-
Notifications
You must be signed in to change notification settings - Fork 3
/
rosbag_to_dataset.py
345 lines (301 loc) · 13.4 KB
/
rosbag_to_dataset.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
from __future__ import print_function
from builtins import input
import numpy as np
import os
import traceback
import rosbag
from cv_bridge import CvBridge
import cv2
from pose2d import Pose2D, apply_tf, inverse_pose2d
import tf_bag
import rospy
from matplotlib import pyplot as plt
from tqdm import tqdm
from pyniel.python_tools.path_tools import make_dir_if_not_exists
from navdreams.auto_debug import enable_auto_debug
enable_auto_debug()
bridge = CvBridge()
# not usable
# bag_path = "~/irl_tests/hg_icra_round2.bag"
# bag_path = "~/rosbags/merged_demo2.bag"
# bag_path = "~/LIANsden/proto_round_rosbags/daniel_manip_spray.bag"
# bag_path = "~/rosbags/HG_rosbags/hg_map.bag"
# usable
# bag_path = "~/rosbags/CLA_rosbags/2019-06-14-10-04-06.bag"
# bag_path = "~/rosbags/CLA_rosbags/2019-06-14-10-13-03.bag"
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-13-03-25.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-11-56-07.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-11-58-01.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-12-01-42.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-12-04-58.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-13-01-00.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-13-08-23.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/Stefan_Kiss_HG_Dataset/onboard/2019-04-05-13-12-11.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/ASL Crowdbot/Rosbags/ASL open lab day/corridor_koze_kids.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/ASL Crowdbot/Rosbags/ASL open lab day/2019-12-13-20-11-46.bag" # noqa
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/rosbags/meet_your_lab1.bag"
# bag_path = "~/Insync/[email protected]/Google Drive - Shared drives/Pepper/rosbags/meet_your_lab2.bag"
# bag_path = "/media/lake/koze_n3d_tests/day1/2022-01-19-14-12-40.bag"
# bag_path = "/media/lake/koze_n3d_tests/day1/2022-01-19-18-39-38.bag"
bag_path = "/media/lake/koze_n3d_tests/day1/2022-01-19-18-50-01.bag"
bag_path = "~/Downloads/2022-02-09-16-09-51_30min_K2.bag"
archive_dir = "~/navdreams_data/wm_experiments/datasets/V/rosbag"
DT = 0.2
# FIXED_FRAME = "odom" # StefanKiss, merged_demo, meet_your_lab
# FIXED_FRAME = "map" # crowdbot CLA
FIXED_FRAME = "reference_map" # open-lab # koze tests
ROBOT_FRAME = "base_footprint"
GOAL_REACHED_DIST = 0.5
MANUALLY_ADDED_GOALS = []
# MANUALLY_ADDED_GOALS = [[-23.2, -24.1]] # meet_your_lab1
# MANUALLY_ADDED_GOALS = [[4.23, -28.5]] # meet_your_lab2
resize_dim = (64, 64)
_W, _H = resize_dim
_CH = 3
odom_topic = '/pepper_robot/odom'
cmd_vel_topic = '/cmd_vel'
image_topic = '/camera/color/image_raw'
cmd_vel_enabled_topic = '/oculus/cmd_vel_enabled'
topics = [cmd_vel_enabled_topic, odom_topic, cmd_vel_topic, image_topic]
# goal_topic = "/move_base_simple/goal"
goal_topic = "/global_planner/goal" # koze tests
print()
print("Required topics:")
print(" " + odom_topic)
print(" " + cmd_vel_topic)
print(" " + image_topic)
print("Optional topics:")
print(" " + goal_topic)
print(" " + cmd_vel_enabled_topic)
print()
bag_path = os.path.expanduser(bag_path)
os.system('rosbag info {} | grep -e {} -e {} -e {} -e {} -e {}'.format(
bag_path.replace(" ", "\ "), odom_topic, cmd_vel_topic, image_topic, goal_topic, cmd_vel_enabled_topic))
input("Are the required topics present?")
# sync concept: pick closest at each dt
# | | | | | | | | odom
# | | | image
# | | | | cmd_vel
# | | | | dt
print("Loading bag...")
bag = rosbag.Bag(bag_path)
print("Initializing Tf Transformer...")
bag_transformer = tf_bag.BagTfTransformer(bag)
start_time = bag.get_start_time()
end_time = bag.get_end_time()
times = np.arange(start_time, end_time, DT)
steps = len(times)
# image
# robotstates [gx, gy, vx, vy, vtheta]
# action = [vx, vy, vtheta]
images = np.ones((steps, _W, _H, _CH)) * np.nan
vels = np.ones((steps, 3)) * np.nan
nextvels = np.zeros_like(vels)
images_eps_min = np.ones((steps,)) * DT / 2.
odom_eps_min = np.ones((steps,)) * DT / 2.
