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equalize

Equalize

Bases: ImageOnlyAlbumentation

Equalize the image histogram.

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
eq_mode str

{'cv', 'pil'}. Use OpenCV or Pillow equalization method.

 'cv'
by_channels bool

If True, use equalization by channels separately, else convert image to YCbCr representation and use equalization by Y channel.

 True
mask Union[None, ndarray]

If given, only the pixels selected by the mask are included in the analysis. May be 1 channel or 3 channel array. Function signature must include image argument.

 None
mask_params List[str]

Params for mask function.

 ()
Image types

uint8

Source code in fastestimator/fastestimator/op/numpyop/univariate/equalize.py 
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@traceable()
class Equalize(ImageOnlyAlbumentation):
    """Equalize the image histogram.

    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".
        eq_mode: {'cv', 'pil'}. Use OpenCV or Pillow equalization method.
        by_channels: If True, use equalization by channels separately, else convert image to YCbCr representation and
            use equalization by `Y` channel.
        mask: If given, only the pixels selected by the mask are included in the analysis. May be 1 channel or 3 channel
            array. Function signature must include `image` argument.
        mask_params: Params for mask function.

    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,
                 eq_mode: str = "cv",
                 by_channels: bool = True,
                 mask: Union[None, np.ndarray] = None,
                 mask_params: List[str] = ()):
        super().__init__(
            EqualizeAlb(mode=eq_mode, by_channels=by_channels, mask=mask, mask_params=mask_params, always_apply=True),
            inputs=inputs,
            outputs=outputs,
            mode=mode,
            ds_id=ds_id)