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clahe

CLAHE

Bases: ImageOnlyAlbumentation

Apply contrast limited adaptive histogram equalization to the image.

Parameters:

Name Type Description Default
inputs Union[str, Iterable[str]]

Key(s) of images to be modified.

 required
outputs Union[str, Iterable[str]]

Key(s) into which to write the modified images.

 required
mode Union[None, str, Iterable[str]]

What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".

 None
ds_id Union[None, str, Iterable[str]]

What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".

 None
clip_limit Union[float, Tuple[float, float]]

upper threshold value for contrast limiting. If clip_limit is a single float value, the range will be (1, clip_limit).

 4.0
tile_grid_size Tuple[int, int]

size of grid for histogram equalization.

 (8, 8)
Image types

uint8

Source code in fastestimator/fastestimator/op/numpyop/univariate/clahe.py 
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@traceable()
class CLAHE(ImageOnlyAlbumentation):
    """Apply contrast limited adaptive histogram equalization to the image.

    Args:
        inputs: Key(s) of images to be modified.
        outputs: Key(s) into which to write the modified images.
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
        clip_limit: upper threshold value for contrast limiting. If clip_limit is a single float value, the range will
            be (1, clip_limit).
        tile_grid_size: size of grid for histogram equalization.

    Image types:
        uint8
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str]],
                 outputs: Union[str, Iterable[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None,
                 clip_limit: Union[float, Tuple[float, float]] = 4.0,
                 tile_grid_size: Tuple[int, int] = (8, 8)):
        super().__init__(CLAHEAlb(clip_limit=clip_limit, tile_grid_size=tile_grid_size, always_apply=True),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode,
                         ds_id=ds_id)