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

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

import torch
from torch import Tensor

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.constants import BorderType, Resample
from kornia.filters import motion_blur

[docs]class RandomMotionBlur(IntensityAugmentationBase2D): r"""Perform motion blur on 2D images (4D tensor). .. image:: _static/img/RandomMotionBlur.png Args: p: probability of applying the transformation. kernel_size: motion kernel size (odd and positive). If int, the kernel will have a fixed size. If Tuple[int, int], it will randomly generate the value from the range batch-wisely. angle: angle of the motion blur in degrees (anti-clockwise rotation). If float, it will generate the value from (-angle, angle). direction: forward/backward direction of the motion blur. Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle), while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a uniformly (but still angled) motion blur. If float, it will generate the value from (-direction, direction). If Tuple[int, int], it will randomly generate the value from the range. border_type: the padding mode to be applied before convolving. CONSTANT = 0, REFLECT = 1, REPLICATE = 2, CIRCULAR = 3. resample: the interpolation mode. 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: Input tensor must be float and normalized into [0, 1] for the best differentiability support. Additionally, this function accepts another transformation tensor (:math:`(B, 3, 3)`), then the applied transformation will be merged int to the input transformation tensor and returned. Please set ``resample`` to ``'bilinear'`` if more meaningful gradients wanted. .. note:: This function internally uses :func:`kornia.filters.motion_blur`. Examples: >>> rng = torch.manual_seed(0) >>> input = torch.ones(1, 1, 5, 5) >>> motion_blur = RandomMotionBlur(3, 35., 0.5, p=1.) >>> motion_blur(input) tensor([[[[0.5773, 1.0000, 1.0000, 1.0000, 0.7561], [0.5773, 1.0000, 1.0000, 1.0000, 0.7561], [0.5773, 1.0000, 1.0000, 1.0000, 0.7561], [0.5773, 1.0000, 1.0000, 1.0000, 0.7561], [0.5773, 1.0000, 1.0000, 1.0000, 0.7561]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomMotionBlur(3, 35., 0.5, p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, kernel_size: Union[int, Tuple[int, int]], angle: Union[Tensor, float, Tuple[float, float]], direction: Union[Tensor, float, Tuple[float, float]], border_type: Union[int, str, BorderType] =, resample: Union[str, int, Resample] =, 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.MotionBlurGenerator(kernel_size, angle, direction) self.flags = dict(border_type=BorderType.get(border_type), resample=Resample.get(resample)) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: # sample a kernel size kernel_size_list: List[int] = params["ksize_factor"].tolist() idx: int = cast(int, torch.randint(len(kernel_size_list), (1,)).item()) return motion_blur( input, kernel_size=kernel_size_list[idx], angle=params["angle_factor"], direction=params["direction_factor"], border_type=flags["border_type"].name.lower(), mode=flags["resample"].name.lower(), )