-
Notifications
You must be signed in to change notification settings - Fork 1.6k
/
depth_box3d.py
270 lines (226 loc) · 10.5 KB
/
depth_box3d.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
258
259
260
261
262
263
264
265
266
267
268
269
270
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet3d.core.points import BasePoints
from .base_box3d import BaseInstance3DBoxes
from .utils import rotation_3d_in_axis
class DepthInstance3DBoxes(BaseInstance3DBoxes):
"""3D boxes of instances in Depth coordinates.
Coordinates in Depth:
.. code-block:: none
up z y front (yaw=-0.5*pi)
^ ^
| /
| /
0 ------> x right (yaw=0)
The relative coordinate of bottom center in a Depth box is (0.5, 0.5, 0),
and the yaw is around the z axis, thus the rotation axis=2.
The yaw is 0 at the positive direction of x axis, and decreases from
the positive direction of x to the positive direction of y.
Also note that rotation of DepthInstance3DBoxes is counterclockwise,
which is reverse to the definition of the yaw angle (clockwise).
A refactor is ongoing to make the three coordinate systems
easier to understand and convert between each other.
Attributes:
tensor (torch.Tensor): Float matrix of N x box_dim.
box_dim (int): Integer indicates the dimension of a box
Each row is (x, y, z, x_size, y_size, z_size, yaw, ...).
with_yaw (bool): If True, the value of yaw will be set to 0 as minmax
boxes.
"""
YAW_AXIS = 2
@property
def gravity_center(self):
"""torch.Tensor: A tensor with center of each box in shape (N, 3)."""
bottom_center = self.bottom_center
gravity_center = torch.zeros_like(bottom_center)
gravity_center[:, :2] = bottom_center[:, :2]
gravity_center[:, 2] = bottom_center[:, 2] + self.tensor[:, 5] * 0.5
return gravity_center
@property
def corners(self):
"""torch.Tensor: Coordinates of corners of all the boxes
in shape (N, 8, 3).
Convert the boxes to corners in clockwise order, in form of
``(x0y0z0, x0y0z1, x0y1z1, x0y1z0, x1y0z0, x1y0z1, x1y1z1, x1y1z0)``
.. code-block:: none
up z
front y ^
/ |
/ |
(x0, y1, z1) + ----------- + (x1, y1, z1)
/| / |
/ | / |
(x0, y0, z1) + ----------- + + (x1, y1, z0)
| / . | /
| / origin | /
(x0, y0, z0) + ----------- + --------> right x
(x1, y0, z0)
"""
if self.tensor.numel() == 0:
return torch.empty([0, 8, 3], device=self.tensor.device)
dims = self.dims
corners_norm = torch.from_numpy(
np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1)).to(
device=dims.device, dtype=dims.dtype)
corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]]
# use relative origin (0.5, 0.5, 0)
corners_norm = corners_norm - dims.new_tensor([0.5, 0.5, 0])
corners = dims.view([-1, 1, 3]) * corners_norm.reshape([1, 8, 3])
# rotate around z axis
corners = rotation_3d_in_axis(
corners, self.tensor[:, 6], axis=self.YAW_AXIS)
corners += self.tensor[:, :3].view(-1, 1, 3)
return corners
def rotate(self, angle, points=None):
"""Rotate boxes with points (optional) with the given angle or rotation
matrix.
Args:
angle (float | torch.Tensor | np.ndarray):
Rotation angle or rotation matrix.
points (torch.Tensor | np.ndarray | :obj:`BasePoints`, optional):
Points to rotate. Defaults to None.
Returns:
tuple or None: When ``points`` is None, the function returns
None, otherwise it returns the rotated points and the
rotation matrix ``rot_mat_T``.
"""
if not isinstance(angle, torch.Tensor):
angle = self.tensor.new_tensor(angle)
assert angle.shape == torch.Size([3, 3]) or angle.numel() == 1, \
f'invalid rotation angle shape {angle.shape}'
if angle.numel() == 1:
self.tensor[:, 0:3], rot_mat_T = rotation_3d_in_axis(
self.tensor[:, 0:3],
angle,
axis=self.YAW_AXIS,
return_mat=True)
else:
rot_mat_T = angle
rot_sin = rot_mat_T[0, 1]
rot_cos = rot_mat_T[0, 0]
angle = np.arctan2(rot_sin, rot_cos)
self.tensor[:, 0:3] = self.tensor[:, 0:3] @ rot_mat_T
if self.with_yaw:
self.tensor[:, 6] += angle
else:
# for axis-aligned boxes, we take the new
# enclosing axis-aligned boxes after rotation
corners_rot = self.corners @ rot_mat_T
new_x_size = corners_rot[..., 0].max(
dim=1, keepdim=True)[0] - corners_rot[..., 0].min(
dim=1, keepdim=True)[0]
new_y_size = corners_rot[..., 1].max(
dim=1, keepdim=True)[0] - corners_rot[..., 1].min(
dim=1, keepdim=True)[0]
self.tensor[:, 3:5] = torch.cat((new_x_size, new_y_size), dim=-1)
if points is not None:
if isinstance(points, torch.Tensor):
points[:, :3] = points[:, :3] @ rot_mat_T
elif isinstance(points, np.ndarray):
rot_mat_T = rot_mat_T.cpu().numpy()
points[:, :3] = np.dot(points[:, :3], rot_mat_T)
elif isinstance(points, BasePoints):
points.rotate(rot_mat_T)
else:
raise ValueError
return points, rot_mat_T
def flip(self, bev_direction='horizontal', points=None):
"""Flip the boxes in BEV along given BEV direction.
