Ma/video-subtitle-remover
1
0
Fork 0
mirror of https://github.com/YaoFANGUK/video-subtitle-remover.git synced 2026-02-27 22:24:42 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
Files
063a896cb94a3d8d933f44d2cce0cf197cfe2ffc
video-subtitle-remover/backend/inpaint/video/model/canny/kernels.py
YaoFANGUK 5e048175d3 vsr v1.0.0
2023-12-08 17:12:26 +08:00

690 lines
25 KiB
Python
Raw Blame History

import math
from math import sqrt
from typing import List, Optional, Tuple
import torch
def normalize_kernel2d(input: torch.Tensor) -> torch.Tensor:
r"""Normalize both derivative and smoothing kernel."""
if len(input.size()) < 2:
raise TypeError(f"input should be at least 2D tensor. Got {input.size()}")
norm: torch.Tensor = input.abs().sum(dim=-1).sum(dim=-1)
return input / (norm.unsqueeze(-1).unsqueeze(-1))
def gaussian(window_size: int, sigma: float) -> torch.Tensor:
device, dtype = None, None
if isinstance(sigma, torch.Tensor):
device, dtype = sigma.device, sigma.dtype
x = torch.arange(window_size, device=device, dtype=dtype) - window_size // 2
if window_size % 2 == 0:
x = x + 0.5
gauss = torch.exp((-x.pow(2.0) / (2 * sigma**2)).float())
return gauss / gauss.sum()
def gaussian_discrete_erf(window_size: int, sigma) -> torch.Tensor:
r"""Discrete Gaussian by interpolating the error function.
Adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py
"""
device = sigma.device if isinstance(sigma, torch.Tensor) else None
sigma = torch.as_tensor(sigma, dtype=torch.float, device=device)
x = torch.arange(window_size).float() - window_size // 2
t = 0.70710678 / torch.abs(sigma)
gauss = 0.5 * ((t * (x + 0.5)).erf() - (t * (x - 0.5)).erf())
gauss = gauss.clamp(min=0)
return gauss / gauss.sum()
def _modified_bessel_0(x: torch.Tensor) -> torch.Tensor:
r"""Adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py
"""
if torch.abs(x) < 3.75:
y = (x / 3.75) * (x / 3.75)
return 1.0 + y * (
3.5156229 + y * (3.0899424 + y * (1.2067492 + y * (0.2659732 + y * (0.360768e-1 + y * 0.45813e-2))))
)
ax = torch.abs(x)
y = 3.75 / ax
ans = 0.916281e-2 + y * (-0.2057706e-1 + y * (0.2635537e-1 + y * (-0.1647633e-1 + y * 0.392377e-2)))
coef = 0.39894228 + y * (0.1328592e-1 + y * (0.225319e-2 + y * (-0.157565e-2 + y * ans)))
return (torch.exp(ax) / torch.sqrt(ax)) * coef
def _modified_bessel_1(x: torch.Tensor) -> torch.Tensor:
r"""adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py
"""
if torch.abs(x) < 3.75:
y = (x / 3.75) * (x / 3.75)
ans = 0.51498869 + y * (0.15084934 + y * (0.2658733e-1 + y * (0.301532e-2 + y * 0.32411e-3)))
return torch.abs(x) * (0.5 + y * (0.87890594 + y * ans))
ax = torch.abs(x)
y = 3.75 / ax
ans = 0.2282967e-1 + y * (-0.2895312e-1 + y * (0.1787654e-1 - y * 0.420059e-2))
ans = 0.39894228 + y * (-0.3988024e-1 + y * (-0.362018e-2 + y * (0.163801e-2 + y * (-0.1031555e-1 + y * ans))))
ans = ans * torch.exp(ax) / torch.sqrt(ax)
return -ans if x < 0.0 else ans
def _modified_bessel_i(n: int, x: torch.Tensor) -> torch.Tensor:
r"""adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py
"""
if n < 2:
raise ValueError("n must be greater than 1.")
if x == 0.0:
return x
device = x.device
tox = 2.0 / torch.abs(x)
ans = torch.tensor(0.0, device=device)
bip = torch.tensor(0.0, device=device)
bi = torch.tensor(1.0, device=device)
m = int(2 * (n + int(sqrt(40.0 * n))))
for j in range(m, 0, -1):
bim = bip + float(j) * tox * bi
bip = bi
bi = bim
if abs(bi) > 1.0e10:
ans = ans * 1.0e-10
bi = bi * 1.0e-10
bip = bip * 1.0e-10
if j == n:
ans = bip
ans = ans * _modified_bessel_0(x) / bi
return -ans if x < 0.0 and (n % 2) == 1 else ans
def gaussian_discrete(window_size, sigma) -> torch.Tensor:
r"""Discrete Gaussian kernel based on the modified Bessel functions.
Adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py
"""
device = sigma.device if isinstance(sigma, torch.Tensor) else None
sigma = torch.as_tensor(sigma, dtype=torch.float, device=device)
sigma2 = sigma * sigma
tail = int(window_size // 2)
out_pos: List[Optional[torch.Tensor]] = [None] * (tail + 1)
out_pos[0] = _modified_bessel_0(sigma2)
out_pos[1] = _modified_bessel_1(sigma2)
for k in range(2, len(out_pos)):
out_pos[k] = _modified_bessel_i(k, sigma2)
out = out_pos[:0:-1]
out.extend(out_pos)
out = torch.stack(out) * torch.exp(sigma2) # type: ignore
return out / out.sum() # type: ignore
def laplacian_1d(window_size) -> torch.Tensor:
r"""One could also use the Laplacian of Gaussian formula to design the filter."""
filter_1d = torch.ones(window_size)
filter_1d[window_size // 2] = 1 - window_size
laplacian_1d: torch.Tensor = filter_1d
return laplacian_1d
def get_box_kernel2d(kernel_size: Tuple[int, int]) -> torch.Tensor:
r"""Utility function that returns a box filter."""
kx: float = float(kernel_size[0])
ky: float = float(kernel_size[1])
scale: torch.Tensor = torch.tensor(1.0) / torch.tensor([kx * ky])
tmp_kernel: torch.Tensor = torch.ones(1, kernel_size[0], kernel_size[1])
return scale.to(tmp_kernel.dtype) * tmp_kernel
def get_binary_kernel2d(window_size: Tuple[int, int]) -> torch.Tensor:
r"""Create a binary kernel to extract the patches.
If the window size is HxW will create a (H*W)xHxW kernel.
"""
window_range: int = window_size[0] * window_size[1]
kernel: torch.Tensor = torch.zeros(window_range, window_range)
for i in range(window_range):
kernel[i, i] += 1.0
return kernel.view(window_range, 1, window_size[0], window_size[1])
def get_sobel_kernel_3x3() -> torch.Tensor:
"""Utility function that returns a sobel kernel of 3x3."""
return torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]])
def get_sobel_kernel_5x5_2nd_order() -> torch.Tensor:
"""Utility function that returns a 2nd order sobel kernel of 5x5."""
return torch.tensor(
[
[-1.0, 0.0, 2.0, 0.0, -1.0],
[-4.0, 0.0, 8.0, 0.0, -4.0],
[-6.0, 0.0, 12.0, 0.0, -6.0],
[-4.0, 0.0, 8.0, 0.0, -4.0],
[-1.0, 0.0, 2.0, 0.0, -1.0],
]
)
def _get_sobel_kernel_5x5_2nd_order_xy() -> torch.Tensor:
"""Utility function that returns a 2nd order sobel kernel of 5x5."""
return torch.tensor(
[
[-1.0, -2.0, 0.0, 2.0, 1.0],
[-2.0, -4.0, 0.0, 4.0, 2.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[2.0, 4.0, 0.0, -4.0, -2.0],
[1.0, 2.0, 0.0, -2.0, -1.0],
]
)
def get_diff_kernel_3x3() -> torch.Tensor:
"""Utility function that returns a first order derivative kernel of 3x3."""
return torch.tensor([[-0.0, 0.0, 0.0], [-1.0, 0.0, 1.0], [-0.0, 0.0, 0.0]])
def get_diff_kernel3d(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
"""Utility function that returns a first order derivative kernel of 3x3x3."""
kernel: torch.Tensor = torch.tensor(
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [-0.5, 0.0, 0.5], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, -0.5, 0.0], [0.0, 0.0, 0.0], [0.0, 0.5, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, -0.5, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 0.0]],
],
],
device=device,
dtype=dtype,
)
return kernel.unsqueeze(1)
def get_diff_kernel3d_2nd_order(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
"""Utility function that returns a first order derivative kernel of 3x3x3."""
kernel: torch.Tensor = torch.tensor(
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [1.0, -2.0, 1.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 1.0, 0.0], [0.0, -2.0, 0.0], [0.0, 1.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, -2.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[1.0, 0.0, -1.0], [0.0, 0.0, 0.0], [-1.0, 0.0, 1.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, -1.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, -1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [1.0, 0.0, -1.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [-1.0, 0.0, 1.0], [0.0, 0.0, 0.0]],
],
],
device=device,
dtype=dtype,
)
return kernel.unsqueeze(1)
def get_sobel_kernel2d() -> torch.Tensor:
kernel_x: torch.Tensor = get_sobel_kernel_3x3()
kernel_y: torch.Tensor = kernel_x.transpose(0, 1)
return torch.stack([kernel_x, kernel_y])
def get_diff_kernel2d() -> torch.Tensor:
kernel_x: torch.Tensor = get_diff_kernel_3x3()
kernel_y: torch.Tensor = kernel_x.transpose(0, 1)
return torch.stack([kernel_x, kernel_y])
def get_sobel_kernel2d_2nd_order() -> torch.Tensor:
gxx: torch.Tensor = get_sobel_kernel_5x5_2nd_order()
gyy: torch.Tensor = gxx.transpose(0, 1)
gxy: torch.Tensor = _get_sobel_kernel_5x5_2nd_order_xy()
return torch.stack([gxx, gxy, gyy])
def get_diff_kernel2d_2nd_order() -> torch.Tensor:
gxx: torch.Tensor = torch.tensor([[0.0, 0.0, 0.0], [1.0, -2.0, 1.0], [0.0, 0.0, 0.0]])
gyy: torch.Tensor = gxx.transpose(0, 1)
gxy: torch.Tensor = torch.tensor([[-1.0, 0.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, -1.0]])
return torch.stack([gxx, gxy, gyy])
def get_spatial_gradient_kernel2d(mode: str, order: int) -> torch.Tensor:
r"""Function that returns kernel for 1st or 2nd order image gradients, using one of the following operators:
sobel, diff.
"""
if mode not in ['sobel', 'diff']:
raise TypeError(
"mode should be either sobel\
or diff. Got {}".format(
mode
)
)
if order not in [1, 2]:
raise TypeError(
"order should be either 1 or 2\
Got {}".format(
order
)
)
if mode == 'sobel' and order == 1:
kernel: torch.Tensor = get_sobel_kernel2d()
elif mode == 'sobel' and order == 2:
kernel = get_sobel_kernel2d_2nd_order()
elif mode == 'diff' and order == 1:
kernel = get_diff_kernel2d()
elif mode == 'diff' and order == 2:
kernel = get_diff_kernel2d_2nd_order()
else:
raise NotImplementedError("")
return kernel
def get_spatial_gradient_kernel3d(mode: str, order: int, device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
r"""Function that returns kernel for 1st or 2nd order scale pyramid gradients, using one of the following
operators: sobel, diff."""
