Source code for kornia.augmentation._3d.geometric.center_crop

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

from torch import Size, Tensor

from kornia.augmentation import random_generator as rg
from kornia.augmentation._3d.base import AugmentationBase3D
from kornia.constants import Resample
from kornia.geometry import crop_by_transform_mat3d, get_perspective_transform3d


[docs]class CenterCrop3D(AugmentationBase3D): r"""Apply center crop on 3D volumes (5D tensor). Args: p: probability of applying the transformation for the whole batch. size (Tuple[int, int, int] or int): Desired output size (out_d, out_h, out_w) of the crop. If integer, out_d = out_h = out_w = size. If Tuple[int, int, int], out_d = size[0], out_h = size[1], out_w = size[2]. resample: resample mode from "nearest" (0) or "bilinear" (1). return_transform: if ``True`` return the matrix describing the transformation applied to each input tensor. If ``False`` and the input is a tuple the applied transformation won't be concatenated. align_corners: interpolation flag. keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False). Shape: - Input: :math:`(C, D, H, W)` or :math:`(B, C, D, H, W)`, Optional: :math:`(B, 4, 4)` - Output: :math:`(B, C, out_d, out_h, out_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, 4, 4)`), then the applied transformation will be merged int to the input transformation tensor and returned. Examples: >>> import torch >>> rng = torch.manual_seed(0) >>> inputs = torch.randn(1, 1, 2, 4, 6) >>> inputs tensor([[[[[-1.1258, -1.1524, -0.2506, -0.4339, 0.8487, 0.6920], [-0.3160, -2.1152, 0.3223, -1.2633, 0.3500, 0.3081], [ 0.1198, 1.2377, 1.1168, -0.2473, -1.3527, -1.6959], [ 0.5667, 0.7935, 0.5988, -1.5551, -0.3414, 1.8530]], <BLANKLINE> [[ 0.7502, -0.5855, -0.1734, 0.1835, 1.3894, 1.5863], [ 0.9463, -0.8437, -0.6136, 0.0316, -0.4927, 0.2484], [ 0.4397, 0.1124, 0.6408, 0.4412, -0.1023, 0.7924], [-0.2897, 0.0525, 0.5229, 2.3022, -1.4689, -1.5867]]]]]) >>> aug = CenterCrop3D(2, p=1.) >>> aug(inputs) tensor([[[[[ 0.3223, -1.2633], [ 1.1168, -0.2473]], <BLANKLINE> [[-0.6136, 0.0316], [ 0.6408, 0.4412]]]]]) To apply the exact augmenation again, you may take the advantage of the previous parameter state: >>> input = torch.rand(1, 3, 32, 32, 32) >>> aug = CenterCrop3D(24, p=1.) >>> (aug(input) == aug(input, params=aug._params)).all() tensor(True) """ def __init__( self, size: Union[int, Tuple[int, int, int]], align_corners: bool = True, resample: Union[str, int, Resample] = Resample.BILINEAR.name, p: float = 1.0, keepdim: bool = False, return_transform: Optional[bool] = None, ) -> None: # same_on_batch is always True for CenterCrop # Since PyTorch does not support ragged tensor. So cropping function happens batch-wisely. super().__init__(p=1.0, return_transform=return_transform, same_on_batch=True, p_batch=p, keepdim=keepdim) if isinstance(size, tuple): self.size = (size[0], size[1], size[2]) elif isinstance(size, int): self.size = (size, size, size) else: raise Exception(f"Invalid size type. Expected (int, tuple(int, int int). Got: {size}.") self.flags = dict(align_corners=align_corners, resample=Resample.get(resample)) def generate_parameters(self, batch_shape: Size) -> Dict[str, Tensor]: return rg.center_crop_generator3d( batch_shape[0], batch_shape[-3], batch_shape[-2], batch_shape[-1], self.size, device=self.device ) def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor: transform: Tensor = get_perspective_transform3d(params["src"].to(input), params["dst"].to(input)) transform = transform.expand(input.shape[0], -1, -1) return transform def apply_transform( self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None ) -> Tensor: transform = cast(Tensor, transform) return crop_by_transform_mat3d( input, transform, self.size, mode=flags["resample"].name.lower(), align_corners=flags["align_corners"] )