In Depth coordinates, it flips x (horizontal) or y (vertical) axis.
Args:
bev_direction (str, optional): Flip direction
(horizontal or vertical). Defaults to 'horizontal'.
points (torch.Tensor | np.ndarray | :obj:`BasePoints`, optional):
Points to flip. Defaults to None.
Returns:
torch.Tensor, numpy.ndarray or None: Flipped points.
"""
assert bev_direction in ('horizontal', 'vertical')
if bev_direction == 'horizontal':
self.tensor[:, 0::7] = -self.tensor[:, 0::7]
if self.with_yaw:
self.tensor[:, 6] = -self.tensor[:, 6] + np.pi
elif bev_direction == 'vertical':
self.tensor[:, 1::7] = -self.tensor[:, 1::7]
if self.with_yaw:
self.tensor[:, 6] = -self.tensor[:, 6]
if points is not None:
assert isinstance(points, (torch.Tensor, np.ndarray, BasePoints))
if isinstance(points, (torch.Tensor, np.ndarray)):
if bev_direction == 'horizontal':
points[:, 0] = -points[:, 0]
elif bev_direction == 'vertical':
points[:, 1] = -points[:, 1]
elif isinstance(points, BasePoints):
points.flip(bev_direction)
return points
def convert_to(self, dst, rt_mat=None):
"""Convert self to ``dst`` mode.
Args:
dst (:obj:`Box3DMode`): The target Box mode.
rt_mat (np.ndarray | torch.Tensor, optional): The rotation and
translation matrix between different coordinates.
Defaults to None.
The conversion from ``src`` coordinates to ``dst`` coordinates
usually comes along the change of sensors, e.g., from camera
to LiDAR. This requires a transformation matrix.
Returns:
:obj:`DepthInstance3DBoxes`:
The converted box of the same type in the ``dst`` mode.
"""
from .box_3d_mode import Box3DMode
return Box3DMode.convert(
box=self, src=Box3DMode.DEPTH, dst=dst, rt_mat=rt_mat)
def enlarged_box(self, extra_width):
"""Enlarge the length, width and height boxes.
Args:
extra_width (float | torch.Tensor): Extra width to enlarge the box.
Returns:
:obj:`DepthInstance3DBoxes`: Enlarged boxes.
"""
enlarged_boxes = self.tensor.clone()
enlarged_boxes[:, 3:6] += extra_width * 2
# bottom center z minus extra_width
enlarged_boxes[:, 2] -= extra_width
return self.new_box(enlarged_boxes)
def get_surface_line_center(self):
"""Compute surface and line center of bounding boxes.
Returns:
torch.Tensor: Surface and line center of bounding boxes.
"""
obj_size = self.dims
center = self.gravity_center.view(-1, 1, 3)
batch_size = center.shape[0]
rot_sin = torch.sin(-self.yaw)
rot_cos = torch.cos(-self.yaw)
rot_mat_T = self.yaw.new_zeros(tuple(list(self.yaw.shape) + [3, 3]))
rot_mat_T[..., 0, 0] = rot_cos
rot_mat_T[..., 0, 1] = -rot_sin
rot_mat_T[..., 1, 0] = rot_sin
rot_mat_T[..., 1, 1] = rot_cos
rot_mat_T[..., 2, 2] = 1
# Get the object surface center
offset = obj_size.new_tensor([[0, 0, 1], [0, 0, -1], [0, 1, 0],
[0, -1, 0], [1, 0, 0], [-1, 0, 0]])
offset = offset.view(1, 6, 3) / 2
surface_3d = (offset *
obj_size.view(batch_size, 1, 3).repeat(1, 6, 1)).reshape(
-1, 3)
# Get the object line center
offset = obj_size.new_tensor([[1, 0, 1], [-1, 0, 1], [0, 1, 1],
[0, -1, 1], [1, 0, -1], [-1, 0, -1],
[0, 1, -1], [0, -1, -1], [1, 1, 0],
[1, -1, 0], [-1, 1, 0], [-1, -1, 0]])
offset = offset.view(1, 12, 3) / 2
line_3d = (offset *
obj_size.view(batch_size, 1, 3).repeat(1, 12, 1)).reshape(
-1, 3)
surface_rot = rot_mat_T.repeat(6, 1, 1)
surface_3d = torch.matmul(surface_3d.unsqueeze(-2),
surface_rot).squeeze(-2)
surface_center = center.repeat(1, 6, 1).reshape(-1, 3) + surface_3d
line_rot = rot_mat_T.repeat(12, 1, 1)
line_3d = torch.matmul(line_3d.unsqueeze(-2), line_rot).squeeze(-2)
line_center = center.repeat(1, 12, 1).reshape(-1, 3) + line_3d
return surface_center, line_center