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

from typing import Any, Dict, Optional

import torch
from torch import Tensor

from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
from kornia.geometry.transform import get_tps_transform, warp_image_tps


[docs]class RandomThinPlateSpline(GeometricAugmentationBase2D): r"""Add random noise to the Thin Plate Spline algorithm. .. image:: _static/img/RandomThinPlateSpline.png Args: scale: the scale factor to apply to the destination points. align_corners: Interpolation flag used by ``grid_sample``. mode: Interpolation mode used by `grid_sample`. Either 'bilinear' or 'nearest'. same_on_batch: apply the same transformation across the batch. 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). .. note:: This function internally uses :func:`kornia.geometry.transform.warp_image_tps`. Examples: >>> img = torch.ones(1, 1, 2, 2) >>> out = RandomThinPlateSpline()(img) >>> out.shape torch.Size([1, 1, 2, 2]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.randn(1, 3, 32, 32) >>> aug = RandomThinPlateSpline(p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, scale: float = 0.2, align_corners: bool = False, 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, p_batch=1.0, keepdim=keepdim ) self.flags = dict(align_corners=align_corners) self.dist = torch.distributions.Uniform(-scale, scale) def generate_parameters(self, shape: torch.Size) -> Dict[str, Tensor]: B, _, _, _ = shape src = torch.tensor([[[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, 1.0], [0.0, 0.0]]]).expand(B, 5, 2) # Bx5x2 dst = src + self.dist.rsample(src.shape) return dict(src=src, dst=dst) # TODO: It is incorrect to return identity def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor: return self.identity_matrix(input) def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: src = params["src"].to(input) dst = params["dst"].to(input) # NOTE: warp_image_tps need to use inverse parameters kernel, affine = get_tps_transform(dst, src) return warp_image_tps(input, src, kernel, affine, flags["align_corners"])