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bilateral_filter.py
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bilateral_filter.py
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#
# Classes and functions in this script are taken from https://github.com/vt-vl-lab/3d-photo-inpainting/blob/master/bilateral_filtering.py
# Licensed under MIT license
#
import numpy as np
from functools import reduce
import time
__all__ = ["sparse_bilateral_filtering"]
def sparse_bilateral_filtering(
depth,
image,
filter_size,
sigma_r=0.5,
sigma_s=4.0,
depth_threshold=0.04,
HR=False,
mask=None,
gsHR=True,
edge_id=None,
num_iter=None,
num_gs_iter=None,
):
save_discontinuities = []
vis_depth = depth.copy()
backup_vis_depth = vis_depth.copy()
depth_max = vis_depth.max()
depth_min = vis_depth.min()
vis_image = image.copy()
for i in range(num_iter):
vis_image = image.copy()
u_over, b_over, l_over, r_over = vis_depth_discontinuity(
vis_depth, depth_threshold, mask=mask
)
vis_image[u_over > 0] = np.array([0, 0, 0])
vis_image[b_over > 0] = np.array([0, 0, 0])
vis_image[l_over > 0] = np.array([0, 0, 0])
vis_image[r_over > 0] = np.array([0, 0, 0])
discontinuity_map = (u_over + b_over + l_over + r_over).clip(0.0, 1.0)
discontinuity_map[depth == 0] = 1
save_discontinuities.append(discontinuity_map)
if mask is not None:
discontinuity_map[mask == 0] = 0
vis_depth = bilateral_filter(
vis_depth,
sigma_r=sigma_r,
sigma_s=sigma_s,
discontinuity_map=discontinuity_map,
HR=HR,
mask=mask,
window_size=filter_size[i],
)
return vis_depth
def vis_depth_discontinuity(
depth, depth_threshold, vis_diff=False, label=False, mask=None
):
if label == False:
disp = 1.0 / depth
u_diff = (disp[1:, :] - disp[:-1, :])[:-1, 1:-1]
b_diff = (disp[:-1, :] - disp[1:, :])[1:, 1:-1]
l_diff = (disp[:, 1:] - disp[:, :-1])[1:-1, :-1]
r_diff = (disp[:, :-1] - disp[:, 1:])[1:-1, 1:]
if mask is not None:
u_mask = (mask[1:, :] * mask[:-1, :])[:-1, 1:-1]
b_mask = (mask[:-1, :] * mask[1:, :])[1:, 1:-1]
l_mask = (mask[:, 1:] * mask[:, :-1])[1:-1, :-1]
r_mask = (mask[:, :-1] * mask[:, 1:])[1:-1, 1:]
u_diff = u_diff * u_mask
b_diff = b_diff * b_mask
l_diff = l_diff * l_mask
r_diff = r_diff * r_mask
u_over = (np.abs(u_diff) > depth_threshold).astype(np.float32)
b_over = (np.abs(b_diff) > depth_threshold).astype(np.float32)
l_over = (np.abs(l_diff) > depth_threshold).astype(np.float32)
r_over = (np.abs(r_diff) > depth_threshold).astype(np.float32)
else:
disp = depth
u_diff = (disp[1:, :] * disp[:-1, :])[:-1, 1:-1]
b_diff = (disp[:-1, :] * disp[1:, :])[1:, 1:-1]
l_diff = (disp[:, 1:] * disp[:, :-1])[1:-1, :-1]
r_diff = (disp[:, :-1] * disp[:, 1:])[1:-1, 1:]
if mask is not None:
u_mask = (mask[1:, :] * mask[:-1, :])[:-1, 1:-1]
b_mask = (mask[:-1, :] * mask[1:, :])[1:, 1:-1]
l_mask = (mask[:, 1:] * mask[:, :-1])[1:-1, :-1]
r_mask = (mask[:, :-1] * mask[:, 1:])[1:-1, 1:]
u_diff = u_diff * u_mask
b_diff = b_diff * b_mask
l_diff = l_diff * l_mask
r_diff = r_diff * r_mask
u_over = (np.abs(u_diff) > 0).astype(np.float32)
b_over = (np.abs(b_diff) > 0).astype(np.float32)
l_over = (np.abs(l_diff) > 0).astype(np.float32)
r_over = (np.abs(r_diff) > 0).astype(np.float32)
u_over = np.pad(u_over, 1, mode="constant")
b_over = np.pad(b_over, 1, mode="constant")
l_over = np.pad(l_over, 1, mode="constant")
r_over = np.pad(r_over, 1, mode="constant")
u_diff = np.pad(u_diff, 1, mode="constant")
b_diff = np.pad(b_diff, 1, mode="constant")
l_diff = np.pad(l_diff, 1, mode="constant")
r_diff = np.pad(r_diff, 1, mode="constant")
if vis_diff:
return [u_over, b_over, l_over, r_over], [u_diff, b_diff, l_diff, r_diff]
else:
return [u_over, b_over, l_over, r_over]
def bilateral_filter(
depth,
sigma_s,
sigma_r,
window_size,
discontinuity_map=None,
HR=False,
mask=None,
):
sort_time = 0
replace_time = 0
filter_time = 0
init_time = 0
filtering_time = 0
midpt = window_size // 2
