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

from typing import Any, Dict, Optional, Tuple, Union

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.core import Tensor
from kornia.enhance import sharpness


[docs]class RandomSharpness(IntensityAugmentationBase2D): r"""Sharpen given tensor image or a batch of tensor images randomly. .. image:: _static/img/RandomSharpness.png Args: p: probability of applying the transformation. sharpness: factor of sharpness strength. Must be above 0. same_on_batch: apply the same transformation across the batch. 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.enhance.sharpness`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 5, 5) >>> sharpness = RandomSharpness(1., p=1.) >>> sharpness(input) tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074], [0.6341, 0.4810, 0.7367, 0.4177, 0.6323], [0.3489, 0.4428, 0.1562, 0.2443, 0.2939], [0.5185, 0.6462, 0.7050, 0.2288, 0.2823], [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomSharpness(1., p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, sharpness: Union[Tensor, float, Tuple[float, float], Tensor] = 0.5, 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, keepdim=keepdim) self._param_generator = rg.PlainUniformGenerator((sharpness, "sharpness", 0.0, (0, float("inf")))) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: factor = params["sharpness"] return sharpness(input, factor)