-
Notifications
You must be signed in to change notification settings - Fork 236
/
object_label_record.py
390 lines (325 loc) · 13.3 KB
/
object_label_record.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
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
#!/usr/bin/env python3
# <Copyright 2019, Argo AI, LLC. Released under the MIT license.>
import json
import os
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
from argoverse.utils.calibration import CameraConfig, proj_cam_to_uv
from argoverse.utils.cv2_plotting_utils import add_text_cv2, draw_clipped_line_segment
from argoverse.utils.se3 import SE3
from argoverse.utils.transform import quat2rotmat
from argoverse.visualization.vis_mask import vis_mask
# If plotting a cuboid onto an image, the label category will be drawn in front
# of an alpha-blended rectangle to increase text readability. Rectangle extends
# to px offsets (defined below) from a fixed point. Fixed point is the centroid
# of the vertices comprising the top face of cuboid.
BKGRND_RECT_OFFS_UP = 30 # px
BKGRND_RECT_OFFS_DOWN = 10 # px
BKGRND_RECT_OFFS_LEFT = 70 # px
BKGRND_RECT_OFFS_RIGHT = 70 # px
TEXT_OFFS_LEFT = 70 # px
# Parm to stop plotting text after N meters to prevent plot overcrowding
MAX_RANGE_THRESH_PLOT_CATEGORY = 50 # meters
BLUE_RGB = (0, 0, 255)
RED_RGB = (255, 0, 0)
GREEN_RGB = (0, 255, 0)
WHITE_BGR = (255, 255, 255)
EMERALD_RGB = (80, 220, 100)
BKGRND_RECT_ALPHA = 0.45
TOP_VERT_INDICES: List[int] = [0, 1, 4, 5]
class ObjectLabelRecord:
"""Parameterizes an object via a 3d bounding box and its pose within the egovehicle's reference frame.
We refer to the object's pose as `egovehicle_SE3_object` and is parameterized by (R,t), where R is
a quaternion in scalar-first order.
"""
def __init__(
self,
quaternion: np.ndarray,
translation: np.ndarray,
length: float,
width: float,
height: float,
occlusion: int,
label_class: Optional[str] = None,
track_id: Optional[str] = None,
score: float = 1.0,
) -> None:
"""Create an ObjectLabelRecord.
Args:
quaternion: Numpy vector representing quaternion (qw,qx,qy,qz), box/cuboid orientation.
translation: Numpy vector representing translation, center of box given as x, y, z.
length: object length.
width: object width.
height: object height.
occlusion: occlusion value.
label_class: class label, see object_classes.py for all possible class in argoverse
track_id: object track id, this is unique for each track
"""
self.quaternion = quaternion
self.translation = translation
self.length = length
self.width = width
self.height = height
self.occlusion = occlusion
self.label_class = label_class
self.track_id = track_id
self.score = score
def as_2d_bbox(self) -> np.ndarray:
"""Convert the object cuboid to a 2D bounding box, with vertices provided in the egovehicle's reference frame.
Length is x, width is y, and z is height
Alternatively could write code like::
x_corners = l / 2 * np.array([1, 1, 1, 1, -1, -1, -1, -1])
y_corners = w / 2 * np.array([1, -1, -1, 1, 1, -1, -1, 1])
z_corners = h / 2 * np.array([1, 1, -1, -1, 1, 1, -1, -1])
corners = np.vstack((x_corners, y_corners, z_corners))
"""
bbox_object_frame = np.array(
[
[self.length / 2.0, self.width / 2.0, self.height / 2.0],
[self.length / 2.0, -self.width / 2.0, self.height / 2.0],
[-self.length / 2.0, self.width / 2.0, self.height / 2.0],
[-self.length / 2.0, -self.width / 2.0, self.height / 2.0],
]
)
egovehicle_SE3_object = SE3(rotation=quat2rotmat(self.quaternion), translation=self.translation)
bbox_in_egovehicle_frame = egovehicle_SE3_object.transform_point_cloud(bbox_object_frame)
return bbox_in_egovehicle_frame
def as_3d_bbox(self) -> np.ndarray:
r"""Calculate the 8 bounding box corners (returned as points inside the egovehicle's frame).
Returns:
Numpy array of shape (8,3)
Corner numbering::
5------4
|\\ |\\
| \\ | \\
6--\\--7 \\
\\ \\ \\ \\
l \\ 1-------0 h
e \\ || \\ || e
n \\|| \\|| i
g \\2------3 g
t width. h
h. t.
