univariate

ImageOnlyAlbumentation

Bases: NumpyOp

Operators which apply to single images (as opposed to images + masks or images + bounding boxes).

This is a wrapper for functionality provided by the Albumentations library: https://github.com/albumentations-team/albumentations. A useful visualization tool for many of the possible effects it provides is available at https://albumentations-demo.herokuapp.com.

Parameters:

Name	Type	Description	Default
func	ImageOnlyTransform	An Albumentation function to be invoked.	required
inputs	Union[str, List[str]]	Key(s) from which to retrieve data from the data dictionary. If more than one key is provided, the func will be run in replay mode so that the exact same augmentation is applied to each value.	required
outputs	Union[str, List[str]]	Key(s) under which to write the outputs of this Op back to the data dictionary.	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

Source code in fastestimator\fastestimator\op\numpyop\univariate\univariate.py

@traceable()
class ImageOnlyAlbumentation(NumpyOp):
    """Operators which apply to single images (as opposed to images + masks or images + bounding boxes).

    This is a wrapper for functionality provided by the Albumentations library:
    https://github.com/albumentations-team/albumentations. A useful visualization tool for many of the possible effects
    it provides is available at https://albumentations-demo.herokuapp.com.

    Args:
        func: An Albumentation function to be invoked.
        inputs: Key(s) from which to retrieve data from the data dictionary. If more than one key is provided, the
            `func` will be run in replay mode so that the exact same augmentation is applied to each value.
        outputs: Key(s) under which to write the outputs of this Op back to the data dictionary.
        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".
    """
    def __init__(self,
                 func: ImageOnlyTransform,
                 inputs: Union[str, List[str]],
                 outputs: Union[str, List[str]],
                 mode: Union[None, str, Iterable[str]] = None):
        super().__init__(inputs=inputs, outputs=outputs, mode=mode)
        assert len(self.inputs) == len(self.outputs), "Input and Output lengths must match"
        self.func = Compose(transforms=[func])
        self.replay_func = ReplayCompose(transforms=[deepcopy(func)])
        self.in_list, self.out_list = True, True

    def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> List[np.ndarray]:
        results = [self.replay_func(image=data[0]) if len(data) > 1 else self.func(image=data[0])]
        for i in range(1, len(data)):
            results.append(self.replay_func.replay(results[0]['replay'], image=data[i]))
        return [result["image"] for result in results]