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

from typing import Any, Dict, List, 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 solarize


[docs]class RandomSolarize(IntensityAugmentationBase2D): r"""Solarize given tensor image or a batch of tensor images randomly. .. image:: _static/img/RandomSolarize.png Args: p: probability of applying the transformation. thresholds: If float x, threshold will be generated from (0.5 - x, 0.5 + x). If tuple (x, y), threshold will be generated from (x, y). additions: If float x, addition will be generated from (-x, x). If tuple (x, y), addition will be generated from (x, y). 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.solarize`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 5, 5) >>> solarize = RandomSolarize(0.1, 0.1, p=1.) >>> solarize(input) tensor([[[[0.4132, 0.1412, 0.1790, 0.2226, 0.3980], [0.2754, 0.4194, 0.0130, 0.4538, 0.2771], [0.4394, 0.4923, 0.1129, 0.2594, 0.3844], [0.3909, 0.2118, 0.1094, 0.2516, 0.3728], [0.2278, 0.0000, 0.4876, 0.0353, 0.5100]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomSolarize(0.1, 0.1, p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, thresholds: Union[Tensor, float, Tuple[float, float], List[float]] = 0.1, additions: Union[Tensor, float, Tuple[float, float], List[float]] = 0.1, 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( (thresholds, "thresholds", 0.5, (0.0, 1.0)), (additions, "additions", 0.0, (-0.5, 0.5)) ) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: thresholds = params["thresholds"] additions: Optional[Tensor] if "additions" in params: additions = params["additions"] else: additions = None return solarize(input, thresholds, additions)