First four corners are the ones facing forward.
The last four are the ones facing backwards.
"""
# 3D bounding box corners. (Convention: x points forward, y to the left, z up.)
x_corners = self.length / 2 * np.array([1, 1, 1, 1, -1, -1, -1, -1])
y_corners = self.width / 2 * np.array([1, -1, -1, 1, 1, -1, -1, 1])
z_corners = self.height / 2 * np.array([1, 1, -1, -1, 1, 1, -1, -1])
corners_object_frame = np.vstack((x_corners, y_corners, z_corners)).T
egovehicle_SE3_object = SE3(rotation=quat2rotmat(self.quaternion), translation=self.translation)
corners_egovehicle_frame = egovehicle_SE3_object.transform_point_cloud(corners_object_frame)
return corners_egovehicle_frame
def render_clip_frustum_cv2(
self,
img: np.ndarray,
corners: np.ndarray,
planes: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
camera_config: CameraConfig,
colors: Tuple[Tuple[int, int, int], Tuple[int, int, int], Tuple[int, int, int]] = (
BLUE_RGB,
RED_RGB,
GREEN_RGB,
),
linewidth: int = 2,
) -> np.ndarray:
r"""We bring the 3D points into each camera, and do the clipping there.
Renders box using OpenCV2. Edge coloring and vertex ordering is roughly based on
https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/nuscenes_utils/data_classes.py
::
5------4
|\\ |\\
| \\ | \\
6--\\--7 \\
\\ \\ \\ \\
l \\ 1-------0 h
e \\ || \\ || e
n \\|| \\|| i
g \\2------3 g
t width. h
h. t.
Args:
img: Numpy array of shape (M,N,3)
corners: Numpy array of shape (8,3) in camera coordinate frame.
planes: Iterable of 5 clipping planes. Each plane is defined by 4 points.
camera_config: CameraConfig object
colors: tuple of RGB 3-tuples, Colors for front, side & rear.
defaults are 0. blue (0,0,255) in RGB and (255,0,0) in OpenCV's BGR
1. red (255,0,0) in RGB and (0,0,255) in OpenCV's BGR
2. green (0,255,0) in RGB and BGR alike.
linewidth: integer, linewidth for plot
Returns:
img: Numpy array of shape (M,N,3), representing updated image
"""
def draw_rect(selected_corners: np.ndarray, color: Tuple[int, int, int]) -> None:
prev = selected_corners[-1]
for corner in selected_corners:
draw_clipped_line_segment(
img,
prev.copy(),
corner.copy(),
camera_config,
linewidth,
planes,
color,
)
prev = corner
# Draw the sides in green
for i in range(4):
# between front and back corners
draw_clipped_line_segment(
img,
corners[i],
corners[i + 4],
camera_config,
linewidth,
planes,
colors[2][::-1],
)
# Draw front (first 4 corners) in blue
draw_rect(corners[:4], colors[0][::-1])
# Draw rear (last 4 corners) in red
draw_rect(corners[4:], colors[1][::-1])
# grab the top vertices
center_top = np.mean(corners[TOP_VERT_INDICES], axis=0)
uv_ct, _, _, _ = proj_cam_to_uv(center_top.reshape(1, 3), camera_config)
uv_ct = uv_ct.squeeze().astype(np.int32) # cast to integer
if label_is_closeby(center_top) and uv_coord_is_valid(uv_ct, img):
top_left = (uv_ct[0] - BKGRND_RECT_OFFS_LEFT, uv_ct[1] - BKGRND_RECT_OFFS_UP)
bottom_right = (uv_ct[0] + BKGRND_RECT_OFFS_LEFT, uv_ct[1] + BKGRND_RECT_OFFS_DOWN)
img = draw_alpha_rectangle(img, top_left, bottom_right, EMERALD_RGB, alpha=BKGRND_RECT_ALPHA)
add_text_cv2(img, text=str(self.label_class), x=uv_ct[0] - TEXT_OFFS_LEFT, y=uv_ct[1], color=WHITE_BGR)
# Draw blue line indicating the front half
center_bottom_forward = np.mean(corners[2:4], axis=0)
center_bottom = np.mean(corners[[2, 3, 7, 6]], axis=0)
draw_clipped_line_segment(
img,
center_bottom,
center_bottom_forward,
camera_config,
linewidth,
planes,
colors[0][::-1],
)
return img
def uv_coord_is_valid(uv: np.ndarray, img: np.ndarray) -> bool:
"""Check if 2d-point lies within 3-channel color image boundaries"""
h, w, _ = img.shape
return bool(uv[0] >= 0 and uv[1] >= 0 and uv[0] < w and uv[1] < h)
def label_is_closeby(box_point: np.ndarray) -> bool:
"""Check if 3d cuboid pt (in egovehicle frame) is within range from
egovehicle to prevent plot overcrowding.
