-
Notifications
You must be signed in to change notification settings - Fork 237
/
example_3d.py
142 lines (119 loc) · 3.96 KB
/
example_3d.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
# coding: utf-8
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from ensemble_boxes import *
def plot_cube(ax, cube_definition, lbl, thickness):
cube_definition_array = [
np.array(list(item))
for item in cube_definition
]
points = []
points += cube_definition_array
vectors = [
cube_definition_array[1] - cube_definition_array[0],
cube_definition_array[2] - cube_definition_array[0],
cube_definition_array[3] - cube_definition_array[0]
]
points += [cube_definition_array[0] + vectors[0] + vectors[1]]
points += [cube_definition_array[0] + vectors[0] + vectors[2]]
points += [cube_definition_array[0] + vectors[1] + vectors[2]]
points += [cube_definition_array[0] + vectors[0] + vectors[1] + vectors[2]]
points = np.array(points)
edges = [
[points[0], points[3], points[5], points[1]],
[points[1], points[5], points[7], points[4]],
[points[4], points[2], points[6], points[7]],
[points[2], points[6], points[3], points[0]],
[points[0], points[2], points[4], points[1]],
[points[3], points[6], points[7], points[5]]
]
faces = Poly3DCollection(edges, linewidths=thickness + 1)
if lbl == 0:
faces.set_edgecolor((1, 0, 0))
else:
faces.set_edgecolor((0, 0, 1))
faces.set_facecolor((0, 0, 1, 0.1))
ax.add_collection3d(faces)
ax.scatter(points[:, 0], points[:, 1], points[:, 2], s=0)
def show_boxes(boxes_list, scores_list, labels_list, image_size=800):
image = np.zeros((image_size, image_size, 3), dtype=np.uint8)
image[...] = 255
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in range(len(boxes_list)):
for j in range(len(boxes_list[i])):
x1 = int(image_size * boxes_list[i][j][0])
y1 = int(image_size * boxes_list[i][j][1])
z1 = int(image_size * boxes_list[i][j][2])
x2 = int(image_size * boxes_list[i][j][3])
y2 = int(image_size * boxes_list[i][j][4])
z2 = int(image_size * boxes_list[i][j][5])
lbl = labels_list[i][j]
cube_definition = [
(x1, y1, z1), (x1, y2, z1), (x2, y1, z1), (x1, y1, z2)
]
plot_cube(ax, cube_definition, lbl, int(4 * scores_list[i][j]))
plt.show()
def example_wbf_3d_2_models(iou_thr=0.55, draw_image=True):
"""
This example shows how to ensemble boxes from 2 models using WBF_3D method
:return:
"""
boxes_list = [
[
[0.00, 0.51, 0.41, 0.81, 0.91, 0.78],
[0.10, 0.31, 0.45, 0.71, 0.61, 0.85],
[0.01, 0.32, 0.55, 0.83, 0.93, 0.95],
[0.02, 0.53, 0.11, 0.11, 0.94, 0.55],
[0.03, 0.24, 0.34, 0.12, 0.35, 0.67],
],
[
[0.04, 0.56, 0.36, 0.84, 0.92, 0.82],
[0.12, 0.33, 0.46, 0.72, 0.64, 0.75],
[0.38, 0.66, 0.55, 0.79, 0.95, 0.90],
[0.08, 0.49, 0.15, 0.21, 0.89, 0.67],
],
]
scores_list = [
[
0.9,
0.8,
0.2,
0.4,
0.7,
],
[
0.5,
0.8,
0.7,
0.3,
]
]
labels_list = [
[
0,
1,
0,
1,
1,
],
[
1,
1,
1,
0,
]
]
weights = [2, 1]
if draw_image:
show_boxes(boxes_list, scores_list, labels_list)
boxes, scores, labels = weighted_boxes_fusion_3d(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=0.0)
if draw_image:
show_boxes([boxes], [scores], [labels.astype(np.int32)])
print(len(boxes))
print(boxes)
if __name__ == '__main__':
draw_image = True
example_wbf_3d_2_models(iou_thr=0.2, draw_image=draw_image)