def recall(self, image, mask):
if self.p <= self.probability:
image = ReplayCompose.replay(self.replay, image=image)["image"]
if self.apply_to_mask:
mask = ReplayCompose.replay(self.replay, image=mask)["image"]
return image, mask
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".
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,
func: ImageOnlyTransform,
inputs: Union[str, List[str]],
outputs: Union[str, List[str]],
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None):
super().__init__(inputs=inputs,
outputs=outputs,
mode=mode,
ds_id=ds_id)
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]
class Transformation(object):
def __init__(self, transformation, probability, apply_to_mask=False):
self.transformation = transformation
self.transformation.p = 1
self.transformation = ReplayCompose([self.transformation])
self.probability = probability
self.p = None
self.apply_to_mask = apply_to_mask
def __call__(self, image, mask):
self.p = random()
return self._call(image, mask)
def _call(self, image, mask):
if self.p <= self.probability:
data = self.transformation(image=image, force_apply=True)
image = data["image"]
self.replay = data["replay"]
if self.apply_to_mask:
mask = self.transformation.replay(self.replay,
image=mask)["image"]
mask[mask > 1] = 255
mask[mask <= 1] = 0
return image, mask
def recall(self, image, mask):
if self.p <= self.probability:
image = ReplayCompose.replay(self.replay, image=image)["image"]
if self.apply_to_mask:
mask = ReplayCompose.replay(self.replay, image=mask)["image"]
return image, mask