from typing import Dict, Optional, Tuple
import torch
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.
return_transform: if ``True`` return the matrix describing the transformation applied to each
input tensor. If ``False`` and the input is a tuple the applied transformation won't be concatenated.
same_on_batch (bool): 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,
return_transform: bool = False,
same_on_batch: bool = False,
p: float = 0.5,
keepdim: bool = False,
) -> None:
super().__init__(
p=p, return_transform=return_transform, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim
)
self.flags = dict(kernel_size=kernel_size, border_type=border_type, normalized=normalized)
def compute_transformation(self, input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
return self.identity_matrix(input)
def apply_transform(
self, input: torch.Tensor, params: Dict[str, torch.Tensor], transform: Optional[torch.Tensor] = None
) -> torch.Tensor:
return box_blur(input, self.flags["kernel_size"], self.flags["border_type"], self.flags["normalized"])