def apply_crop(input: torch.Tensor,
params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Apply cropping by src bounding box and dst bounding box.
Order: top-left, top-right, bottom-right and bottom-left. The coordinates must be in the x, y order.
Args:
input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W).
params (Dict[str, torch.Tensor]):
- params['src']: The applied cropping src matrix :math: `(*, 4, 2)`.
- params['dst']: The applied cropping dst matrix :math: `(*, 4, 2)`.
- params['interpolation']: Integer tensor. NEAREST = 0, BILINEAR = 1.
- params['align_corners']: Boolean tensor.
Returns:
torch.Tensor: The cropped input.
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
resample_mode: str = Resample.get(
params['interpolation'].item()).name.lower() # type: ignore
align_corners: bool = cast(bool, params['align_corners'].item())
return crop_by_boxes(input,
params['src'],
params['dst'],
resample_mode,
align_corners=align_corners)
def apply_crop(input: torch.Tensor,
params: Dict[str, torch.Tensor],
return_transform: bool = False) -> UnionType:
"""
Args:
params (dict): A dict that must have {'src': torch.Tensor, 'dst': torch.Tensor}. Can be generated from
kornia.augmentation.random_generator.random_crop_generator
return_transform (bool): if ``True`` return the matrix describing the transformation applied to each
input tensor.
Returns:
torch.Tensor: The grayscaled input
torch.Tensor: The applied cropping matrix :math: `(*, 4, 2)` if return_transform flag
is set to ``True``
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
return crop_by_boxes(
input,
params['src'],
params['dst'],
Resample.get(
params['interpolation'].item()).name.lower(), # type: ignore
return_transform=return_transform)