Un nouveau rapport suggère que la Corée du Sud est le premier pays à avoir remplacé 10 % de sa main-d'œuvre dans les usines par des robots. La Corée du Sud compte aujourd'hui deux fois plus de robots dans ses usines que n'importe quel autre pays du monde, d’après des chiffres d’une récente enquête. Le tableau ravive les questionnements sur la pertinence d’opter pour des robots dans certains cas. La Corée du Sud compte désormais 1102 robots pour 10 000 salariés, ce qui fait de ce pays le numéro un mondial de l'utilisation de la technologie en lieu et place de la main-d'œuvre humaine pour accomplir des tâches. Seul Singapour se rapproche de la Corée du Sud en ce qui concerne les robots, avec 770 robots pour 10 000 travailleurs.
La Chine est de loin le plus grand marché du monde, avec 276 288 robots installés en 2023, soit 51 % des installations mondiales. Le Japon reste le deuxième marché pour les robots, avec 46 106 unités installées en 2023. L'Inde, un marché émergent, a également connu une croissance rapide des installations de robots, le taux augmentant de 59 % d'une année sur l'autre pour atteindre 8 510 unités en 2023.Robots are coming globally, is your job replaceable? pic.twitter.com/JLAVpLuUon
— Robinson M רו🚀🦿🦸 (@RobinsonMuiru) December 1, 2024
Les usines et les zones à risque pour les humains comptent parmi les cas les plus pertinents de mise à contribution des robots🌏Visualized: Countries with the most Industrial Robots (per 10000 workers🦾)
— Supply Chain Logistics Consulting (@PRO_SupplyChain) December 3, 2024
🇰🇷South Korea 1012
🇸🇬Singapore 730
🇩🇪Germany 415
🇯🇵Japan 397
🇨🇳China 392
🌐 Global Average: 151#supplychain #logistics #RBA #robotics pic.twitter.com/n9MvCnqErj
Les robots sont devenus des alliés précieux dans la gestion des sites nucléaires contaminés. A Fukushima Daiichi au Japon, la société Tokyo Electric Power Company Holdings (TEPCO), exploitante de la centrale nucléaire, met à contribution un bras robotique pour des tests de ramassage des débris de combustible nucléaire fondu. Ce cas d’utilisation d’un robot en lieu et place des humains est parmi les plus pertinents compte tenu de la dangerosité des émissions radioactives.
Les chiens robots font aussi l’objet de mise à contribution dans le cas des centrales nucléaires pour les besoins de mesure de niveaux de radioactivité. Ce type de mise en œuvre desdits robots consiste en général en de la détection et suivi d’objets. Dans ce cas, il y a collecte des images provenant de deux caméras avant et détection d’objet sur une classe spécifiée. Cette détection utilise Tensorflow via le tensorflow_object_detector. Il accepte n'importe quel modèle Tensorflow et permet au développeur de spécifier un sous-ensemble de classes de détection incluses dans le modèle. Il effectue cet ensemble d'opérations pour un nombre prédéfini d'itérations, en bloquant pendant une durée prédéfinie entre chaque itération. L'application détermine ensuite l'emplacement de la détection la plus fiable de la classe spécifiée et se dirige vers l'objet.
L’application est organisée en trois ensembles de processus Python communiquant avec le robot Spot. Le diagramme des processus est illustré ci-dessous. Le processus principal communique avec le robot Spot via GRPC et reçoit constamment des images. Ces images sont poussées dans la RAW_IMAGES_QUEUE et lues par les processus Tensorflow. Ces processus détectent des objets dans les images et poussent l'emplacement dans PROCESSED_BOXES_QUEUE. Le thread principal détermine alors l'emplacement de l'objet et envoie des commandes au robot pour qu'il se dirige vers l'objet.
| Code : | Sélectionner tout |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 | Copyright (c) 2023 Boston Dynamics, Inc. All rights reserved.
