-
Notifications
You must be signed in to change notification settings - Fork 11
/
bilateral_filter.py
257 lines (234 loc) · 9.56 KB
/
bilateral_filter.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
#
# 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)