Source code for kornia.filters.sobel

import torch
import torch.nn as nn
import torch.nn.functional as F

from .kernels import get_spatial_gradient_kernel2d, get_spatial_gradient_kernel3d, normalize_kernel2d


[docs]def spatial_gradient(input: torch.Tensor, mode: str = 'sobel', order: int = 1, normalized: bool = True) -> torch.Tensor: r"""Compute the first order image derivative in both x and y using a Sobel operator. .. image:: _static/img/spatial_gradient.png Args: input: input image tensor with shape :math:`(B, C, H, W)`. mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. normalized: whether the output is normalized. Return: the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`. .. note:: See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/ filtering_edges.html>`__. Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = spatial_gradient(input) # 1x3x2x4x4 >>> output.shape torch.Size([1, 3, 2, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 4: raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}") # allocate kernel kernel: torch.Tensor = get_spatial_gradient_kernel2d(mode, order) if normalized: kernel = normalize_kernel2d(kernel) # prepare kernel b, c, h, w = input.shape tmp_kernel: torch.Tensor = kernel.to(input).detach() tmp_kernel = tmp_kernel.unsqueeze(1) # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2] out_channels: int = 3 if order == 2 else 2 padded_inp: torch.Tensor = F.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate') out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1) return out.reshape(b, c, out_channels, h, w)
[docs]def spatial_gradient3d(input: torch.Tensor, mode: str = 'diff', order: int = 1) -> torch.Tensor: r"""Compute the first and second order volume derivative in x, y and d using a diff operator. Args: input: input features tensor with shape :math:`(B, C, D, H, W)`. mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. Return: the spatial gradients of the input feature map with shape math:`(B, C, 3, D, H, W)` or :math:`(B, C, 6, D, H, W)`. Examples: >>> input = torch.rand(1, 4, 2, 4, 4) >>> output = spatial_gradient3d(input) >>> output.shape torch.Size([1, 4, 3, 2, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 5: raise ValueError(f"Invalid input shape, we expect BxCxDxHxW. Got: {input.shape}") b, c, d, h, w = input.shape dev = input.device dtype = input.dtype if (mode == 'diff') and (order == 1): # we go for the special case implementation due to conv3d bad speed x: torch.Tensor = F.pad(input, 6 * [1], 'replicate') center = slice(1, -1) left = slice(0, -2) right = slice(2, None) out = torch.empty(b, c, 3, d, h, w, device=dev, dtype=dtype) out[..., 0, :, :, :] = x[..., center, center, right] - x[..., center, center, left] out[..., 1, :, :, :] = x[..., center, right, center] - x[..., center, left, center] out[..., 2, :, :, :] = x[..., right, center, center] - x[..., left, center, center] out = 0.5 * out else: # prepare kernel # allocate kernel kernel: torch.Tensor = get_spatial_gradient_kernel3d(mode, order) tmp_kernel: torch.Tensor = kernel.to(input).detach() tmp_kernel = tmp_kernel.repeat(c, 1, 1, 1, 1) # convolve input tensor with grad kernel kernel_flip: torch.Tensor = tmp_kernel.flip(-3) # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [ kernel.size(2) // 2, kernel.size(2) // 2, kernel.size(3) // 2, kernel.size(3) // 2, kernel.size(4) // 2, kernel.size(4) // 2, ] out_ch: int = 6 if order == 2 else 3 out = F.conv3d(F.pad(input, spatial_pad, 'replicate'), kernel_flip, padding=0, groups=c).view( b, c, out_ch, d, h, w ) return out
[docs]def sobel(input: torch.Tensor, normalized: bool = True, eps: float = 1e-6) -> torch.Tensor: r"""Compute the Sobel operator and returns the magnitude per channel. .. image:: _static/img/sobel.png Args: input: the input image with shape :math:`(B,C,H,W)`. normalized: if True, L1 norm of the kernel is set to 1. eps: regularization number to avoid NaN during backprop. Return: the sobel edge gradient magnitudes map with shape :math:`(B,C,H,W)`. .. note:: See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/ filtering_edges.html>`__. Example: >>> input = torch.rand(1, 3, 4, 4) >>> output = sobel(input) # 1x3x4x4 >>> output.shape torch.Size([1, 3, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 4: raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}") # comput the x/y gradients edges: torch.Tensor = spatial_gradient(input, normalized=normalized) # unpack the edges gx: torch.Tensor = edges[:, :, 0] gy: torch.Tensor = edges[:, :, 1] # compute gradient maginitude magnitude: torch.Tensor = torch.sqrt(gx * gx + gy * gy + eps) return magnitude
[docs]class SpatialGradient(nn.Module): r"""Compute the first order image derivative in both x and y using a Sobel operator. Args: mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. normalized: whether the output is normalized. Return: the sobel edges of the input feature map. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, 2, H, W)` Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = SpatialGradient()(input) # 1x3x2x4x4 """ def __init__(self, mode: str = 'sobel', order: int = 1, normalized: bool = True) -> None: super().__init__() self.normalized: bool = normalized self.order: int = order self.mode: str = mode def __repr__(self) -> str: return ( self.__class__.__name__ + '(' 'order=' + str(self.order) + ', ' + 'normalized=' + str(self.normalized) + ', ' + 'mode=' + self.mode + ')' ) def forward(self, input: torch.Tensor) -> torch.Tensor: return spatial_gradient(input, self.mode, self.order, self.normalized)
[docs]class SpatialGradient3d(nn.Module): r"""Compute the first and second order volume derivative in x, y and d using a diff operator. Args: mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. Return: the spatial gradients of the input feature map. Shape: - Input: :math:`(B, C, D, H, W)`. D, H, W are spatial dimensions, gradient is calculated w.r.t to them. - Output: :math:`(B, C, 3, D, H, W)` or :math:`(B, C, 6, D, H, W)` Examples: >>> input = torch.rand(1, 4, 2, 4, 4) >>> output = SpatialGradient3d()(input) >>> output.shape torch.Size([1, 4, 3, 2, 4, 4]) """ def __init__(self, mode: str = 'diff', order: int = 1) -> None: super().__init__() self.order: int = order self.mode: str = mode self.kernel = get_spatial_gradient_kernel3d(mode, order) return def __repr__(self) -> str: return self.__class__.__name__ + '(' 'order=' + str(self.order) + ', ' + 'mode=' + self.mode + ')' def forward(self, input: torch.Tensor) -> torch.Tensor: # type: ignore return spatial_gradient3d(input, self.mode, self.order)
[docs]class Sobel(nn.Module): r"""Compute the Sobel operator and returns the magnitude per channel. Args: normalized: if True, L1 norm of the kernel is set to 1. eps: regularization number to avoid NaN during backprop. Return: the sobel edge gradient magnitudes map. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = Sobel()(input) # 1x3x4x4 """ def __init__(self, normalized: bool = True, eps: float = 1e-6) -> None: super().__init__() self.normalized: bool = normalized self.eps: float = eps def __repr__(self) -> str: return self.__class__.__name__ + '(' 'normalized=' + str(self.normalized) + ')' def forward(self, input: torch.Tensor) -> torch.Tensor: return sobel(input, self.normalized, self.eps)