def match_nn(
desc1: torch.Tensor, desc2: torch.Tensor, dm: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
r"""Function, which finds nearest neighbors in desc2 for each vector in desc1.
If the distance matrix dm is not provided, :py:func:`torch.cdist` is used.
Args:
desc1: Batch of descriptors of a shape :math:`(B1, D)`.
desc2: Batch of descriptors of a shape :math:`(B2, D)`.
dm: Tensor containing the distances from each descriptor in desc1
to each descriptor in desc2, shape of :math:`(B1, B2)`.
Returns:
- Descriptor distance of matching descriptors, shape of :math:`(B1, 1)`.
- Long tensor indexes of matching descriptors in desc1 and desc2, shape of :math:`(B1, 2)`.
"""
KORNIA_CHECK_SHAPE(desc1, ["B", "DIM"])
KORNIA_CHECK_SHAPE(desc2, ["B", "DIM"])
if dm is None:
dm = torch.cdist(desc1, desc2)
else:
if not ((dm.size(0) == desc1.size(0)) and (dm.size(1) == desc2.size(0))):
raise AssertionError
match_dists, idxs_in_2 = torch.min(dm, dim=1)
idxs_in1: torch.Tensor = torch.arange(0, idxs_in_2.size(0), device=idxs_in_2.device)
matches_idxs: torch.Tensor = torch.cat([idxs_in1.view(-1, 1), idxs_in_2.view(-1, 1)], dim=1)
return match_dists.view(-1, 1), matches_idxs.view(-1, 2)
def laf_from_center_scale_ori(
xy: torch.Tensor,
scale: Optional[torch.Tensor] = None,
ori: Optional[torch.Tensor] = None) -> torch.Tensor:
"""Return orientation of the LAFs, in radians. Useful to create kornia LAFs from OpenCV keypoints.
Args:
xy: tensor [BxNx2].
scale: tensor [BxNx1x1]. If not provided, scale = 1 is assumed
ori: tensor [BxNx1]. If not provided orientation = 0 is assumed
Returns:
tensor BxNx2x3.
"""
KORNIA_CHECK_SHAPE(xy, ["B", "N", "2"])
device = xy.device
dtype = xy.dtype
B, N = xy.shape[:2]
if scale is None:
scale = torch.ones(B, N, 1, 1, device=device, dtype=dtype)
if ori is None:
ori = torch.zeros(B, N, 1, device=device, dtype=dtype)
KORNIA_CHECK_SHAPE(scale, ["B", "N", "1", "1"])
KORNIA_CHECK_SHAPE(ori, ["B", "N", "1"])
unscaled_laf: torch.Tensor = torch.cat(
[angle_to_rotation_matrix(ori.squeeze(-1)),
xy.unsqueeze(-1)], dim=-1)
laf: torch.Tensor = scale_laf(unscaled_laf, scale)
return laf
def match_smnn(
desc1: torch.Tensor, desc2: torch.Tensor, th: float = 0.8, dm: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Function, which finds mutual nearest neighbors in desc2 for each vector in desc1.
the method satisfies first to second nearest neighbor distance <= th.
If the distance matrix dm is not provided, :py:func:`torch.cdist` is used.
Args:
desc1: Batch of descriptors of a shape :math:`(B1, D)`.
desc2: Batch of descriptors of a shape :math:`(B2, D)`.
th: distance ratio threshold.
dm: Tensor containing the distances from each descriptor in desc1
to each descriptor in desc2, shape of :math:`(B1, B2)`.
Return:
- Descriptor distance of matching descriptors, shape of. :math:`(B3, 1)`.
- Long tensor indexes of matching descriptors in desc1 and desc2,
shape of :math:`(B3, 2)` where 0 <= B3 <= B1.
