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

from typing import Any, Dict, Optional, Tuple

from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.constants import BorderType
from kornia.filters import gaussian_blur2d


[docs]class RandomGaussianBlur(IntensityAugmentationBase2D): r"""Apply gaussian blur given tensor image or a batch of tensor images randomly. .. image:: _static/img/RandomGaussianBlur.png Args: kernel_size: the size of the kernel. sigma: the standard deviation of the kernel. border_type: the padding mode to be applied before convolving. The expected modes are: ``constant``, ``reflect``, ``replicate`` or ``circular``. 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). Shape: - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)` - Output: :math:`(B, C, H, W)` .. note:: This function internally uses :func:`kornia.filters.gaussian_blur2d`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 5, 5) >>> blur = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.) >>> blur(input) tensor([[[[0.6699, 0.4645, 0.3193, 0.1741, 0.1955], [0.5422, 0.6657, 0.6261, 0.6527, 0.5195], [0.3826, 0.2638, 0.1902, 0.1620, 0.2141], [0.6329, 0.6732, 0.5634, 0.4037, 0.2049], [0.8307, 0.6753, 0.7147, 0.5768, 0.7097]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = "reflect", same_on_batch: bool = False, p: float = 0.5, keepdim: bool = False, return_transform: Optional[bool] = None, ) -> 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, sigma=sigma, border_type=BorderType.get(border_type)) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: return gaussian_blur2d(input, flags["kernel_size"], flags["sigma"], flags["border_type"].name.lower())