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random_shadow

RandomShadow

Bases: ImageOnlyAlbumentation

Add shadows to an image

Parameters:

Name Type Description Default
inputs Union[str, Sequence[str]]

Key(s) of images to be modified.

 required
outputs Union[str, Sequence[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
shadow_roi Tuple[float, float, float, float]

Region of the image where shadows will appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1].

 (0.0, 0.5, 1.0, 1.0)
num_shadows_lower int

Lower limit for the possible number of shadows. Should be in range [0, num_shadows_upper].

 1
num_shadows_upper int

Lower limit for the possible number of shadows. Should be in range [num_shadows_lower, inf].

 2
shadow_dimension int

Number of edges in the shadow polygons.

 5
Image types

uint8, float32

Source code in fastestimator/fastestimator/op/numpyop/univariate/random_shadow.py 
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@traceable()
class RandomShadow(ImageOnlyAlbumentation):
    """Add shadows to an 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".
        shadow_roi: Region of the image where shadows will appear (x_min, y_min, x_max, y_max).
            All values should be in range [0, 1].
        num_shadows_lower: Lower limit for the possible number of shadows. Should be in range [0, `num_shadows_upper`].
        num_shadows_upper: Lower limit for the possible number of shadows.
            Should be in range [`num_shadows_lower`, inf].
        shadow_dimension: Number of edges in the shadow polygons.

    Image types:
        uint8, float32
    """
    def __init__(self,
                 inputs: Union[str, Sequence[str]],
                 outputs: Union[str, Sequence[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None,
                 shadow_roi: Tuple[float, float, float, float] = (0.0, 0.5, 1.0, 1.0),
                 num_shadows_lower: int = 1,
                 num_shadows_upper: int = 2,
                 shadow_dimension: int = 5):
        warn("RandomShadow does not work with multi-threaded Pipelines. Either do not use this Op or else " +
             "set your Pipeline num_process=0")
        # TODO - Have pipeline look for bad ops and auto-magically set num_process correctly
        super().__init__(
            RandomShadowAlb(shadow_roi=shadow_roi,
                            num_shadows_lower=num_shadows_lower,
                            num_shadows_upper=num_shadows_upper,
                            shadow_dimension=shadow_dimension,
                            always_apply=True),
            inputs=inputs,
            outputs=outputs,
            mode=mode,
            ds_id=ds_id)