-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathobject_filling.py
207 lines (164 loc) · 7.43 KB
/
object_filling.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
import torch
import numpy as np
from alive_progress import alive_bar
from skimage import measure, segmentation
import skimage.io, skimage.transform
import matplotlib.pyplot as plt
import matplotlib as mpl
import cupy as cp
def get_plane(img):
img_loader_x = skimage.io.imread('scanner/normalized_vectors_x_plus_half.tif')
img_loader_y = skimage.io.imread('scanner/normalized_vectors_y_plus_half.tif')
nv_x = cp.asarray(skimage.img_as_float(img_loader_x) - 0.5)
nv_y = cp.asarray(skimage.img_as_float(img_loader_y) - 0.5)
img_mean = cp.mean(img[img != 0])
mean_error_map = cp.power((img - img_mean), 2)
mean_error_map = cp.where(img > 0, mean_error_map, cp.infty)
mean_sorted = cp.dstack(cp.unravel_index(cp.argsort(mean_error_map.ravel()), img.shape))[0]
depth_sorted = cp.dstack(cp.unravel_index(cp.argsort(img.ravel()), img.shape))[0]
a = mean_sorted[:20000]
b = depth_sorted
acceptable_mean = mean_error_map[a[19999, 0], a[19999, 1]]
acceptable_depth = img[b[440000, 0], b[440000, 1]]
img1 = cp.where(img == 0, 1.0, img)
x = nv_x * img1
y = nv_y * img1 * (-1.0)
z = img1.copy()
mesh_points = cp.empty((int(img.shape[0] / 20) * int(img.shape[1] / 20), 2))
for i in range(int(img.shape[0] / 20)):
for j in range(int(img.shape[1] / 20)):
mesh_points[int(i * img.shape[1] / 20) + j, 0] = i * 20
mesh_points[int(i * img.shape[1] / 20) + j, 1] = j * 20
mean_points = None
depth_points = None
for i in range(mesh_points.shape[0]):
if mean_error_map[int(mesh_points[i, 0]), int(mesh_points[i, 1])] < acceptable_mean:
if mean_points is None:
mean_points = cp.array([mesh_points[i]])
else:
mean_points = cp.vstack([mean_points, mesh_points[i]])
if img[int(mesh_points[i, 0]), int(mesh_points[i, 1])] > acceptable_depth:
if depth_points is None:
depth_points = cp.array([mesh_points[i]])
else:
depth_points = cp.vstack([depth_points, mesh_points[i]])
mean_points = mean_points[mean_points[:, 1].argsort()]
depth_points = depth_points[depth_points[:, 1].argsort()]
'''
cmap = mpl.cm.get_cmap("winter").copy()
cmap.set_under(color='black')
plt.figure(plt.figure('HR Depth map'))
plt.imshow(img.get(), cmap=cmap, vmin=0.001)
for i in range(mean_points.shape[0]):
plt.scatter(int(mean_points[i, 1]), int(mean_points[i, 0]), c='pink', marker='o')
for i in range(depth_points.shape[0]):
plt.scatter(int(depth_points[i, 1]), int(depth_points[i, 0]), c='yellow', marker='o')
plt.show()
'''
mean_X = cp.empty((mean_points.shape[0]))
mean_Y = cp.empty((mean_points.shape[0]))
mean_Z = cp.empty((mean_points.shape[0]))
for i in range(mean_points.shape[0]):
mean_X[i] = x[int(mean_points[i, 0]), int(mean_points[i, 1])]
mean_Y[i] = y[int(mean_points[i, 0]), int(mean_points[i, 1])]
mean_Z[i] = z[int(mean_points[i, 0]), int(mean_points[i, 1])]
real_mean_points = cp.c_[mean_X, mean_Y, mean_Z]
depth_X = cp.empty((depth_points.shape[0]))
depth_Y = cp.empty((depth_points.shape[0]))
depth_Z = cp.empty((depth_points.shape[0]))
for i in range(depth_points.shape[0]):
depth_X[i] = x[int(depth_points[i, 0]), int(depth_points[i, 1])]
depth_Y[i] = y[int(depth_points[i, 0]), int(depth_points[i, 1])]
depth_Z[i] = z[int(depth_points[i, 0]), int(depth_points[i, 1])]
real_depth_points = cp.c_[depth_X, depth_Y, depth_Z]
A = real_mean_points[0]
B = real_depth_points[int(real_depth_points.shape[0] / 2)]
C = real_mean_points[real_mean_points.shape[0] - 1]
