Source code for kornia.augmentation._2d.geometric.affine

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

import torch

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
from kornia.constants import Resample, SamplePadding
from kornia.core import Tensor
from kornia.geometry.conversions import deg2rad
from kornia.geometry.transform import get_affine_matrix2d, warp_affine

[docs]class RandomAffine(GeometricAugmentationBase2D): r"""Apply a random 2D affine transformation to a tensor image. .. image:: _static/img/RandomAffine.png The transformation is computed so that the image center is kept invariant. Args: degrees: Range of degrees to select from. If degrees is a number instead of sequence like (min, max), the range of degrees will be (-degrees, +degrees). Set to 0 to deactivate rotations. translate: tuple of maximum absolute fraction for horizontal and vertical translations. For example translate=(a, b), then horizontal shift is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default. scale: scaling factor interval. If (a, b) represents isotropic scaling, the scale is randomly sampled from the range a <= scale <= b. If (a, b, c, d), the scale is randomly sampled from the range a <= scale_x <= b, c <= scale_y <= d. Will keep original scale by default. shear: Range of degrees to select from. If float, a shear parallel to the x axis in the range (-shear, +shear) will be applied. If (a, b), a shear parallel to the x axis in the range (-shear, +shear) will be applied. If (a, b, c, d), then x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied. Will not apply shear by default. resample: resample mode from "nearest" (0) or "bilinear" (1). padding_mode: padding mode from "zeros" (0), "border" (1) or "refection" (2). same_on_batch: apply the same transformation across the batch. align_corners: interpolation flag. p: probability of applying the transformation. 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.geometry.transform.warp_affine`. Examples: >>> import torch >>> rng = torch.manual_seed(0) >>> input = torch.rand(1, 1, 3, 3) >>> aug = RandomAffine((-15., 20.), p=1.) >>> out = aug(input) >>> out, aug.transform_matrix (tensor([[[[0.3961, 0.7310, 0.1574], [0.1781, 0.3074, 0.5648], [0.4804, 0.8379, 0.4234]]]]), tensor([[[ 0.9923, -0.1241, 0.1319], [ 0.1241, 0.9923, -0.1164], [ 0.0000, 0.0000, 1.0000]]])) >>> aug.inverse(out) tensor([[[[0.3890, 0.6573, 0.1865], [0.2063, 0.3074, 0.5459], [0.3892, 0.7896, 0.4224]]]]) >>> input tensor([[[[0.4963, 0.7682, 0.0885], [0.1320, 0.3074, 0.6341], [0.4901, 0.8964, 0.4556]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomAffine((-15., 20.), p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, degrees: Union[Tensor, float, Tuple[float, float]], translate: Optional[Union[Tensor, Tuple[float, float]]] = None, scale: Optional[Union[Tensor, Tuple[float, float], Tuple[float, float, float, float]]] = None, shear: Optional[Union[Tensor, float, Tuple[float, float]]] = None, resample: Union[str, int, Resample] =, same_on_batch: bool = False, align_corners: bool = False, padding_mode: Union[str, int, SamplePadding] =, 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.AffineGenerator = rg.AffineGenerator(degrees, translate, scale, shear) self.flags = dict( resample=Resample.get(resample), padding_mode=SamplePadding.get(padding_mode), align_corners=align_corners ) def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor: return get_affine_matrix2d( torch.as_tensor(params["translations"], device=input.device, dtype=input.dtype), torch.as_tensor(params["center"], device=input.device, dtype=input.dtype), torch.as_tensor(params["scale"], device=input.device, dtype=input.dtype), torch.as_tensor(params["angle"], device=input.device, dtype=input.dtype), deg2rad(torch.as_tensor(params["sx"], device=input.device, dtype=input.dtype)), deg2rad(torch.as_tensor(params["sy"], device=input.device, dtype=input.dtype)), ) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: _, _, height, width = input.shape transform = cast(Tensor, transform) return warp_affine( input, transform[:, :2, :], (height, width), flags["resample"].name.lower(), align_corners=flags["align_corners"], padding_mode=flags["padding_mode"].name.lower(), ) def inverse_transform( self, input: Tensor, flags: Dict[str, Any], transform: Optional[Tensor] = None, size: Optional[Tuple[int, int]] = None, ) -> Tensor: return self.apply_transform( input, params=self._params, transform=torch.as_tensor(transform, device=input.device, dtype=input.dtype), flags=flags, )