"""
return bool(np.linalg.norm(box_point) < MAX_RANGE_THRESH_PLOT_CATEGORY)
def draw_alpha_rectangle(
img: np.ndarray,
top_left: Tuple[int, int],
bottom_right: Tuple[int, int],
color_rgb: Tuple[int, int, int],
alpha: float,
) -> np.ndarray:
"""Alpha blend colored rectangle into image. Corner coords given as (x,y) tuples"""
img_h, img_w, _ = img.shape
mask = np.zeros((img_h, img_w), dtype=np.uint8)
mask[top_left[1] : bottom_right[1], top_left[0] : bottom_right[0]] = 1
return vis_mask(img, mask, np.array(list(color_rgb[::-1])), alpha)
def form_obj_label_from_json(label: Dict[str, Any]) -> Tuple[np.ndarray, str]:
"""Construct object from loaded json.
The dictionary loaded from saved json file is expected to have the
following fields::
['frame_index', 'center', 'rotation', 'length', 'width', 'height',
'track_label_uuid', 'occlusion', 'on_driveable_surface', 'key_frame',
'stationary', 'label_class']
Args:
label: Python dictionary that was loaded from saved json file
Returns:
Tuple of (bbox_ego_frame, color); bbox is a numpy array of shape (4,3); color is "g" or "r"
"""
tr_x = label["center"]["x"]
tr_y = label["center"]["y"]
tr_z = label["center"]["z"]
translation = np.array([tr_x, tr_y, tr_z])
rot_w = label["rotation"]["w"]
rot_x = label["rotation"]["x"]
rot_y = label["rotation"]["y"]
rot_z = label["rotation"]["z"]
quaternion = np.array([rot_w, rot_x, rot_y, rot_z])
obj_label_rec = ObjectLabelRecord(
quaternion=quaternion,
translation=translation,
length=label["length"],
width=label["width"],
height=label["height"],
occlusion=label["occlusion"],
)
bbox_ego_frame = obj_label_rec.as_2d_bbox()
if label["occlusion"] == 0:
color = "g"
else:
color = "r"
return bbox_ego_frame, color
def json_label_dict_to_obj_record(label: Dict[str, Any]) -> ObjectLabelRecord:
"""Convert a label dict (from JSON) to an ObjectLabelRecord.
NB: "Shrink-wrapped" objects don't have the occlusion field, but
other other objects do.
Args:
label: Python dictionary with relevant info about a cuboid, loaded from json
Returns:
ObjectLabelRecord object
"""
tr_x = label["center"]["x"]
tr_y = label["center"]["y"]
tr_z = label["center"]["z"]
translation = np.array([tr_x, tr_y, tr_z])
rot_w = label["rotation"]["w"]
rot_x = label["rotation"]["x"]
rot_y = label["rotation"]["y"]
rot_z = label["rotation"]["z"]
quaternion = np.array([rot_w, rot_x, rot_y, rot_z])
length = label["length"]
width = label["width"]
height = label["height"]
if "occlusion" in label:
occlusion = label["occlusion"]
else:
occlusion = 0
if "label_class" in label:
label_class = label["label_class"]
if "name" in label_class:
label_class = label_class["name"]
else:
label_class = None
if "track_label_uuid" in label:
track_id = label["track_label_uuid"]
else:
track_id = None
if "score" in label:
score = label["score"]
else:
score = 1.0
obj_rec = ObjectLabelRecord(
quaternion,
translation,
length,
width,
height,
occlusion,
label_class,
track_id,
score,
)
return obj_rec
def read_label(label_filename: str) -> List[ObjectLabelRecord]:
"""Read label from the json file.
Args:
label_filename: label filename,
Returns:
List of ObjectLabelRecords constructed from the file.
"""
if not os.path.exists(label_filename):
return []
with open(label_filename, "r") as f:
labels = json.load(f)
objects = [json_label_dict_to_obj_record(item) for item in labels]
return objects