v1 = C - A
v2 = B - A
'''
cmap = mpl.cm.get_cmap("winter").copy()
cmap.set_under(color='black')
plt.figure(plt.figure('HR Depth map'))
plt.imshow(img, cmap=cmap, vmin=0.001)
plt.scatter(int(mean_points[0, 1]), int(mean_points[0, 0]), c='red', marker='o')
plt.scatter(int(depth_points[int(depth_points.shape[0] / 2), 1]), int(depth_points[int(depth_points.shape[0] / 2), 0]), c='red', marker='o')
plt.scatter(int(mean_points[mean_points.shape[0] - 1, 1]), int(mean_points[mean_points.shape[0] - 1, 0]), c='red', marker='o')
plt.show()
'''
normal = cp.cross(v1, v2)
d = -cp.sum(normal * A)
full_plane = -d / (normal[0] * nv_x + normal[1] * nv_y * (-1.0) + normal[2])
img_dis = abs(normal[0] * x + + normal[1] * y + normal[2] * img + d) / cp.sqrt(normal[0]**2 + normal[1]**2 + normal[2]**2)
img_dis = cp.where(img == 0, 0, img_dis)
return full_plane, img_dis
def fill_depth_map(img, background_value):
mask = np.where(img > 0, 0, 1).astype(np.uint8)
labels, holes = measure.label(mask, background=0, return_num=True)
img = cp.asarray(img)
labels = cp.asarray(labels)
print("Filling " + str(holes) + " holes")
with alive_bar(holes) as bar:
for i in range(1, holes + 1):
label = labels == i
label_img_border_pixels = cp.sum(label.astype(cp.int32)[0, :]) + \
cp.sum(label.astype(cp.int32)[label.shape[0] - 1, :]) + \
cp.sum(label.astype(cp.int32)[1:(label.shape[1] - 1), 0]) + \
cp.sum(label.astype(cp.int32)[1:(label.shape[1] - 1), label.shape[1] - 1])
if label_img_border_pixels == 0:
top = cp.roll(label, 1, axis=0)
bottom = cp.roll(label, -1, axis=0)
right = cp.roll(label, 1, axis=1)
left = cp.roll(label, -1, axis=1)
hole_border = cp.logical_or(cp.logical_or(top, bottom), cp.logical_or(right, left))
border_values = img * hole_border
background = label_img_border_pixels > 0
if not background:
line_values = border_values.max(1)
hole = (labels == i).astype(float) * line_values[:, cp.newaxis]
else:
hole = (labels == i).astype(float) * 0
bar()
img = cp.where(hole > 0, hole, img)
plane, dis = get_plane(img)
object_map = cp.where(dis < 10, 0, 1.0)
img = cp.where(img == 0, background_value, img)
'''
cmap = mpl.cm.get_cmap("winter").copy()
cmap.set_under(color='black')
plt.figure(plt.figure('HR Plane'))
plt.imshow(object_map, cmap='gray')
plt.figure(plt.figure('HR Depth map'))
plt.imshow(img, cmap=cmap, vmin=0.001)
plt.show()
'''
return img.get(), object_map.get()
def fill_texture(tex, def_map):
mask = np.where(def_map > 0, 0, 1).astype(np.uint8)
labels, holes = measure.label(mask, background=0, return_num=True)
tex = cp.asarray(tex)
labels = cp.asarray(labels)
print("Filling " + str(holes) + " holes")
with alive_bar(holes) as bar:
for i in range(1, holes + 1):
label = labels == i
label_img_border_pixels = cp.sum(label.astype(cp.int32)[0, :]) + \
cp.sum(label.astype(cp.int32)[label.shape[0] - 1, :]) + \
cp.sum(label.astype(cp.int32)[1:(label.shape[1] - 1), 0]) + \
cp.sum(label.astype(cp.int32)[1:(label.shape[1] - 1), label.shape[1] - 1])
if label_img_border_pixels == 0:
top = cp.roll(label, 1, axis=0)
bottom = cp.roll(label, -1, axis=0)
right = cp.roll(label, 1, axis=1)
left = cp.roll(label, -1, axis=1)
hole_border = cp.logical_or(cp.logical_or(top, bottom), cp.logical_or(right, left))
border_values = tex * hole_border
background = label_img_border_pixels > 0
if not background:
line_values = border_values.max(1)
hole = (labels == i).astype(float) * line_values[:, cp.newaxis]
else:
hole = (labels == i).astype(float) * 0
bar()
tex = cp.where(hole > 0, hole, tex)
return tex.get()