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iaa_additive_gaussian_noise

IAAAdditiveGaussianNoise

Bases: ImageOnlyAlbumentation

Add gaussian noise to the input 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
loc int

mean of the normal distribution that generates the noise. Default: 0.

 0
scale Tuple[float, float]

standard deviation of the normal distribution that generates the noise. Default: (0.01 * 255, 0.05 * 255)

 (2.5500000000000003, 12.75)
Image types

uint8, float32

Source code in fastestimator/fastestimator/op/numpyop/univariate/iaa_additive_gaussian_noise.py 
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@traceable()
class IAAAdditiveGaussianNoise(ImageOnlyAlbumentation):
    """Add gaussian noise to the input 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".
        loc: mean of the normal distribution that generates the noise. Default: 0.
        scale: standard deviation of the normal distribution that generates the noise. Default: (0.01 * 255, 0.05 * 255)

    Image types:
        uint8, float32
    """
    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,
                 loc: int = 0,
                 scale:Tuple[float, float] =(2.5500000000000003, 12.75)):
        super().__init__(
            IAAAdditiveGaussianNoiseAlb(loc=loc, scale=scale, always_apply=True),
            inputs=inputs,
            outputs=outputs,
            mode=mode,
            ds_id=ds_id)