if mode not in ['sobel', 'diff']:
raise TypeError(
"mode should be either sobel\
or diff. Got {}".format(
mode
)
)
if order not in [1, 2]:
raise TypeError(
"order should be either 1 or 2\
Got {}".format(
order
)
)
if mode == 'sobel':
raise NotImplementedError("Sobel kernel for 3d gradient is not implemented yet")
if mode == 'diff' and order == 1:
kernel = get_diff_kernel3d(device, dtype)
elif mode == 'diff' and order == 2:
kernel = get_diff_kernel3d_2nd_order(device, dtype)
else:
raise NotImplementedError("")
return kernel
def get_gaussian_kernel1d(kernel_size: int, sigma: float, force_even: bool = False) -> torch.Tensor:
r"""Function that returns Gaussian filter coefficients.
Args:
kernel_size: filter size. It should be odd and positive.
sigma: gaussian standard deviation.
force_even: overrides requirement for odd kernel size.
Returns:
1D tensor with gaussian filter coefficients.
Shape:
- Output: :math:`(\text{kernel_size})`
Examples:
>>> get_gaussian_kernel1d(3, 2.5)
tensor([0.3243, 0.3513, 0.3243])
>>> get_gaussian_kernel1d(5, 1.5)
tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201])
"""
if not isinstance(kernel_size, int) or ((kernel_size % 2 == 0) and not force_even) or (kernel_size <= 0):
raise TypeError("kernel_size must be an odd positive integer. " "Got {}".format(kernel_size))
window_1d: torch.Tensor = gaussian(kernel_size, sigma)
return window_1d
def get_gaussian_discrete_kernel1d(kernel_size: int, sigma: float, force_even: bool = False) -> torch.Tensor:
r"""Function that returns Gaussian filter coefficients based on the modified Bessel functions. Adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py.
Args:
kernel_size: filter size. It should be odd and positive.
sigma: gaussian standard deviation.
force_even: overrides requirement for odd kernel size.
Returns:
1D tensor with gaussian filter coefficients.
Shape:
- Output: :math:`(\text{kernel_size})`
Examples:
>>> get_gaussian_discrete_kernel1d(3, 2.5)
tensor([0.3235, 0.3531, 0.3235])
>>> get_gaussian_discrete_kernel1d(5, 1.5)
tensor([0.1096, 0.2323, 0.3161, 0.2323, 0.1096])
"""
if not isinstance(kernel_size, int) or ((kernel_size % 2 == 0) and not force_even) or (kernel_size <= 0):
raise TypeError("kernel_size must be an odd positive integer. " "Got {}".format(kernel_size))
window_1d = gaussian_discrete(kernel_size, sigma)
return window_1d
def get_gaussian_erf_kernel1d(kernel_size: int, sigma: float, force_even: bool = False) -> torch.Tensor:
r"""Function that returns Gaussian filter coefficients by interpolating the error function, adapted from:
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py.
Args:
kernel_size: filter size. It should be odd and positive.
sigma: gaussian standard deviation.
force_even: overrides requirement for odd kernel size.
Returns:
1D tensor with gaussian filter coefficients.
Shape:
- Output: :math:`(\text{kernel_size})`
Examples:
>>> get_gaussian_erf_kernel1d(3, 2.5)
tensor([0.3245, 0.3511, 0.3245])
>>> get_gaussian_erf_kernel1d(5, 1.5)
tensor([0.1226, 0.2331, 0.2887, 0.2331, 0.1226])
"""
if not isinstance(kernel_size, int) or ((kernel_size % 2 == 0) and not force_even) or (kernel_size <= 0):
raise TypeError("kernel_size must be an odd positive integer. " "Got {}".format(kernel_size))
window_1d = gaussian_discrete_erf(kernel_size, sigma)
return window_1d
def get_gaussian_kernel2d(
kernel_size: Tuple[int, int], sigma: Tuple[float, float], force_even: bool = False
) -> torch.Tensor:
r"""Function that returns Gaussian filter matrix coefficients.
Args:
kernel_size: filter sizes in the x and y direction.