"""
KORNIA_CHECK_SHAPE(desc1, ["B", "DIM"])
KORNIA_CHECK_SHAPE(desc2, ["B", "DIM"])
if desc1.shape[0] < 2:
raise AssertionError
if desc2.shape[0] < 2:
raise AssertionError
if dm is None:
dm = torch.cdist(desc1, desc2)
else:
if not ((dm.size(0) == desc1.size(0)) and (dm.size(1) == desc2.size(0))):
raise AssertionError
dists1, idx1 = match_snn(desc1, desc2, th, dm)
dists2, idx2 = match_snn(desc2, desc1, th, dm.t())
if len(dists2) > 0 and len(dists1) > 0:
idx2 = idx2.flip(1)
idxs_dm = torch.cdist(idx1.float(), idx2.float(), p=1.0)
mutual_idxs1 = idxs_dm.min(dim=1)[0] < 1e-8
mutual_idxs2 = idxs_dm.min(dim=0)[0] < 1e-8
good_idxs1 = idx1[mutual_idxs1.view(-1)]
good_idxs2 = idx2[mutual_idxs2.view(-1)]
dists1_good = dists1[mutual_idxs1.view(-1)]
dists2_good = dists2[mutual_idxs2.view(-1)]
_, idx_upl1 = torch.sort(good_idxs1[:, 0])
_, idx_upl2 = torch.sort(good_idxs2[:, 0])
good_idxs1 = good_idxs1[idx_upl1]
match_dists = torch.max(dists1_good[idx_upl1], dists2_good[idx_upl2])
matches_idxs = good_idxs1
else:
matches_idxs, match_dists = torch.empty(0, 2, device=dm.device), torch.empty(0, 1, device=dm.device)
return match_dists.view(-1, 1), matches_idxs.view(-1, 2)
def raise_error_if_laf_is_not_valid(laf: torch.Tensor) -> None:
"""Auxiliary function, which verifies that input.
Args:
laf: [BxNx2x3] shape.
"""
KORNIA_CHECK_SHAPE(laf, ["B", "N", "2", "3"])
def forward(self, patch: torch.Tensor) -> torch.Tensor:
"""Args:
patch: (torch.Tensor) shape [Bx1xHxW]
Returns:
torch.Tensor: ellipse_shape shape [Bx1x3]"""
KORNIA_CHECK_SHAPE(patch, ["B", "1", "H", "W"])
self.weighting = self.weighting.to(patch.dtype).to(patch.device)
grads: torch.Tensor = self.gradient(patch) * self.weighting
# unpack the edges
gx: torch.Tensor = grads[:, :, 0]
gy: torch.Tensor = grads[:, :, 1]
# abc == 1st axis, mixture, 2nd axis. Ellipse_shape is a 2nd moment matrix.
ellipse_shape = torch.cat(
[
gx.pow(2).mean(dim=2).mean(dim=2, keepdim=True),
(gx * gy).mean(dim=2).mean(dim=2, keepdim=True),
gy.pow(2).mean(dim=2).mean(dim=2, keepdim=True),
],
dim=2,
)
# Now lets detect degenerate cases: when 2 or 3 elements are close to zero (e.g. if patch is completely black
bad_mask = ((ellipse_shape < self.eps).float().sum(dim=2, keepdim=True)
>= 2).to(ellipse_shape.dtype)
# We will replace degenerate shape with circular shapes.
circular_shape = torch.tensor([1.0, 0.0, 1.0]).to(
ellipse_shape.device).to(ellipse_shape.dtype).view(1, 1, 3)
ellipse_shape = ellipse_shape * (1.0 -
bad_mask) + circular_shape * bad_mask
# normalization
ellipse_shape = ellipse_shape / ellipse_shape.max(dim=2,
keepdim=True)[0]
return ellipse_shape
def forward(self, input):
KORNIA_CHECK_SHAPE(input, ["B", "1", "H", "W"])
B, CH, W, H = input.size()
self.bin_pooling_kernel = self.bin_pooling_kernel.to(input.dtype).to(input.device)
self.PoolingConv = self.PoolingConv.to(input.dtype).to(input.device)
grads: torch.Tensor = spatial_gradient(input, 'diff')
# unpack the edges
gx: torch.Tensor = grads[:, :, 0]
gy: torch.Tensor = grads[:, :, 1]
mag: torch.Tensor = torch.sqrt(gx * gx + gy * gy + self.eps)
ori: torch.Tensor = torch.atan2(gy, gx + self.eps) + 2.0 * pi
o_big: torch.Tensor = float(self.num_ang_bins) * ori / (2.0 * pi)
bo0_big_: torch.Tensor = torch.floor(o_big)
wo1_big_: torch.Tensor = (o_big - bo0_big_)
bo0_big: torch.Tensor = bo0_big_ % self.num_ang_bins
bo1_big: torch.Tensor = (bo0_big + 1) % self.num_ang_bins
wo0_big: torch.Tensor = (1.0 - wo1_big_) * mag # type: ignore
wo1_big: torch.Tensor = wo1_big_ * mag
ang_bins = []
for i in range(0, self.num_ang_bins):
out = self.bin_pooling_kernel((bo0_big == i).to(input.dtype) * wo0_big + # noqa
(bo1_big == i).to(input.dtype) * wo1_big)
ang_bins.append(out)
ang_bins = torch.cat(ang_bins, dim=1)
out_no_norm = self.PoolingConv(ang_bins)
out = F.normalize(out_no_norm, dim=1, p=2).clamp_(0, float(self.clipval))
out = F.normalize(out, dim=1, p=2)
if self.rootsift:
out = torch.sqrt(F.normalize(out, p=1) + self.eps)
return out
def forward(self, laf: torch.Tensor, img: torch.Tensor) -> torch.Tensor:
"""
Args:
laf: (torch.Tensor) shape [BxNx2x3]
img: (torch.Tensor) shape [Bx1xHxW]
Returns:
torch.Tensor: laf_out shape [BxNx2x3]"""
raise_error_if_laf_is_not_valid(laf)
KORNIA_CHECK_SHAPE(img, ["B", "1", "H", "W"])
B, N = laf.shape[:2]
PS: int = self.patch_size
patches: torch.Tensor = extract_patches_from_pyramid(
img, make_upright(laf), PS, True).view(-1, 1, PS, PS)
ellipse_shape: torch.Tensor = self.affine_shape_detector(patches)
ellipses = torch.cat(
[laf.view(-1, 2, 3)[..., 2].unsqueeze(1), ellipse_shape],
dim=2).view(B, N, 5)
scale_orig = get_laf_scale(laf)
if self.preserve_orientation:
ori_orig = get_laf_orientation(laf)
laf_out = ellipse_to_laf(ellipses)
ellipse_scale = get_laf_scale(laf_out)
laf_out = scale_laf(laf_out, scale_orig / ellipse_scale)
if self.preserve_orientation:
laf_out = set_laf_orientation(laf_out, ori_orig)
return laf_out
def forward(self, laf: torch.Tensor, img: torch.Tensor) -> torch.Tensor:
"""
Args:
laf: shape [BxNx2x3]
img: shape [Bx1xHxW]
Returns:
laf_out shape [BxNx2x3]
"""
raise_error_if_laf_is_not_valid(laf)
KORNIA_CHECK_SHAPE(img, ["B", "1", "H", "W"])
B, N = laf.shape[:2]
PS: int = self.patch_size
patches: torch.Tensor = extract_patches_from_pyramid(
img, make_upright(laf), PS, True).view(-1, 1, PS, PS)
xy = self.features(self._normalize_input(patches)).view(-1, 3)
a1 = torch.cat(
[1.0 + xy[:, 0].reshape(-1, 1, 1), 0 * xy[:, 0].reshape(-1, 1, 1)],
dim=2)
a2 = torch.cat(
[xy[:, 1].reshape(-1, 1, 1), 1.0 + xy[:, 2].reshape(-1, 1, 1)],
dim=2)
new_laf_no_center = torch.cat([a1, a2], dim=1).reshape(B, N, 2, 2)
new_laf = torch.cat([new_laf_no_center, laf[:, :, :, 2:3]], dim=3)
scale_orig = get_laf_scale(laf)
if self.preserve_orientation:
ori_orig = get_laf_orientation(laf)
ellipse_scale = get_laf_scale(new_laf)
laf_out = scale_laf(make_upright(new_laf), scale_orig / ellipse_scale)
if self.preserve_orientation:
laf_out = set_laf_orientation(laf_out, ori_orig)
return laf_out
def forward(self, input):
KORNIA_CHECK_SHAPE(input, ["B", "1", f"{self.patch_size}", f"{self.patch_size}"])
B: int = input.shape[0]
self.pk = self.pk.to(input.dtype).to(input.device)
grads: torch.Tensor = spatial_gradient(input, 'diff')
# unpack the edges
gx: torch.Tensor = grads[:, :, 0]
gy: torch.Tensor = grads[:, :, 1]
mag: torch.Tensor = torch.sqrt(gx * gx + gy * gy + self.eps)
ori: torch.Tensor = torch.atan2(gy, gx + self.eps) + 2.0 * pi
mag = mag * self.gk.expand_as(mag).type_as(mag).to(mag.device)
o_big: torch.Tensor = float(self.num_ang_bins) * ori / (2.0 * pi)
bo0_big_: torch.Tensor = torch.floor(o_big)
wo1_big_: torch.Tensor = o_big - bo0_big_
bo0_big: torch.Tensor = bo0_big_ % self.num_ang_bins
bo1_big: torch.Tensor = (bo0_big + 1) % self.num_ang_bins
wo0_big: torch.Tensor = (1.0 - wo1_big_) * mag # type: ignore
wo1_big: torch.Tensor = wo1_big_ * mag
ang_bins = []
for i in range(0, self.num_ang_bins):
out = self.pk((bo0_big == i).to(input.dtype) * wo0_big + (bo1_big == i).to(input.dtype) * wo1_big)
ang_bins.append(out)
ang_bins = torch.cat(ang_bins, dim=1)
ang_bins = ang_bins.view(B, -1)
ang_bins = F.normalize(ang_bins, p=2)
ang_bins = torch.clamp(ang_bins, 0.0, float(self.clipval))
ang_bins = F.normalize(ang_bins, p=2)
if self.rootsift:
ang_bins = torch.sqrt(F.normalize(ang_bins, p=1) + self.eps)
return ang_bins
def forward(self, input: torch.Tensor) -> torch.Tensor:
KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
x_norm: torch.Tensor = self._normalize_input(input)
x_features: torch.Tensor = self.features(x_norm)
mean: torch.Tensor = torch.jit.annotate(torch.Tensor, self.mean)
components: torch.Tensor = torch.jit.annotate(torch.Tensor,
self.components)
x_prePCA = F.normalize(x_features.view(x_features.size(0), -1))
pca = torch.mm(x_prePCA - mean, components)
return F.normalize(pca, dim=1)
def match_snn(
desc1: torch.Tensor, desc2: torch.Tensor, th: float = 0.8, dm: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Function, which finds nearest neighbors in desc2 for each vector in desc1.
The method satisfies first to second nearest neighbor distance <= th.
If the distance matrix dm is not provided, :py:func:`torch.cdist` is used.
Args:
desc1: Batch of descriptors of a shape :math:`(B1, D)`.
desc2: Batch of descriptors of a shape :math:`(B2, D)`.
th: distance ratio threshold.
dm: Tensor containing the distances from each descriptor in desc1
to each descriptor in desc2, shape of :math:`(B1, B2)`.
Return:
- Descriptor distance of matching descriptors, shape of :math:`(B3, 1)`.
- Long tensor indexes of matching descriptors in desc1 and desc2. Shape: :math:`(B3, 2)`,
where 0 <= B3 <= B1.
"""
KORNIA_CHECK_SHAPE(desc1, ["B", "DIM"])
KORNIA_CHECK_SHAPE(desc2, ["B", "DIM"])
if desc2.shape[0] < 2:
raise AssertionError
if dm is None:
dm = torch.cdist(desc1, desc2)
else:
if not ((dm.size(0) == desc1.size(0)) and (dm.size(1) == desc2.size(0))):
raise AssertionError
vals, idxs_in_2 = torch.topk(dm, 2, dim=1, largest=False)
ratio = vals[:, 0] / vals[:, 1]
mask = ratio <= th
match_dists = ratio[mask]
idxs_in1 = torch.arange(0, idxs_in_2.size(0), device=dm.device)[mask]
idxs_in_2 = idxs_in_2[:, 0][mask]
matches_idxs = torch.cat([idxs_in1.view(-1, 1), idxs_in_2.view(-1, 1)], dim=1)
return match_dists.view(-1, 1), matches_idxs.view(-1, 2)
def match_mnn(
desc1: torch.Tensor, desc2: torch.Tensor, dm: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Function, which finds mutual nearest neighbors in desc2 for each vector in desc1.
If the distance matrix dm is not provided, :py:func:`torch.cdist` is used.
Args:
desc1: Batch of descriptors of a shape :math:`(B1, D)`.
desc2: Batch of descriptors of a shape :math:`(B2, D)`.
dm: Tensor containing the distances from each descriptor in desc1
to each descriptor in desc2, shape of :math:`(B1, B2)`.