ax = np.arange(-midpt, midpt + 1.0)
xx, yy = np.meshgrid(ax, ax)
if discontinuity_map is not None:
spatial_term = np.exp(-(xx ** 2 + yy ** 2) / (2.0 * sigma_s ** 2))
# padding
depth = depth[1:-1, 1:-1]
depth = np.pad(depth, ((1, 1), (1, 1)), "edge")
pad_depth = np.pad(depth, (midpt, midpt), "edge")
if discontinuity_map is not None:
discontinuity_map = discontinuity_map[1:-1, 1:-1]
discontinuity_map = np.pad(discontinuity_map, ((1, 1), (1, 1)), "edge")
pad_discontinuity_map = np.pad(discontinuity_map, (midpt, midpt), "edge")
pad_discontinuity_hole = 1 - pad_discontinuity_map
# filtering
output = depth.copy()
pad_depth_patches = rolling_window(pad_depth, [window_size, window_size], [1, 1])
if discontinuity_map is not None:
pad_discontinuity_patches = rolling_window(
pad_discontinuity_map, [window_size, window_size], [1, 1]
)
pad_discontinuity_hole_patches = rolling_window(
pad_discontinuity_hole, [window_size, window_size], [1, 1]
)
if mask is not None:
pad_mask = np.pad(mask, (midpt, midpt), "constant")
pad_mask_patches = rolling_window(pad_mask, [window_size, window_size], [1, 1])
from itertools import product
if discontinuity_map is not None:
pH, pW = pad_depth_patches.shape[:2]
for pi in range(pH):
for pj in range(pW):
if mask is not None and mask[pi, pj] == 0:
continue
if discontinuity_map is not None:
if bool(pad_discontinuity_patches[pi, pj].any()) is False:
continue
discontinuity_patch = pad_discontinuity_patches[pi, pj]
discontinuity_holes = pad_discontinuity_hole_patches[pi, pj]
depth_patch = pad_depth_patches[pi, pj]
depth_order = depth_patch.ravel().argsort()
patch_midpt = depth_patch[window_size // 2, window_size // 2]
if discontinuity_map is not None:
coef = discontinuity_holes.astype(np.float32)
if mask is not None:
coef = coef * pad_mask_patches[pi, pj]
else:
range_term = np.exp(
-((depth_patch - patch_midpt) ** 2) / (2.0 * sigma_r ** 2)
)
coef = spatial_term * range_term
if coef.max() == 0:
output[pi, pj] = patch_midpt
continue
if discontinuity_map is not None and (coef.max() == 0):
output[pi, pj] = patch_midpt
else:
coef = coef / (coef.sum())
coef_order = coef.ravel()[depth_order]
cum_coef = np.cumsum(coef_order)
ind = np.digitize(0.5, cum_coef)
output[pi, pj] = depth_patch.ravel()[depth_order][ind]
else:
pH, pW = pad_depth_patches.shape[:2]
for pi in range(pH):
for pj in range(pW):
if discontinuity_map is not None:
if (
pad_discontinuity_patches[pi, pj][
window_size // 2, window_size // 2
]
== 1
):
continue
discontinuity_patch = pad_discontinuity_patches[pi, pj]
discontinuity_holes = 1.0 - discontinuity_patch
depth_patch = pad_depth_patches[pi, pj]
depth_order = depth_patch.ravel().argsort()
patch_midpt = depth_patch[window_size // 2, window_size // 2]
range_term = np.exp(
-((depth_patch - patch_midpt) ** 2) / (2.0 * sigma_r ** 2)
)
if discontinuity_map is not None:
coef = spatial_term * range_term * discontinuity_holes
else:
coef = spatial_term * range_term
if coef.sum() == 0:
output[pi, pj] = patch_midpt
continue
if discontinuity_map is not None and (coef.sum() == 0):
output[pi, pj] = patch_midpt
else:
coef = coef / (coef.sum())
coef_order = coef.ravel()[depth_order]
cum_coef = np.cumsum(coef_order)
ind = np.digitize(0.5, cum_coef)
output[pi, pj] = depth_patch.ravel()[depth_order][ind]
return output
def rolling_window(a, window, strides):
assert (
len(a.shape) == len(window) == len(strides)
), "'a', 'window', 'strides' dimension mismatch"
shape_fn = lambda i, w, s: (a.shape[i] - w) // s + 1
shape = [shape_fn(i, w, s) for i, (w, s) in enumerate(zip(window, strides))] + list(
window
)
def acc_shape(i):
if i + 1 >= len(a.shape):
return 1
else:
return reduce(lambda x, y: x * y, a.shape[i + 1 :])
_strides = [acc_shape(i) * s * a.itemsize for i, s in enumerate(strides)] + list(
a.strides
)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=_strides)