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gaussian_blur

GaussianBlur

Bases: ImageOnlyAlbumentation

Blur the image with a Gaussian filter of random kernel size.

Parameters:

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

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
blur_limit Union[int, Tuple[int, int]]

maximum Gaussian kernel size for blurring the input image. Should be odd and in range [3, inf).

 7
Image types

uint8, float32

Source code in fastestimator\fastestimator\op\numpyop\univariate\gaussian_blur.py 
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class GaussianBlur(ImageOnlyAlbumentation):
    """Blur the image with a Gaussian filter of random kernel size.

    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".
        blur_limit: maximum Gaussian kernel size for blurring the input image. Should be odd and in range [3, inf).

    Image types:
        uint8, float32
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str], Callable],
                 outputs: Union[str, Iterable[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 blur_limit: Union[int, Tuple[int, int]] = 7):
        super().__init__(GaussianBlurAlb(blur_limit=blur_limit, always_apply=True),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode)