# 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))
```