for topic, msg, t in tqdm(bag.read_messages(topics=topics)):
ts = t.to_sec()
floatstep = (ts - start_time) / DT # how many steps have passed since bag start
closest_step = int(np.clip(np.round(floatstep), 0, steps-1))
eps = np.abs(ts - times[closest_step])
if topic == odom_topic:
current_vel = [msg.twist.twist.linear.x, msg.twist.twist.linear.y, msg.twist.twist.angular.z]
if eps < odom_eps_min[closest_step]:
odom_eps_min[closest_step] = eps
vels[closest_step] = current_vel
if topic == image_topic:
if eps < images_eps_min[closest_step]:
images_eps_min[closest_step] = eps
cv_image = bridge.imgmsg_to_cv2(msg, desired_encoding="passthrough")
cv_resized = cv2.resize(cv_image, resize_dim)
images[closest_step] = cv_resized
nextvels[:-1] = vels[1:] # assume vel is action at previous timestep
missing_images = np.any(np.isnan(images.reshape((len(images), -1))), axis=-1)
missing_vels = np.any(np.isnan(vels), axis=-1)
missing_nextvels = np.any(np.isnan(nextvels), axis=-1)
images = images.astype(np.uint8) # save some space!
# apply received goal messages to all future steps
goals_in_fix = np.ones((steps, 2)) * np.nan
goal_changed = np.zeros((steps,))
for topic, msg, t in bag.read_messages(topics=[goal_topic]):
goal_in_msg = np.array([msg.pose.position.x, msg.pose.position.y])
if FIXED_FRAME != msg.header.frame_id:
rospy.logwarn_once("""Warning! Goal frame used in rosbag ({}) != Fixed frame used in this script ({})
Optimally, the fixed frame would be refmap (static). Worse, are gmap (SLAM), or even odom.
Please ensure that the rosbag has the desired frame, and set FIXED_FRAME accordingly.
""".format(msg.header.frame_id, FIXED_FRAME))
try:
p2_msg_in_fix = Pose2D(bag_transformer.lookupTransform(
FIXED_FRAME, msg.header.frame_id, msg.header.stamp))
except: # noqa
traceback.print_exc()
raise ValueError("Could not find goal position in fixed frame! Is fixed frame wrong? \
hint: usually goals / global plans are set in fixed frame")
goal_in_fix = apply_tf(goal_in_msg[None, :], p2_msg_in_fix)[0]
# calculate timestep from which to start applying goal
ts = t.to_sec()
floatstep = (ts - start_time) / DT # how many steps have passed since bag start
closest_step = int(np.clip(np.round(floatstep), 0, steps))
goals_in_fix[closest_step:] = goal_in_fix
goal_changed[closest_step] = 1
# add true robot position information to all steps
robot_in_fix = np.ones((steps, 3)) * np.nan
missing_pos = np.zeros((steps,))
for step, time in enumerate(times):
try:
p2_rob_in_fix = Pose2D(bag_transformer.lookupTransform(
FIXED_FRAME, ROBOT_FRAME, rospy.Time(time)))
except: # noqa
traceback.print_exc()
missing_pos[step] = 1
continue
robot_in_fix[step] = p2_rob_in_fix
if np.any(missing_pos):
plt.figure()
plt.plot(missing_pos)
plt.show()
# find when robot is close to goal
def find_close_to_goal(steps, times, goals_in_fix, robot_in_fix, GOAL_REACHED_DIST):
print("Finding close-to-goal steps")
close_to_goal = np.zeros((steps,))
for step, time in enumerate(times):
if not np.any(np.isnan(goals_in_fix[step])):
close_to_goal[step] = np.linalg.norm(
robot_in_fix[step, :2] - goals_in_fix[step]
) < GOAL_REACHED_DIST
for manual_goal_in_fix in MANUALLY_ADDED_GOALS:
close_to_manual_goal = np.linalg.norm(
robot_in_fix[step, :2] - manual_goal_in_fix
) < GOAL_REACHED_DIST
close_to_goal[step] = close_to_goal[step] or close_to_manual_goal
return close_to_goal
close_to_goal = find_close_to_goal(steps, times, goals_in_fix, robot_in_fix, GOAL_REACHED_DIST)
def cut_sequences(steps,
close_to_goal, goal_changed,
missing_images, missing_vels, missing_nextvels, missing_pos):
print("Cutting sequences")
# cut into sequence of length > 10
# show each sequence in plot
MIN_SEQ_LENGTH = 24
sequence_ids = np.ones((steps,)) * -1
sequence_steps = np.ones((steps,)) * -1
sequence_ends = np.zeros((steps,))
current_sequence_id = 0
current_sequence_length = 0
for step in range(steps):
last_step = step == steps-1
if close_to_goal[step] or goal_changed[step] or last_step or \
missing_images[step] or missing_vels[step] or missing_nextvels[step] or missing_pos[step]:
if current_sequence_length >= MIN_SEQ_LENGTH: # terminate sequence if valid
current_sequence_length = 0
current_sequence_id += 1
sequence_ends[step-1] = 1
else:
sequence_ids[step] = -1
else:
sequence_ids[step] = current_sequence_id
sequence_steps[step] = current_sequence_length
current_sequence_length += 1
n_sequences = current_sequence_id
return sequence_ids, sequence_steps, sequence_ends, n_sequences
sequence_ids, sequence_steps, sequence_ends, n_sequences = cut_sequences(
steps, close_to_goal, goal_changed, missing_images, missing_vels, missing_nextvels, missing_pos)
# fill goals
for seq_id in range(n_sequences):
seq_mask = sequence_ids == seq_id
seq_robot_in_fix = robot_in_fix[seq_mask]
seq_goals_in_fix = goals_in_fix[seq_mask]
if np.any(np.isnan(seq_goals_in_fix)):
print("Sequence {} (len {}): goal missing! Use last point as goal?".format(
seq_id, len(seq_robot_in_fix)))
plt.figure("missing goal")
plt.title("Goal missing")
plt.plot(seq_robot_in_fix[:, 0], seq_robot_in_fix[:, 1])
plt.show()
plt.close('all')
if True:
print("using last point as goal.")
