Source code for kornia.filters.kernels_geometry

from typing import Tuple, Union, cast

import torch

from kornia.geometry.transform import rotate, rotate3d
from kornia.utils import _extract_device_dtype


[docs]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: motion kernel width and height. It should be odd and positive. angle: angle of the motion blur in degrees (anti-clockwise rotation). direction: 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: interpolation mode for rotating the kernel. ``'bilinear'`` or ``'nearest'``. Returns: The motion blur kernel of shape :math:`(B, k_\text{size}, k_\text{size})`. 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
def get_motion_kernel3d( kernel_size: int, angle: Union[torch.Tensor, Tuple[float, float, float]], direction: Union[torch.Tensor, float] = 0.0, mode: str = 'nearest', ) -> torch.Tensor: r"""Return 3D motion blur filter. Args: kernel_size: motion kernel width, height and depth. It should be odd and positive. angle: Range of yaw (x-axis), pitch (y-axis), roll (z-axis) to select from. If tensor, it must be :math:`(B, 3)`. If tuple, it must be (yaw, pitch, raw). direction: 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: interpolation mode for rotating the kernel. ``'bilinear'`` or ``'nearest'``. Returns: The motion blur kernel with shape :math:`(B, k_\text{size}, k_\text{size}, k_\text{size})`. Examples: >>> get_motion_kernel3d(3, (0., 0., 0.), 0.) tensor([[[[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]], <BLANKLINE> [[0.0000, 0.0000, 0.0000], [0.3333, 0.3333, 0.3333], [0.0000, 0.0000, 0.0000]], <BLANKLINE> [[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]]]]) >>> get_motion_kernel3d(3, (90., 90., 0.), -0.5) tensor([[[[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000], [0.0000, 0.5000, 0.0000]], <BLANKLINE> [[0.0000, 0.0000, 0.0000], [0.0000, 0.3333, 0.0000], [0.0000, 0.0000, 0.0000]], <BLANKLINE> [[0.0000, 0.1667, 0.0000], [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]]]]) """ if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size < 3: raise TypeError(f"ksize must be an odd integer >= than 3. Got {kernel_size}.") device, dtype = _extract_device_dtype( [angle if isinstance(angle, torch.Tensor) else None, direction if isinstance(direction, torch.Tensor) else None] ) if not isinstance(angle, torch.Tensor): angle = torch.tensor([angle], device=device, dtype=dtype) angle = cast(torch.Tensor, angle) if angle.dim() == 1: angle = angle.unsqueeze(0) if not (len(angle.shape) == 2 and angle.size(1) == 3): raise AssertionError(f"angle must be (B, 3). 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, int] = (kernel_size, 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, kernel_size // 2, :] = torch.stack( # [(direction + ((1 - 2 * direction) / (kernel_size - 1)) * i) for i in range(kernel_size)], dim=-1) 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, None], [0, 0, kernel_size // 2, kernel_size // 2, 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 = rotate3d(kernel, angle[:, 0], angle[:, 1], angle[:, 2], mode=mode, align_corners=True) kernel = kernel[:, 0] kernel = kernel / kernel.sum(dim=(1, 2, 3), keepdim=True) return kernel