def __init__(
self, ksize: int, angle: float, direction: float, border_type: str = 'constant'
) -> None:
super(MotionBlur, self).__init__()
self.ksize = ksize
self.angle: float = angle
self.direction: float = direction
assert border_type in ["constant", "reflect", "replicate", "circular"]
self.border_type: str = border_type
self.kernel: torch.Tensor = torch.unsqueeze(
get_motion_kernel2d(self.ksize, self.angle, self.direction),
dim=0
)
def motion_blur(input: torch.Tensor,
kernel_size: int,
angle: Union[float, torch.Tensor],
direction: Union[float, torch.Tensor],
border_type: str = 'constant') -> torch.Tensor:
r"""
Function that blurs a tensor using the motion filter.
See :class:`~kornia.filters.MotionBlur` for details.
"""
assert border_type in ["constant", "reflect", "replicate", "circular"]
kernel: torch.Tensor = torch.unsqueeze(get_motion_kernel2d(
kernel_size, angle, direction),
dim=0)
return filter2D(input, kernel, border_type)
def motion_blur(
input: torch.Tensor,
kernel_size: int,
angle: Union[float, torch.Tensor],
direction: Union[float, torch.Tensor],
border_type: str = 'constant',
mode: str = 'nearest',
) -> torch.Tensor:
r"""Perform motion blur on 2D images (4D tensor).
Args:
input (torch.Tensor): the input tensor with shape :math:`(B, C, H, W)`.
kernel_size (int): motion kernel width and height. It should be odd and positive.
angle (Union[torch.Tensor, float]): angle of the motion blur in degrees (anti-clockwise rotation).
If tensor, it must be :math:`(B,)`.
direction (tensor or 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.
If tensor, it must be :math:`(B,)`.
border_type (str): the padding mode to be applied before convolving. The expected modes are:
``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'constant'``.
mode (str): interpolation mode for rotating the kernel. ``'bilinear'`` or ``'nearest'``.
Default: ``'nearest'``
Return:
torch.Tensor: the blurred image with shape :math:`(B, C, H, W)`.
Example:
>>> input = torch.randn(1, 3, 80, 90).repeat(2, 1, 1, 1)
>>> # perform exact motion blur across the batch
>>> out_1 = motion_blur(input, 5, 90., 1)
>>> torch.allclose(out_1[0], out_1[1])
True
>>> # perform element-wise motion blur across the batch
>>> out_1 = motion_blur(input, 5, torch.tensor([90., 180,]), torch.tensor([1., -1.]))
>>> torch.allclose(out_1[0], out_1[1])
False
"""
assert border_type in ["constant", "reflect", "replicate", "circular"]
kernel: torch.Tensor = get_motion_kernel2d(kernel_size, angle, direction,
mode)
return kornia.filter2d(input, kernel, border_type)