from typing import Any, Dict, Optional, Tuple
from torch import Tensor
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.filters import box_blur
[docs]class RandomBoxBlur(IntensityAugmentationBase2D):
"""Add random blur with a box filter to an image tensor.
.. image:: _static/img/RandomBoxBlur.png
Args:
kernel_size: the blurring kernel size.
border_type: the padding mode to be applied before convolving.
The expected modes are: ``constant``, ``reflect``, ``replicate`` or ``circular``.
normalized: if True, L1 norm of the kernel is set to 1.
same_on_batch: apply the same transformation across the batch.
p: probability of applying the transformation.
keepdim: whether to keep the output shape the same as input (True) or broadcast it
to the batch form (False).
.. note::
This function internally uses :func:`kornia.filters.box_blur`.
Examples:
>>> img = torch.ones(1, 1, 24, 24)
>>> out = RandomBoxBlur((7, 7))(img)
>>> out.shape
torch.Size([1, 1, 24, 24])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomBoxBlur((7, 7), p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (3, 3),
border_type: str = "reflect",
normalized: bool = True,
same_on_batch: bool = False,
p: float = 0.5,
keepdim: bool = False,
) -> None:
super().__init__(p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim)
self.flags = {"kernel_size": kernel_size, "border_type": border_type, "normalized": normalized}
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
return box_blur(input, flags["kernel_size"], flags["border_type"], flags["normalized"])