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

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 posterize


[docs]class RandomPosterize(IntensityAugmentationBase2D): r"""Posterize given tensor image or a batch of tensor images randomly. .. image:: _static/img/RandomPosterize.png Args: p: probability of applying the transformation. bits: Integer that ranged from (0, 8], in which 0 gives black image and 8 gives the original. If int x, bits will be generated from (x, 8). If tuple (x, y), bits 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.posterize`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 5, 5) >>> posterize = RandomPosterize(3, p=1.) >>> posterize(input) tensor([[[[0.4706, 0.7529, 0.0627, 0.1255, 0.2824], [0.6275, 0.4706, 0.8784, 0.4392, 0.6275], [0.3451, 0.3765, 0.0000, 0.1569, 0.2824], [0.5020, 0.6902, 0.7843, 0.1569, 0.2510], [0.6588, 0.9098, 0.3765, 0.8471, 0.4078]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomPosterize(3, p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, bits: Union[int, Tuple[int, int], Tensor] = 3, 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) # TODO: the generator should receive the device self._param_generator = rg.PosterizeGenerator(bits) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: return posterize(input, params["bits_factor"].to(input.device))