Spaces:
Runtime error
Runtime error
File size: 9,482 Bytes
0b9f920 |
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 |
import numpy as np
from functools import reduce
def sparse_bilateral_filtering(
depth, image, config, HR=False, mask=None, gsHR=True, edge_id=None, num_iter=None, num_gs_iter=None, spdb=False
):
"""
config:
- filter_size
"""
import time
save_images = []
save_depths = []
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):
if isinstance(config["filter_size"], list):
window_size = config["filter_size"][i]
else:
window_size = config["filter_size"]
vis_image = image.copy()
save_images.append(vis_image)
save_depths.append(vis_depth)
u_over, b_over, l_over, r_over = vis_depth_discontinuity(vis_depth, config, 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, config, discontinuity_map=discontinuity_map, HR=HR, mask=mask, window_size=window_size
)
return save_images, save_depths
def vis_depth_discontinuity(depth, config, vis_diff=False, label=False, mask=None):
"""
config:
-
"""
if label == False:
disp = 1./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) > config['depth_threshold']).astype(np.float32)
b_over = (np.abs(b_diff) > config['depth_threshold']).astype(np.float32)
l_over = (np.abs(l_diff) > config['depth_threshold']).astype(np.float32)
r_over = (np.abs(r_diff) > config['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, config, discontinuity_map=None, HR=False, mask=None, window_size=False):
sort_time = 0
replace_time = 0
filter_time = 0
init_time = 0
filtering_time = 0
sigma_s = config['sigma_s']
sigma_r = config['sigma_r']
if window_size == False:
window_size = config['filter_size']
midpt = window_size//2
ax = np.arange(-midpt, midpt+1.)
xx, yy = np.meshgrid(ax, ax)
if discontinuity_map is not None:
spatial_term = np.exp(-(xx**2 + yy**2) / (2. * 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. * 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. - 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. * 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)
|