goals_in_fix[seq_mask] = seq_robot_in_fix[-1, :2]
# re-calculate close-to-goal, re-cut sequences
close_to_goal = find_close_to_goal(steps, times, goals_in_fix, robot_in_fix, GOAL_REACHED_DIST)
sequence_ids, sequence_steps, sequence_ends, n_sequences = cut_sequences(
steps, close_to_goal, goal_changed, missing_images, missing_vels, missing_nextvels, missing_pos)
# infer goal_in_robot
goals_in_robot = np.zeros_like(goals_in_fix)
for step in range(steps):
if np.any(np.isnan(goals_in_fix[step])):
continue
p2_fix_in_robot = inverse_pose2d(robot_in_fix[step])
goal_in_robot = apply_tf(goals_in_fix[step][None, :], p2_fix_in_robot)[0]
goals_in_robot[step] = goal_in_robot
# plot sequences
plt.figure("sequences")
plt.title("Sequences")
legends = []
for seq_id in range(n_sequences):
seq_mask = sequence_ids == seq_id
seq_robot_in_fix = robot_in_fix[seq_mask]
seq_goals_in_fix = goals_in_fix[seq_mask]
seq_nextvels = nextvels[seq_mask]
legends.append(str(seq_id))
plt.plot(seq_robot_in_fix[:, 0], seq_robot_in_fix[:, 1])
plt.legend(legends)
plt.show()
# transform to robotstates
scans = np.ones((steps, _W, _H, _CH)) * np.nan
robotstates = np.ones((steps, 5)) * np.nan
actions = np.ones((steps, 3)) * np.nan
dones = np.zeros((steps,))
scans = images
robotstates[:, 0] = goals_in_robot[:, 0]
robotstates[:, 1] = goals_in_robot[:, 1]
robotstates[:, 2] = vels[:, 0]
robotstates[:, 3] = vels[:, 1]
robotstates[:, 4] = vels[:, 2]
actions = nextvels
dones = sequence_ends
# remove any data outside of valid sequence
valid_mask = sequence_ids != -1
scans = scans[valid_mask]
robotstates = robotstates[valid_mask]
actions = actions[valid_mask]
dones = dones[valid_mask]
rewards = np.zeros_like(dones)
print("Found {} sequences, {} total steps".format(n_sequences, len(robotstates)))
# plot all sequence-ending conditions
plt.figure()
legends = []
for i, varname in enumerate([
"missing_images", "missing_vels", "missing_pos",
"missing_nextvels", "close_to_goal", "goal_changed"]):
series = locals().get(varname)
legends.append(varname)
# slightly separate our plotted lines for clarity
plt.plot(series + i * 0.02)
plt.legend(legends)
plt.show()
# show evenly spaced images
N = 10
thumbnails = scans[::len(scans) // N]
fig, axes = plt.subplots(1, N)
for i, ax in enumerate(axes):
ax.imshow(thumbnails[i].astype(np.uint8))
plt.show()
if np.any(np.isnan(scans)):
raise ValueError
if np.any(np.isnan(robotstates)):
raise ValueError
if np.any(np.isnan(actions)):
raise ValueError
if np.any(np.isnan(rewards)):
raise ValueError
# write
if n_sequences > 0:
bag_name = os.path.splitext(os.path.basename(bag_path))[0].replace('_', '')
archive_dir = os.path.expanduser(archive_dir)
make_dir_if_not_exists(archive_dir)
archive_path = os.path.join(archive_dir,
"{}_scans_robotstates_actions_rewards_dones.npz".format(bag_name))
np.savez_compressed(archive_path,
scans=scans, robotstates=robotstates, actions=actions, rewards=rewards, dones=dones)
print("Saved to {}".format(archive_path))