Sizes should be odd and positive.
sigma: gaussian standard deviation in the x and y
direction.
force_even: overrides requirement for odd kernel size.
Returns:
2D tensor with gaussian filter matrix coefficients.
Shape:
- Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)`
Examples:
>>> get_gaussian_kernel2d((3, 3), (1.5, 1.5))
tensor([[0.0947, 0.1183, 0.0947],
[0.1183, 0.1478, 0.1183],
[0.0947, 0.1183, 0.0947]])
>>> get_gaussian_kernel2d((3, 5), (1.5, 1.5))
tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370],
[0.0462, 0.0899, 0.1123, 0.0899, 0.0462],
[0.0370, 0.0720, 0.0899, 0.0720, 0.0370]])
"""
if not isinstance(kernel_size, tuple) or len(kernel_size) != 2:
raise TypeError(f"kernel_size must be a tuple of length two. Got {kernel_size}")
if not isinstance(sigma, tuple) or len(sigma) != 2:
raise TypeError(f"sigma must be a tuple of length two. Got {sigma}")
ksize_x, ksize_y = kernel_size
sigma_x, sigma_y = sigma
kernel_x: torch.Tensor = get_gaussian_kernel1d(ksize_x, sigma_x, force_even)
kernel_y: torch.Tensor = get_gaussian_kernel1d(ksize_y, sigma_y, force_even)
kernel_2d: torch.Tensor = torch.matmul(kernel_x.unsqueeze(-1), kernel_y.unsqueeze(-1).t())
return kernel_2d
def get_laplacian_kernel1d(kernel_size: int) -> torch.Tensor:
r"""Function that returns the coefficients of a 1D Laplacian filter.
Args:
kernel_size: filter size. It should be odd and positive.
Returns:
1D tensor with laplacian filter coefficients.
Shape:
- Output: math:`(\text{kernel_size})`
Examples:
>>> get_laplacian_kernel1d(3)
tensor([ 1., -2., 1.])
>>> get_laplacian_kernel1d(5)
tensor([ 1., 1., -4., 1., 1.])
"""
if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size <= 0:
raise TypeError(f"ksize must be an odd positive integer. Got {kernel_size}")
window_1d: torch.Tensor = laplacian_1d(kernel_size)
return window_1d
def get_laplacian_kernel2d(kernel_size: int) -> torch.Tensor:
r"""Function that returns Gaussian filter matrix coefficients.
Args:
kernel_size: filter size should be odd.
Returns:
2D tensor with laplacian filter matrix coefficients.
Shape:
- Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)`
Examples:
>>> get_laplacian_kernel2d(3)
tensor([[ 1., 1., 1.],
[ 1., -8., 1.],
[ 1., 1., 1.]])
>>> get_laplacian_kernel2d(5)
tensor([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., -24., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
"""
if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size <= 0:
raise TypeError(f"ksize must be an odd positive integer. Got {kernel_size}")
kernel = torch.ones((kernel_size, kernel_size))
mid = kernel_size // 2
kernel[mid, mid] = 1 - kernel_size**2
kernel_2d: torch.Tensor = kernel
return kernel_2d
def get_pascal_kernel_2d(kernel_size: int, norm: bool = True) -> torch.Tensor:
"""Generate pascal filter kernel by kernel size.
Args:
kernel_size: height and width of the kernel.
norm: if to normalize the kernel or not. Default: True.
Returns:
kernel shaped as :math:`(kernel_size, kernel_size)`
Examples:
>>> get_pascal_kernel_2d(1)
tensor([[1.]])
>>> get_pascal_kernel_2d(4)
tensor([[0.0156, 0.0469, 0.0469, 0.0156],
[0.0469, 0.1406, 0.1406, 0.0469],
[0.0469, 0.1406, 0.1406, 0.0469],
[0.0156, 0.0469, 0.0469, 0.0156]])
>>> get_pascal_kernel_2d(4, norm=False)
tensor([[1., 3., 3., 1.],
[3., 9., 9., 3.],
[3., 9., 9., 3.],
[1., 3., 3., 1.]])
