Source code for kornia.feature.responses

from typing import Optional, Union

import torch

from kornia.core import Module, Tensor, tensor
from kornia.filters import gaussian_blur2d, spatial_gradient
from kornia.testing import KORNIA_CHECK_SHAPE


[docs]def harris_response( input: Tensor, k: Union[Tensor, float] = 0.04, grads_mode: str = 'sobel', sigmas: Optional[Tensor] = None ) -> Tensor: r"""Compute the Harris cornerness function. Function does not do any normalization or nms. The response map is computed according the following formulation: .. math:: R = max(0, det(M) - k \cdot trace(M)^2) where: .. math:: M = \sum_{(x,y) \in W} \begin{bmatrix} I^{2}_x & I_x I_y \\ I_x I_y & I^{2}_y \\ \end{bmatrix} and :math:`k` is an empirically determined constant :math:`k ∈ [ 0.04 , 0.06 ]` Args: input: input image with shape :math:`(B, C, H, W)`. k: the Harris detector free parameter. grads_mode: can be ``'sobel'`` for standalone use or ``'diff'`` for use on Gaussian pyramid. sigmas: coefficients to be multiplied by multichannel response. Should be shape of :math:`(B)` It is necessary for performing non-maxima-suppression across different scale pyramid levels. See `vlfeat <https://github.com/vlfeat/vlfeat/blob/master/vl/covdet.c#L874>`_. Return: the response map per channel with shape :math:`(B, C, H, W)`. Example: >>> input = torch.tensor([[[ ... [0., 0., 0., 0., 0., 0., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 0., 0., 0., 0., 0., 0.], ... ]]]) # 1x1x7x7 >>> # compute the response map harris_response(input, 0.04) tensor([[[[0.0012, 0.0039, 0.0020, 0.0000, 0.0020, 0.0039, 0.0012], [0.0039, 0.0065, 0.0040, 0.0000, 0.0040, 0.0065, 0.0039], [0.0020, 0.0040, 0.0029, 0.0000, 0.0029, 0.0040, 0.0020], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0020, 0.0040, 0.0029, 0.0000, 0.0029, 0.0040, 0.0020], [0.0039, 0.0065, 0.0040, 0.0000, 0.0040, 0.0065, 0.0039], [0.0012, 0.0039, 0.0020, 0.0000, 0.0020, 0.0039, 0.0012]]]]) """ # TODO: Recompute doctest KORNIA_CHECK_SHAPE(input, ["B", "C", "H", "W"]) if sigmas is not None: if not isinstance(sigmas, Tensor): raise TypeError(f"sigmas type is not a Tensor. Got {type(sigmas)}") if (not len(sigmas.shape) == 1) or (sigmas.size(0) != input.size(0)): raise ValueError(f"Invalid sigmas shape, we expect B == input.size(0). Got: {sigmas.shape}") gradients: Tensor = spatial_gradient(input, grads_mode) dx: Tensor = gradients[:, :, 0] dy: Tensor = gradients[:, :, 1] # compute the structure tensor M elements dx2: Tensor = gaussian_blur2d(dx**2, (7, 7), (1.0, 1.0)) dy2: Tensor = gaussian_blur2d(dy**2, (7, 7), (1.0, 1.0)) dxy: Tensor = gaussian_blur2d(dx * dy, (7, 7), (1.0, 1.0)) det_m: Tensor = dx2 * dy2 - dxy * dxy trace_m: Tensor = dx2 + dy2 # compute the response map scores: Tensor = det_m - k * (trace_m**2) if sigmas is not None: scores = scores * sigmas.pow(4).view(-1, 1, 1, 1) return scores
