def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], transform: Optional[Tensor] = None
) -> Tensor:
transform = cast(Tensor, transform)
return warp_perspective3d(
input,
transform,
(input.shape[-3], input.shape[-2], input.shape[-1]),
flags=self.flags["resample"].name.lower(),
align_corners=self.flags["align_corners"],
)
def apply_transform(
self, input: torch.Tensor, params: Dict[str, torch.Tensor], transform: Optional[torch.Tensor] = None
) -> torch.Tensor:
transform = cast(torch.Tensor, transform)
return warp_perspective3d(
input,
transform,
(input.shape[-3], input.shape[-2], input.shape[-1]),
flags=self.resample.name.lower(),
align_corners=self.align_corners,
)
def apply_perspective3d(input: torch.Tensor, params: Dict[str, torch.Tensor],
flags: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Perform perspective transform of the given torch.Tensor or batch of tensors.
Args:
input (torch.Tensor): Tensor to be transformed with shape (D, H, W), (C, D, H, W), (B, C, D, H, W).
params (Dict[str, torch.Tensor]):
- params['start_points']: Tensor containing [top-left, top-right, bottom-right,
bottom-left] of the orignal image with shape Bx8x3.
- params['end_points']: Tensor containing [top-left, top-right, bottom-right,
bottom-left] of the transformed image with shape Bx8x3.
flags (Dict[str, torch.Tensor]):
- params['interpolation']: Integer tensor. NEAREST = 0, BILINEAR = 1.
- params['align_corners']: Boolean tensor.
Returns:
torch.Tensor: Perspectively transformed tensor.
"""
input = _transform_input3d(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
_, _, depth, height, width = input.shape
# compute the homography between the input points
transform: torch.Tensor = compute_perspective_transformation3d(
input, params)
out_data: torch.Tensor = input.clone()
# apply the computed transform
depth, height, width = input.shape[-3:]
resample_name: str = Resample(flags['interpolation'].item()).name.lower()
align_corners: bool = cast(bool, flags['align_corners'].item())
out_data = warp_perspective3d(input,
transform, (depth, height, width),
flags=resample_name,
align_corners=align_corners)
return out_data.view_as(input)