Return:
- Descriptor distance of matching descriptors, shape of. :math:`(B3, 1)`.
- Long tensor indexes of matching descriptors in desc1 and desc2, shape of :math:`(B3, 2)`,
where 0 <= B3 <= min(B1, B2)
"""
KORNIA_CHECK_SHAPE(desc1, ["B", "DIM"])
KORNIA_CHECK_SHAPE(desc2, ["B", "DIM"])
if dm is None:
dm = torch.cdist(desc1, desc2)
else:
if not ((dm.size(0) == desc1.size(0)) and (dm.size(1) == desc2.size(0))):
raise AssertionError
ms = min(dm.size(0), dm.size(1))
match_dists, idxs_in_2 = torch.min(dm, dim=1)
match_dists2, idxs_in_1 = torch.min(dm, dim=0)
minsize_idxs = torch.arange(ms, device=dm.device)
if dm.size(0) <= dm.size(1):
mutual_nns = minsize_idxs == idxs_in_1[idxs_in_2][:ms]
matches_idxs = torch.cat([minsize_idxs.view(-1, 1), idxs_in_2.view(-1, 1)], dim=1)[mutual_nns]
match_dists = match_dists[mutual_nns]
else:
mutual_nns = minsize_idxs == idxs_in_2[idxs_in_1][:ms]
matches_idxs = torch.cat([idxs_in_1.view(-1, 1), minsize_idxs.view(-1, 1)], dim=1)[mutual_nns]
match_dists = match_dists2[mutual_nns]
return match_dists.view(-1, 1), matches_idxs.view(-1, 2)
def dog_response(input: torch.Tensor) -> torch.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]
def draw_convex_polygon(images: Tensor, polygons: Union[Tensor, List[Tensor]],
colors: Tensor) -> Tensor:
r"""Draws convex polygons on a batch of image tensors.
Args:
images: is tensor of BxCxHxW.
polygons: represents polygons as points, either BxNx2 or List of variable length polygons.
N is the number of points.
2 is (x, y).
color: a B x 3 tensor or 3 tensor with color to fill in.
Returns:
This operation modifies image inplace but also returns the drawn tensor for
convenience with same shape the of the input BxCxHxW.
Note:
This function assumes a coordinate system (0, h - 1), (0, w - 1) in the image, with (0, 0) being the center
of the top-left pixel and (w - 1, h - 1) being the center of the bottom-right coordinate.
Example:
>>> img = torch.rand(1, 3, 12, 16)
>>> poly = torch.tensor([[[4, 4], [12, 4], [12, 8], [4, 8]]])
>>> color = torch.tensor([[0.5, 0.5, 0.5]])
>>> out = draw_convex_polygon(img, poly, color)
"""
# TODO: implement optional linetypes for smooth edges
KORNIA_CHECK_SHAPE(images, ["B", "C", "H", "W"])
b_i, c_i, h_i, w_i, device = *images.shape, images.device
if isinstance(polygons, List):
polygons = _batch_polygons(polygons)
b_p, _, xy, device_p, dtype_p = *polygons.shape, polygons.device, polygons.dtype
if len(colors.shape) == 1:
colors = colors.expand(b_i, c_i)
b_c, _, device_c = *colors.shape, colors.device
KORNIA_CHECK(xy == 2, "Polygon vertices must be xy, i.e. 2-dimensional")
KORNIA_CHECK(b_i == b_p == b_c,
"Image, polygon, and color must have same batch dimension")
KORNIA_CHECK(device == device_p == device_c,
"Image, polygon, and color must have same device")
x_left, x_right = _get_convex_edges(polygons, h_i, w_i)
ws = torch.arange(w_i, device=device, dtype=dtype_p)[None, None, :]
fill_region = (ws >= x_left[..., :, None]) & (ws <= x_right[..., :, None])
images = (~fill_region[:, None]
) * images + fill_region[:, None] * colors[..., None, None]
return images
def forward(self, laf: torch.Tensor, img: torch.Tensor) -> torch.Tensor:
"""
Args:
laf: shape [BxNx2x3]
img: shape [Bx1xHxW]
Returns:
laf_out, shape [BxNx2x3]
"""
raise_error_if_laf_is_not_valid(laf)
KORNIA_CHECK_SHAPE(img, ["B", "C", "H", "W"])
if laf.size(0) != img.size(0):
raise ValueError(f"Batch size of laf and img should be the same. Got {img.size(0)}, {laf.size(0)}")
