def get_motion_kernel2d(
kernel_size: int,
angle: Union[torch.Tensor, float],
direction: Union[torch.Tensor, float] = 0.) -> torch.Tensor:
r"""Function that returns motion blur filter.
Args:
kernel_size (int): motion kernel width and height. It should be odd and positive.
angle (torch.Tensor, float): angle of the motion blur in degrees (anti-clockwise rotation).
direction (float): forward/backward direction of the motion blur.
Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle),
while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a
uniformly (but still angled) motion blur.
Returns:
torch.Tensor: the motion blur kernel.
Shape:
- Output: :math:`(ksize, ksize)`
Examples::
>>> kornia.filters.get_motion_kernel2d(5, 0., 0.)
tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.2000, 0.2000, 0.2000, 0.2000, 0.2000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])
>>> kornia.filters.get_motion_kernel2d(3, 215., -0.5)
tensor([[0.0000, 0.0412, 0.0732],
[0.1920, 0.3194, 0.0804],
[0.2195, 0.0743, 0.0000]])
"""
if not isinstance(kernel_size,
int) or kernel_size % 2 == 0 or kernel_size < 3:
raise TypeError("ksize must be an odd integer >= than 3")
if not isinstance(angle, torch.Tensor):
angle = torch.tensor(angle)
assert angle.dim() == 0, f"angle must be a 0-dim tensor. Got {angle}."
if not isinstance(direction, torch.Tensor):
direction = torch.tensor(direction)
assert direction.dim(
) == 0, f"direction must be a 0-dim tensor. Got {direction}."
kernel_tuple: Tuple[int, int] = (kernel_size, kernel_size)
# direction from [-1, 1] to [0, 1] range
direction = (torch.clamp(direction, -1., 1.).item() + 1.) / 2.
kernel = torch.zeros(kernel_tuple, dtype=torch.float)
kernel[kernel_tuple[0] // 2, :] = torch.linspace(direction,
1. - direction,
steps=kernel_tuple[0])
kernel = kernel.unsqueeze(0).unsqueeze(0)
# rotate (counterclockwise) kernel by given angle
kernel = rotate(kernel, angle)
kernel = kernel[0][0]
kernel = kernel / kernel.sum()
return kernel
def get_motion_kernel2d(
kernel_size: int,
angle: Union[torch.Tensor, float],
direction: Union[torch.Tensor, float] = 0.) -> torch.Tensor:
r"""Return 2D motion blur filter.
Args:
kernel_size (int): motion kernel width and height. It should be odd and positive.
angle (torch.Tensor, float): angle of the motion blur in degrees (anti-clockwise rotation).
direction (float): forward/backward direction of the motion blur.
Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle),
while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a
uniformly (but still angled) motion blur.
Returns:
torch.Tensor: the motion blur kernel.
Shape:
- Output: :math:`(ksize, ksize)`
Examples::
>>> kornia.filters.get_motion_kernel2d(5, 0., 0.)
tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.2000, 0.2000, 0.2000, 0.2000, 0.2000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])
>>> kornia.filters.get_motion_kernel2d(3, 215., -0.5)
tensor([[0.0000, 0.0412, 0.0732],
[0.1920, 0.3194, 0.0804],
[0.2195, 0.0743, 0.0000]])
"""
if not isinstance(kernel_size,
int) or kernel_size % 2 == 0 or kernel_size < 3:
raise TypeError("ksize must be an odd integer >= than 3")
if not isinstance(angle, torch.Tensor):
angle = torch.tensor([angle])
angle = cast(torch.Tensor, angle)
if angle.dim() == 0:
angle = angle.unsqueeze(0)
assert angle.dim() == 1, f"angle must be a 1-dim tensor. Got {angle}."
if not isinstance(direction, torch.Tensor):
direction = torch.tensor([direction])
direction = cast(torch.Tensor, direction)
if direction.dim() == 0:
direction = direction.unsqueeze(0)
assert direction.dim(
) == 1, f"direction must be a 1-dim tensor. Got {direction}."
assert direction.size(0) == angle.size(0), \
f"direction and angle must have the same length. Got {direction} and {angle}."
kernel_tuple: Tuple[int, int] = (kernel_size, kernel_size)
# direction from [-1, 1] to [0, 1] range
direction = (torch.clamp(direction, -1., 1.) + 1.) / 2.
