Source code for kornia.augmentation._2d.intensity.box_blur

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"])