def compute_perspective_transformation3d(
input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Compute the applied transformation matrix :math: `(*, 4, 4)`.
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.
Returns:
torch.Tensor: The applied transformation matrix :math: `(*, 4, 4)`
"""
input = _transform_input3d(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
perspective_transform: torch.Tensor = get_perspective_transform3d(
params['start_points'], params['end_points']).type_as(input)
transform: torch.Tensor = K.eye_like(4, input)
transform = perspective_transform
return transform
def compute_crop_transformation3d(
input: torch.Tensor, params: Dict[str, torch.Tensor], flags: Dict[str, torch.Tensor]
) -> torch.Tensor:
r"""Compute the cropping transformation matrix :math: `(*, 4, 4)`.
Args:
input (torch.Tensor): Tensor to be transformed with shape :math:`(*, C, D, H, W)`.
params (Dict[str, torch.Tensor]):
- params['src']: The applied cropping src matrix :math: `(*, 8, 3)`.
- params['dst']: The applied cropping dst matrix :math: `(*, 8, 3)`.
Returns:
torch.Tensor: The cropping transformation matrix :math: `(*, 4, 4)`.
"""
transform: torch.Tensor = get_perspective_transform3d(params['src'].to(input.dtype), params['dst'].to(input.dtype))
transform = transform.expand(input.shape[0], -1, -1).to(input)
return transform
def compute_crop_transformation3d(input: torch.Tensor,
params: Dict[str, torch.Tensor],
flags: Dict[str, torch.Tensor]):
r"""Compute the applied transformation matrix :math: `(*, 4, 4)`.
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: `(*, 8, 3)`.
- params['dst']: The applied cropping dst matrix :math: `(*, 8, 3)`.
Returns:
torch.Tensor: The applied transformation matrix :math: `(*, 4, 4)`
"""
input = _transform_input3d(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transform: torch.Tensor = get_perspective_transform3d(
params['src'].to(input.dtype), params['dst'].to(input.dtype))
transform = transform.expand(input.shape[0], -1, -1).type_as(input)
return transform
def compute_perspective_transformation3d(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Compute the perspective transformation matrix :math: `(*, 4, 4)`.
Args:
input (torch.Tensor): Tensor to be transformed with shape :math:`(*, 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.
Returns:
torch.Tensor: The perspective transformation matrix :math: `(*, 4, 4)`
"""
perspective_transform: torch.Tensor = get_perspective_transform3d(
params['start_points'], params['end_points']).to(input)
transform: torch.Tensor = K.eye_like(4, input)
transform = perspective_transform
return transform
def compute_transformation(self, input: Tensor, params: Dict[str, Tensor]) -> Tensor:
return get_perspective_transform3d(params["start_points"], params["end_points"]).to(input)
def compute_transformation(self, input: Tensor, params: Dict[str, Tensor]) -> 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 compute_transformation(self, input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
transform: torch.Tensor = get_perspective_transform3d(params['src'].to(input), params['dst'].to(input))
transform = transform.expand(input.shape[0], -1, -1)
return transform
def compute_transformation(self, input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
return get_perspective_transform3d(params['start_points'], params['end_points']).to(input)