kernel = torch.zeros((direction.size(0), *kernel_tuple), dtype=torch.float)
# Element-wise linspace
kernel[:, kernel_tuple[0] // 2, :] = torch.stack(
[(direction - (1 / (kernel_tuple[0] - 1)) * i)
for i in range(kernel_tuple[0])],
dim=-1)
kernel = kernel.unsqueeze(1)
# rotate (counterclockwise) kernel by given angle
kernel = rotate(kernel, angle, mode='nearest', align_corners=True)
kernel = kernel[:, 0]
kernel = kernel / kernel.sum(dim=(1, 2), keepdim=True)
return kernel
def get_motion_kernel2d(
kernel_size: int,
angle: Union[torch.Tensor, float],
direction: Union[torch.Tensor, float] = 0.0,
mode: str = 'nearest',
) -> torch.Tensor:
r"""Return 2D motion blur filter.
Args:
kernel_size (int): motion kernel width and height. It should be odd and positive.
angle (torch.Tensor, float): angle of the motion blur in degrees (anti-clockwise rotation).
direction (float): forward/backward direction of the motion blur.
Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle),
while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a
uniformly (but still angled) motion blur.
mode (str): interpolation mode for rotating the kernel. ``'bilinear'`` or ``'nearest'``.
Default: ``'nearest'``
Returns:
torch.Tensor: the motion blur kernel.
Shape:
- Output: :math:`(B, ksize, ksize)`
Examples::
>>> get_motion_kernel2d(5, 0., 0.)
tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.2000, 0.2000, 0.2000, 0.2000, 0.2000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]])
>>> get_motion_kernel2d(3, 215., -0.5)
tensor([[[0.0000, 0.0000, 0.1667],
[0.0000, 0.3333, 0.0000],
[0.5000, 0.0000, 0.0000]]])
"""
device, dtype = _extract_device_dtype(
[angle if isinstance(angle, torch.Tensor) else None, direction if isinstance(direction, torch.Tensor) else None]
)
if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size < 3:
raise TypeError("ksize must be an odd integer >= than 3")
if not isinstance(angle, torch.Tensor):
angle = torch.tensor([angle], device=device, dtype=dtype)
angle = cast(torch.Tensor, angle)
if angle.dim() == 0:
angle = angle.unsqueeze(0)
if angle.dim() != 1:
raise AssertionError(f"angle must be a 1-dim tensor. Got {angle}.")
if not isinstance(direction, torch.Tensor):
direction = torch.tensor([direction], device=device, dtype=dtype)
direction = cast(torch.Tensor, direction)
if direction.dim() == 0:
direction = direction.unsqueeze(0)
if direction.dim() != 1:
raise AssertionError(f"direction must be a 1-dim tensor. Got {direction}.")
if direction.size(0) != angle.size(0):
raise AssertionError(f"direction and angle must have the same length. Got {direction} and {angle}.")
kernel_tuple: Tuple[int, int] = (kernel_size, kernel_size)
# direction from [-1, 1] to [0, 1] range
direction = (torch.clamp(direction, -1.0, 1.0) + 1.0) / 2.0
# kernel = torch.zeros((direction.size(0), *kernel_tuple), device=device, dtype=dtype)
# Element-wise linspace
# kernel[:, kernel_size // 2, :] = torch.stack(
# [(direction + ((1 - 2 * direction) / (kernel_size - 1)) * i) for i in range(kernel_size)], dim=-1)
# Alternatively
# m = ((1 - 2 * direction)[:, None].repeat(1, kernel_size) / (kernel_size - 1))
# kernel[:, kernel_size // 2, :] = direction[:, None].repeat(1, kernel_size) + m * torch.arange(0, kernel_size)
k = torch.stack([(direction + ((1 - 2 * direction) / (kernel_size - 1)) * i) for i in range(kernel_size)], dim=-1)
kernel = torch.nn.functional.pad(k[:, None], [0, 0, kernel_size // 2, kernel_size // 2, 0, 0])
if kernel.shape != torch.Size([direction.size(0), *kernel_tuple]):
raise AssertionError
kernel = kernel.unsqueeze(1)
# rotate (counterclockwise) kernel by given angle
kernel = rotate(kernel, angle, mode=mode, align_corners=True)
kernel = kernel[:, 0]
kernel = kernel / kernel.sum(dim=(1, 2), keepdim=True)
return kernel