B, N = laf.shape[:2]
patches: torch.Tensor = extract_patches_from_pyramid(img, laf, self.patch_size).view(
-1, 1, self.patch_size, self.patch_size
)
angles_radians: torch.Tensor = self.angle_detector(patches).view(B, N)
prev_angle = get_laf_orientation(laf).view_as(angles_radians)
laf_out: torch.Tensor = set_laf_orientation(laf, rad2deg(angles_radians) + prev_angle)
return laf_out
def hessian_response(input: torch.Tensor,
grads_mode: str = 'sobel',
sigmas: Optional[torch.Tensor] = None) -> torch.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, torch.Tensor):
raise TypeError(
f"sigmas type is not a torch.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: torch.Tensor = spatial_gradient(input, grads_mode, 2)
dxx: torch.Tensor = gradients[:, :, 0]
dxy: torch.Tensor = gradients[:, :, 1]
dyy: torch.Tensor = gradients[:, :, 2]
scores: torch.Tensor = dxx * dyy - dxy**2
if sigmas is not None:
scores = scores * sigmas.pow(4).view(-1, 1, 1, 1)
return scores
def forward(self, input: torch.Tensor) -> torch.Tensor:
KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
x = self.features(input)
x = x.view(x.size(0), -1)
x = self.descr(x)
return x
def gftt_response(input: torch.Tensor,
grads_mode: str = 'sobel',
sigmas: Optional[torch.Tensor] = None) -> torch.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: torch.Tensor = spatial_gradient(input, grads_mode)
dx: torch.Tensor = gradients[:, :, 0]
dy: torch.Tensor = gradients[:, :, 1]
dx2: torch.Tensor = gaussian_blur2d(dx**2, (7, 7), (1.0, 1.0))
dy2: torch.Tensor = gaussian_blur2d(dy**2, (7, 7), (1.0, 1.0))
dxy: torch.Tensor = gaussian_blur2d(dx * dy, (7, 7), (1.0, 1.0))
det_m: torch.Tensor = dx2 * dy2 - dxy * dxy
trace_m: torch.Tensor = dx2 + dy2
e1: torch.Tensor = 0.5 * (trace_m + torch.sqrt(
(trace_m**2 - 4 * det_m).abs()))
e2: torch.Tensor = 0.5 * (trace_m - torch.sqrt(
(trace_m**2 - 4 * det_m).abs()))
scores: torch.Tensor = torch.min(e1, e2)
if sigmas is not None:
scores = scores * sigmas.pow(4).view(-1, 1, 1, 1)
return scores
def harris_response(
input: torch.Tensor,
k: Union[torch.Tensor, float] = 0.04,
grads_mode: str = 'sobel',
sigmas: Optional[torch.Tensor] = None,
) -> torch.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, torch.Tensor):
raise TypeError(
f"sigmas type is not a torch.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: torch.Tensor = spatial_gradient(input, grads_mode)
dx: torch.Tensor = gradients[:, :, 0]
dy: torch.Tensor = gradients[:, :, 1]
# compute the structure tensor M elements
dx2: torch.Tensor = gaussian_blur2d(dx**2, (7, 7), (1.0, 1.0))
dy2: torch.Tensor = gaussian_blur2d(dy**2, (7, 7), (1.0, 1.0))
dxy: torch.Tensor = gaussian_blur2d(dx * dy, (7, 7), (1.0, 1.0))
det_m: torch.Tensor = dx2 * dy2 - dxy * dxy
trace_m: torch.Tensor = dx2 + dy2
# compute the response map
scores: torch.Tensor = det_m - k * (trace_m**2)
if sigmas is not None:
scores = scores * sigmas.pow(4).view(-1, 1, 1, 1)
return scores
def forward(self, input: torch.Tensor, eps: float = 1e-10) -> torch.Tensor:
KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
descr = self.desc_norm(self.layers(input) + eps)
descr = descr.view(descr.size(0), -1)
return descr
def forward(self, input: torch.Tensor) -> torch.Tensor:
KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
x_norm: torch.Tensor = self._normalize_input(input)
x_features: torch.Tensor = self.features(x_norm)
x_out = x_features.view(x_features.size(0), -1)
return F.normalize(x_out, dim=1)
