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sharpness

Sharpness

Bases: NumpyOp

Randomly change the sharpness of an image.

This is a wrapper for functionality provided by the PIL library: https://github.com/python-pillow/Pillow/tree/master/src/PIL.

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
limit float

Factor range for changing sharpness. If limit is a single float, the range will be (-limit, limit). A factor of 0.0 gives a blurred image, a factor of 1.0 gives the original image, and a factor of 2.0 gives a sharpened image.

 0.54
Image types

uint8

Source code in fastestimator/fastestimator/op/numpyop/univariate/sharpness.py 
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@traceable()
class Sharpness(NumpyOp):
    """Randomly change the sharpness of an image.

    This is a wrapper for functionality provided by the PIL library:
    https://github.com/python-pillow/Pillow/tree/master/src/PIL.

    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".
        limit: Factor range for changing sharpness. If limit is a single float, the range will be (-limit, limit).
            A factor of 0.0 gives a blurred image, a factor of 1.0 gives the original image, and a factor of 2.0 gives
            a sharpened image.

    Image types:
        uint8
    """
    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,
                 limit: float = 0.54):
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
        self.limit = param_to_range(limit)
        self.in_list, self.out_list = True, True

    def set_rua_level(self, magnitude_coef: float) -> None:
        """Set the augmentation intensity based on the magnitude_coef.

        This method is specifically designed to be invoked by the RUA Op.

        Args:
            magnitude_coef: The desired augmentation intensity (range [0-1]).
        """
        param_mid = (self.limit[1] + self.limit[0]) / 2
        param_extent = magnitude_coef * ((self.limit[1] - self.limit[0]) / 2)
        self.limit = (param_mid - param_extent, param_mid + param_extent)

    def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> List[np.ndarray]:
        factor = 1.0 + random.uniform(self.limit[0], self.limit[1])
        return [Sharpness._apply_sharpness(elem, factor) for elem in data]

    @staticmethod
    def _apply_sharpness(data: np.ndarray, factor: float) -> np.ndarray:
        im = Image.fromarray(data)
        im = ImageEnhance.Sharpness(im).enhance(factor)
        return np.array(im)

set_rua_level

Set the augmentation intensity based on the magnitude_coef.

This method is specifically designed to be invoked by the RUA Op.

Parameters:

Name Type Description Default
magnitude_coef float

The desired augmentation intensity (range [0-1]).

 required
Source code in fastestimator/fastestimator/op/numpyop/univariate/sharpness.py 
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def set_rua_level(self, magnitude_coef: float) -> None:
    """Set the augmentation intensity based on the magnitude_coef.

    This method is specifically designed to be invoked by the RUA Op.

    Args:
        magnitude_coef: The desired augmentation intensity (range [0-1]).
    """
    param_mid = (self.limit[1] + self.limit[0]) / 2
    param_extent = magnitude_coef * ((self.limit[1] - self.limit[0]) / 2)
    self.limit = (param_mid - param_extent, param_mid + param_extent)