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gaussian_noise

GaussianNoise

Bases: ImageOnlyAlbumentation

Apply gaussian noise to the image.

WARNING: This assumes that floating point images are in the range [0,1] and will trim the output to that range. If your image is in a range like [-0.5, 0.5] then you do not want to use this Op.

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
var_limit Union[float, Tuple[float, float]]

Variance range for noise. If var_limit is a single float, the range will be (0, var_limit).

 (10.0, 50.0)
mean float

Mean of the noise.

 0.0
Image types

uint8, float32

Source code in fastestimator/fastestimator/op/numpyop/univariate/gaussian_noise.py 
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@traceable()
class GaussianNoise(ImageOnlyAlbumentation):
    """Apply gaussian noise to the image.

    WARNING: This assumes that floating point images are in the range [0,1] and will trim the output to that range. If
    your image is in a range like [-0.5, 0.5] then you do not want to use this Op.

    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".
        var_limit: Variance range for noise. If var_limit is a single float, the range will be (0, var_limit).
        mean: Mean of the noise.

    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,
                 var_limit: Union[float, Tuple[float, float]] = (10.0, 50.0),
                 mean: float = 0.0):
        super().__init__(GaussNoiseAlb(var_limit=var_limit, mean=mean, always_apply=True),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode,
                         ds_id=ds_id)