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]]]).repeat(B, 1, 1)  # 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"])
```