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guided_filter.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from boxfilter import boxfilter2d
class GuidedFilter2d(nn.Module):
def __init__(self, radius: int, eps: float):
super().__init__()
self.r = radius
self.eps = eps
def forward(self, x, guide):
if guide.shape[1] == 3:
return guidedfilter2d_color(guide, x, self.r, self.eps)
elif guide.shape[1] == 1:
return guidedfilter2d_gray(guide, x, self.r, self.eps)
else:
raise NotImplementedError
class FastGuidedFilter2d(GuidedFilter2d):
"""Fast guided filter"""
def __init__(self, radius: int, eps: float, s: int):
super().__init__(radius, eps)
self.s = s
def forward(self, x, guide):
if guide.shape[1] == 3:
return guidedfilter2d_color(guide, x, self.r, self.eps, self.s)
elif guide.shape[1] == 1:
return guidedfilter2d_gray(guide, x, self.r, self.eps, self.s)
else:
raise NotImplementedError
def guidedfilter2d_color(guide, src, radius, eps, scale=None):
"""guided filter for a color guide image
Parameters
-----
guide: (B, 3, H, W)-dim torch.Tensor
guide image
src: (B, C, H, W)-dim torch.Tensor
filtering image
radius: int
filter radius
eps: float
regularization coefficient
"""
assert guide.shape[1] == 3
if src.ndim == 3:
src = src[:, None]
if scale is not None:
guide_sub = guide.clone()
src = F.interpolate(src, scale_factor=1./scale, mode="nearest")
guide = F.interpolate(guide, scale_factor=1./scale, mode="nearest")
radius = radius // scale
guide_r, guide_g, guide_b = torch.chunk(guide, 3, 1) # b x 1 x H x W
ones = torch.ones_like(guide_r)
N = boxfilter2d(ones, radius)
mean_I = boxfilter2d(guide, radius) / N # b x 3 x H x W
mean_I_r, mean_I_g, mean_I_b = torch.chunk(mean_I, 3, 1) # b x 1 x H x W
mean_p = boxfilter2d(src, radius) / N # b x C x H x W
mean_Ip_r = boxfilter2d(guide_r * src, radius) / N # b x C x H x W
mean_Ip_g = boxfilter2d(guide_g * src, radius) / N # b x C x H x W
mean_Ip_b = boxfilter2d(guide_b * src, radius) / N # b x C x H x W
cov_Ip_r = mean_Ip_r - mean_I_r * mean_p # b x C x H x W
cov_Ip_g = mean_Ip_g - mean_I_g * mean_p # b x C x H x W
cov_Ip_b = mean_Ip_b - mean_I_b * mean_p # b x C x H x W
var_I_rr = boxfilter2d(guide_r * guide_r, radius) / N - mean_I_r * mean_I_r + eps # b x 1 x H x W
var_I_rg = boxfilter2d(guide_r * guide_g, radius) / N - mean_I_r * mean_I_g # b x 1 x H x W
var_I_rb = boxfilter2d(guide_r * guide_b, radius) / N - mean_I_r * mean_I_b # b x 1 x H x W
var_I_gg = boxfilter2d(guide_g * guide_g, radius) / N - mean_I_g * mean_I_g + eps # b x 1 x H x W
var_I_gb = boxfilter2d(guide_g * guide_b, radius) / N - mean_I_g * mean_I_b # b x 1 x H x W
var_I_bb = boxfilter2d(guide_b * guide_b, radius) / N - mean_I_b * mean_I_b + eps # b x 1 x H x W
# determinant
cov_det = var_I_rr * var_I_gg * var_I_bb \
+ var_I_rg * var_I_gb * var_I_rb \
+ var_I_rb * var_I_rg * var_I_gb \
- var_I_rb * var_I_gg * var_I_rb \
- var_I_rg * var_I_rg * var_I_bb \
- var_I_rr * var_I_gb * var_I_gb # b x 1 x H x W
# inverse
inv_var_I_rr = (var_I_gg * var_I_bb - var_I_gb * var_I_gb) / cov_det # b x 1 x H x W
inv_var_I_rg = - (var_I_rg * var_I_bb - var_I_rb * var_I_gb) / cov_det # b x 1 x H x W
inv_var_I_rb = (var_I_rg * var_I_gb - var_I_rb * var_I_gg) / cov_det # b x 1 x H x W
inv_var_I_gg = (var_I_rr * var_I_bb - var_I_rb * var_I_rb) / cov_det # b x 1 x H x W
inv_var_I_gb = - (var_I_rr * var_I_gb - var_I_rb * var_I_rg) / cov_det # b x 1 x H x W
inv_var_I_bb = (var_I_rr * var_I_gg - var_I_rg * var_I_rg) / cov_det # b x 1 x H x W
inv_sigma = torch.stack([
torch.stack([inv_var_I_rr, inv_var_I_rg, inv_var_I_rb], 1),
torch.stack([inv_var_I_rg, inv_var_I_gg, inv_var_I_gb], 1),
torch.stack([inv_var_I_rb, inv_var_I_gb, inv_var_I_bb], 1)
], 1).squeeze(-3) # b x 3 x 3 x H x W
cov_Ip = torch.stack([cov_Ip_r, cov_Ip_g, cov_Ip_b], 1) # b x 3 x C x H x W
a = torch.einsum("bichw,bijhw->bjchw", (cov_Ip, inv_sigma))
b = mean_p - a[:, 0] * mean_I_r - a[:, 1] * mean_I_g - a[:, 2] * mean_I_b # b x C x H x W
mean_a = torch.stack([boxfilter2d(a[:, i], radius) / N for i in range(3)], 1)
mean_b = boxfilter2d(b, radius) / N
if scale is not None:
guide = guide_sub
mean_a = torch.stack([F.interpolate(mean_a[:, i], guide.shape[-2:], mode='bilinear') for i in range(3)], 1)
mean_b = F.interpolate(mean_b, guide.shape[-2:], mode='bilinear')
q = torch.einsum("bichw,bihw->bchw", (mean_a, guide)) + mean_b
return q
def guidedfilter2d_gray(guide, src, radius, eps, scale=None):
"""guided filter for a gray scale guide image
Parameters
-----
guide: (B, 1, H, W)-dim torch.Tensor
guide image
src: (B, C, H, W)-dim torch.Tensor
filtering image
radius: int
filter radius
eps: float
regularization coefficient
"""
if guide.ndim == 3:
guide = guide[:, None]
if src.ndim == 3:
src = src[:, None]
if scale is not None:
guide_sub = guide.clone()
src = F.interpolate(src, scale_factor=1./scale, mode="nearest")
guide = F.interpolate(guide, scale_factor=1./scale, mode="nearest")
radius = radius // scale
ones = torch.ones_like(guide)
N = boxfilter2d(ones, radius)
mean_I = boxfilter2d(guide, radius) / N
mean_p = boxfilter2d(src, radius) / N
mean_Ip = boxfilter2d(guide*src, radius) / N
cov_Ip = mean_Ip - mean_I * mean_p
mean_II = boxfilter2d(guide*guide, radius) / N
var_I = mean_II - mean_I * mean_I
a = cov_Ip / (var_I + eps)
b = mean_p - a * mean_I
mean_a = boxfilter2d(a, radius) / N
mean_b = boxfilter2d(b, radius) / N
if scale is not None:
guide = guide_sub
mean_a = F.interpolate(mean_a, guide.shape[-2:], mode='bilinear')
mean_b = F.interpolate(mean_b, guide.shape[-2:], mode='bilinear')
q = mean_a * guide + mean_b
return q