"""
a = get_pascal_kernel_1d(kernel_size)
filt = a[:, None] * a[None, :]
if norm:
filt = filt / torch.sum(filt)
return filt
def get_pascal_kernel_1d(kernel_size: int, norm: bool = False) -> torch.Tensor:
"""Generate Yang Hui triangle (Pascal's triangle) by a given number.
Args:
kernel_size: height and width of the kernel.
norm: if to normalize the kernel or not. Default: False.
Returns:
kernel shaped as :math:`(kernel_size,)`
Examples:
>>> get_pascal_kernel_1d(1)
tensor([1.])
>>> get_pascal_kernel_1d(2)
tensor([1., 1.])
>>> get_pascal_kernel_1d(3)
tensor([1., 2., 1.])
>>> get_pascal_kernel_1d(4)
tensor([1., 3., 3., 1.])
>>> get_pascal_kernel_1d(5)
tensor([1., 4., 6., 4., 1.])
>>> get_pascal_kernel_1d(6)
tensor([ 1., 5., 10., 10., 5., 1.])
"""
pre: List[float] = []
cur: List[float] = []
for i in range(kernel_size):
cur = [1.0] * (i + 1)
for j in range(1, i // 2 + 1):
value = pre[j - 1] + pre[j]
cur[j] = value
if i != 2 * j:
cur[-j - 1] = value
pre = cur
out = torch.as_tensor(cur)
if norm:
out = out / torch.sum(out)
return out
def get_canny_nms_kernel(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
"""Utility function that returns 3x3 kernels for the Canny Non-maximal suppression."""
kernel: torch.Tensor = torch.tensor(
[
[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
],
device=device,
dtype=dtype,
)
return kernel.unsqueeze(1)
def get_hysteresis_kernel(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
"""Utility function that returns the 3x3 kernels for the Canny hysteresis."""
kernel: torch.Tensor = torch.tensor(
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
device=device,
dtype=dtype,
)
return kernel.unsqueeze(1)
def get_hanning_kernel1d(kernel_size: int, device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
r"""Returns Hanning (also known as Hann) kernel, used in signal processing and KCF tracker.
.. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
See further in numpy docs https://numpy.org/doc/stable/reference/generated/numpy.hanning.html
Args:
kernel_size: The size the of the kernel. It should be positive.
Returns:
1D tensor with Hanning filter coefficients.
.. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
Shape:
- Output: math:`(\text{kernel_size})`
Examples:
>>> get_hanning_kernel1d(4)
tensor([0.0000, 0.7500, 0.7500, 0.0000])
"""
if not isinstance(kernel_size, int) or kernel_size <= 2:
raise TypeError(f"ksize must be an positive integer > 2. Got {kernel_size}")
x: torch.Tensor = torch.arange(kernel_size, device=device, dtype=dtype)
x = 0.5 - 0.5 * torch.cos(2.0 * math.pi * x / float(kernel_size - 1))
return x
def get_hanning_kernel2d(kernel_size: Tuple[int, int], device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor:
r"""Returns 2d Hanning kernel, used in signal processing and KCF tracker.
Args:
kernel_size: The size of the kernel for the filter. It should be positive.
Returns:
2D tensor with Hanning filter coefficients.
.. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
Shape:
- Output: math:`(\text{kernel_size[0], kernel_size[1]})`
"""
if kernel_size[0] <= 2 or kernel_size[1] <= 2:
raise TypeError(f"ksize must be an tuple of positive integers > 2. Got {kernel_size}")
ky: torch.Tensor = get_hanning_kernel1d(kernel_size[0], device, dtype)[None].T
kx: torch.Tensor = get_hanning_kernel1d(kernel_size[1], device, dtype)[None]
kernel2d = ky @ kx
return kernel2d
Reference in New Issue View Git Blame Copy Permalink