_value
#     return img


[docs]def cutout( img: np.ndarray, holes: Iterable[Tuple[int, int, int, int, int, int]], fill_value: Union[int, float] = 0 ) -> np.ndarray: # Make a copy of the input image since we don't want to modify it directly img = img.copy() for x1, y1, z1, x2, y2, z2 in holes: img[y1:y2, x1:x2, z1:z2] = fill_value return img