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zoom_blur

ZoomBlur

Bases: ImageOnlyAlbumentation

Apply zoom blur transform.

Parameters:

Name Type Description Default
max_factor Union[float, Tuple[float, float]]

range for max factor for blurring. If max_factor is a single float, the range will be (1, limit). All max_factor values should be larger than 1. If max_factor is a tuple, it represents the (min, max) range for the factor.

 (1, 1.31)
step_factor Union[float, Tuple[float, float]]

Step size for zoom. All step_factor values should be positive. If single float will be used as step parameter for np.arange. If tuple of float step_factor will be in range [step_factor[0], step_factor[1])

 (0.01, 0.03)
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
Source code in fastestimator/fastestimator/op/numpyop/univariate/zoom_blur.py 
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@traceable()
class ZoomBlur(ImageOnlyAlbumentation):
    """Apply zoom blur transform.

    Args:
        max_factor: range for max factor for blurring. If max_factor is a single float, the range will be (1, limit).
            All max_factor values should be larger than 1. If max_factor is a tuple, it represents the
            (min, max) range for the factor.
        step_factor: Step size for zoom. All step_factor values should be positive. If single float will be used as
            step parameter for np.arange. If tuple of float step_factor will be in range
            [step_factor[0], step_factor[1])
        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".
    """
    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,
                 max_factor: Union[float, Tuple[float, float]] = (1,1.31),
                 step_factor: Union[float, Tuple[float, float]] = (0.01,0.03)
                 ):
        super().__init__(ZoomBlurAlb(max_factor=max_factor,
                                     step_factor=step_factor,
                                     always_apply=True),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode,
                         ds_id=ds_id)