[docs]def gftt_response(input: Tensor, grads_mode: str = 'sobel', sigmas: Optional[Tensor] = None) -> Tensor: r"""Compute the Shi-Tomasi cornerness function. Function does not do any normalization or nms. The response map is computed according the following formulation: .. math:: R = min(eig(M)) where: .. math:: M = \sum_{(x,y) \in W} \begin{bmatrix} I^{2}_x & I_x I_y \\ I_x I_y & I^{2}_y \\ \end{bmatrix} Args: input: input image with shape :math:`(B, C, H, W)`. grads_mode: can be ``'sobel'`` for standalone use or ``'diff'`` for use on Gaussian pyramid. sigmas: coefficients to be multiplied by multichannel response. Should be shape of :math:`(B)` It is necessary for performing non-maxima-suppression across different scale pyramid levels. See `vlfeat <https://github.com/vlfeat/vlfeat/blob/master/vl/covdet.c#L874>`_. Return: the response map per channel with shape :math:`(B, C, H, W)`. Example: >>> input = torch.tensor([[[ ... [0., 0., 0., 0., 0., 0., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 0., 0., 0., 0., 0., 0.], ... ]]]) # 1x1x7x7 >>> # compute the response map gftt_response(input) tensor([[[[0.0155, 0.0334, 0.0194, 0.0000, 0.0194, 0.0334, 0.0155], [0.0334, 0.0575, 0.0339, 0.0000, 0.0339, 0.0575, 0.0334], [0.0194, 0.0339, 0.0497, 0.0000, 0.0497, 0.0339, 0.0194], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0194, 0.0339, 0.0497, 0.0000, 0.0497, 0.0339, 0.0194], [0.0334, 0.0575, 0.0339, 0.0000, 0.0339, 0.0575, 0.0334], [0.0155, 0.0334, 0.0194, 0.0000, 0.0194, 0.0334, 0.0155]]]]) """ # TODO: Recompute doctest KORNIA_CHECK_SHAPE(input, ["B", "C", "H", "W"]) gradients: Tensor = spatial_gradient(input, grads_mode) dx: Tensor = gradients[:, :, 0] dy: Tensor = gradients[:, :, 1] dx2: Tensor = gaussian_blur2d(dx**2, (7, 7), (1.0, 1.0)) dy2: Tensor = gaussian_blur2d(dy**2, (7, 7), (1.0, 1.0)) dxy: Tensor = gaussian_blur2d(dx * dy, (7, 7), (1.0, 1.0)) det_m: Tensor = dx2 * dy2 - dxy * dxy trace_m: Tensor = dx2 + dy2 e1: Tensor = 0.5 * (trace_m + torch.sqrt((trace_m**2 - 4 * det_m).abs())) e2: Tensor = 0.5 * (trace_m - torch.sqrt((trace_m**2 - 4 * det_m).abs())) scores: Tensor = torch.min(e1, e2) if sigmas is not None: scores = scores * sigmas.pow(4).view(-1, 1, 1, 1) return scores
[docs]def hessian_response(input: Tensor, grads_mode: str = 'sobel', sigmas: Optional[Tensor] = None) -> Tensor: r"""Compute the absolute of determinant of the Hessian matrix. Function does not do any normalization or nms. The response map is computed according the following formulation: .. math:: R = det(H) where: .. math:: M = \sum_{(x,y) \in W} \begin{bmatrix} I_{xx} & I_{xy} \\ I_{xy} & I_{yy} \\ \end{bmatrix} Args: input: input image with shape :math:`(B, C, H, W)`. grads_mode: can be ``'sobel'`` for standalone use or ``'diff'`` for use on Gaussian pyramid. sigmas: coefficients to be multiplied by multichannel response. Should be shape of :math:`(B)` It is necessary for performing non-maxima-suppression across different scale pyramid levels. See `vlfeat <https://github.com/vlfeat/vlfeat/blob/master/vl/covdet.c#L874>`_. Return: the response map per channel with shape :math:`(B, C, H, W)`. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples: >>> input = torch.tensor([[[ ... [0., 0., 0., 0., 0., 0., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 1., 1., 1., 1., 1., 0.], ... [0., 0., 0., 0., 0., 0., 0.], ... ]]]) # 1x1x7x7 >>> # compute the response map hessian_response(input) tensor([[[[0.0155, 0.0334, 0.0194, 0.0000, 0.0194, 0.0334, 0.0155], [0.0334, 0.0575, 0.0339, 0.0000, 0.0339, 0.0575, 0.0334], [0.0194, 0.0339, 0.0497, 0.0000, 0.0497, 0.0339, 0.0194], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0194, 0.0339, 0.0497, 0.0000, 0.0497, 0.0339, 0.0194], [0.0334, 0.0575, 0.0339, 0.0000, 0.0339, 0.0575, 0.0334], [0.0155, 0.0334, 0.0194, 0.0000, 0.0194, 0.0334, 0.0155]]]]) """ # TODO: Recompute doctest KORNIA_CHECK_SHAPE(input, ["B", "C", "H", "W"]) if sigmas is not None: if not isinstance(sigmas, Tensor): raise TypeError(f"sigmas type is not a Tensor. Got {type(sigmas)}") if (not len(sigmas.shape) == 1) or (sigmas.size(0) != input.size(0)): raise ValueError(f"Invalid sigmas shape, we expect B == input.size(0). Got: {sigmas.shape}") gradients: Tensor = spatial_gradient(input, grads_mode, 2) dxx: Tensor = gradients[:, :, 0] dxy: Tensor = gradients[:, :, 1] dyy: Tensor = gradients[:, :, 2] scores: Tensor = dxx * dyy - dxy**2 if sigmas is not None: scores = scores * sigmas.pow(4).view(-1, 1, 1, 1) return scores
[docs]def dog_response(input: Tensor) -> Tensor: r"""Compute the Difference-of-Gaussian response. Args: input: a given the gaussian 5d tensor :math:`(B, C, D, H, W)`. Return: the response map per channel with shape :math:`(B, C, D-1, H, W)`. """ KORNIA_CHECK_SHAPE(input, ["B", "C", "L", "H", "W"]) return input[:, :, 1:] - input[:, :, :-1]
[docs]class BlobDoG(Module): r"""Module that calculates Difference-of-Gaussians blobs. See :func:`~kornia.feature.dog_response` for details. """ def __init__(self) -> None: super().__init__() return def __repr__(self) -> str: return self.__class__.__name__ def forward(self, input: Tensor, sigmas: Optional[Tensor] = None) -> Tensor: return dog_response(input)
[docs]class CornerHarris(Module): r"""Module that calculates Harris corners. See :func:`~kornia.feature.harris_response` for details. """ k: Tensor def __init__(self, k: Union[float, Tensor], grads_mode='sobel') -> None: super().__init__() if isinstance(k, float): self.register_buffer('k', tensor(k)) else: self.register_buffer('k', k) self.grads_mode: str = grads_mode return def __repr__(self) -> str: return self.__class__.__name__ + '(k=' + str(self.k) + ', ' + 'grads_mode=' + self.grads_mode + ')' def forward(self, input: Tensor, sigmas: Optional[Tensor] = None) -> Tensor: return harris_response(input, self.k, self.grads_mode, sigmas)
[docs]class CornerGFTT(Module): r"""Module that calculates Shi-Tomasi corners. See :func:`~kornia.feature.gfft_response` for details. """ def __init__(self, grads_mode='sobel') -> None: super().__init__() self.grads_mode: str = grads_mode return def __repr__(self) -> str: return self.__class__.__name__ + 'grads_mode=' + self.grads_mode + ')' def forward(self, input: Tensor, sigmas: Optional[Tensor] = None) -> Tensor: return gftt_response(input, self.grads_mode, sigmas)
[docs]class BlobHessian(Module): r"""Module that calculates Hessian blobs. See :func:`~kornia.feature.hessian_response` for details. """ def __init__(self, grads_mode='sobel') -> None: super().__init__() self.grads_mode: str = grads_mode return def __repr__(self) -> str: return self.__class__.__name__ + 'grads_mode=' + self.grads_mode + ')' def forward(self, input: Tensor, sigmas: Optional[Tensor] = None) -> Tensor: return hessian_response(input, self.grads_mode, sigmas)