#
# Downloading, reproducing, distributing or otherwise using the SDK Software
# is subject to the terms and conditions of the Boston Dynamics Software
# Development Kit License (20191101-BDSDK-SL).
"""Tutorial to show how to use the Boston Dynamics API to detect and follow an object"""
import argparse
import io
import json
import math
import os
import signal
import sys
import time
from multiprocessing import Barrier, Process, Queue, Value
from queue import Empty, Full
from threading import BrokenBarrierError, Thread
import cv2
import numpy as np
from PIL import Image
from scipy import ndimage
from tensorflow_object_detection import DetectorAPI
import bosdyn.client
import bosdyn.client.util
from bosdyn import geometry
from bosdyn.api import geometry_pb2 as geo
from bosdyn.api import image_pb2, trajectory_pb2
from bosdyn.api.image_pb2 import ImageSource
from bosdyn.api.spot import robot_command_pb2 as spot_command_pb2
from bosdyn.client.async_tasks import AsyncPeriodicQuery, AsyncTasks
from bosdyn.client.frame_helpers import (GROUND_PLANE_FRAME_NAME, VISION_FRAME_NAME, get_a_tform_b,
get_vision_tform_body)
from bosdyn.client.image import ImageClient
from bosdyn.client.lease import LeaseClient, LeaseKeepAlive
from bosdyn.client.math_helpers import Quat, SE3Pose
from bosdyn.client.robot_command import (CommandFailedError, CommandTimedOutError,
RobotCommandBuilder, RobotCommandClient, blocking_stand)
from bosdyn.client.robot_state import RobotStateClient
LOGGER = bosdyn.client.util.get_logger()
SHUTDOWN_FLAG = Value('i', 0)
# Don't let the queues get too backed up
QUEUE_MAXSIZE = 10
# This is a multiprocessing.Queue for communication between the main process and the
# Tensorflow processes.
# Entries in this queue are in the format:
# {
# 'source': Name of the camera,
# 'world_tform_cam': transform from VO to camera,
# 'world_tform_gpe': transform from VO to ground plane,
# 'raw_image_time': Time when the image was collected,
# 'cv_image': The decoded image,
# 'visual_dims': (cols, rows),
# 'depth_image': depth image proto,
# 'system_cap_time': Time when the image was received by the main process,
# 'image_queued_time': Time when the image was done preprocessing and queued
# }
RAW_IMAGES_QUEUE = Queue(QUEUE_MAXSIZE)
# This is a multiprocessing.Queue for communication between the Tensorflow processes and
# the bbox print process. This is meant for running in a containerized environment with no access
# to an X display
# Entries in this queue have the following fields in addition to those in :
# {
# 'processed_image_start_time': Time when the image was received by the TF process,
# 'processed_image_end_time': Time when the image was processing for bounding boxes
# 'boxes': list of detected bounding boxes for the processed image
# 'classes': classes of objects,
# 'scores': confidence scores,
# }
PROCESSED_BOXES_QUEUE = Queue(QUEUE_MAXSIZE)
# Barrier for waiting on Tensorflow processes to start, initialized in main()
TENSORFLOW_PROCESS_BARRIER = None
COCO_CLASS_DICT = {
1: 'person',
2: 'bicycle',
3: 'car',
4: 'motorcycle',
5: 'airplane',
6: 'bus',
7: 'train',
8: 'truck',
9: 'boat',
10: 'trafficlight',
11: 'firehydrant',
13: 'stopsign',
14: 'parkingmeter',
15: 'bench',
16: 'bird',
17: 'cat',
18: 'dog',
19: 'horse',
20: 'sheep',
21: 'cow',
22: 'elephant',
23: 'bear',
24: 'zebra',
25: 'giraffe',
27: 'backpack',
28: 'umbrella',
31: 'handbag',
32: 'tie',
33: 'suitcase',
34: 'frisbee',
35: 'skis',
36: 'snowboard',
37: 'sportsball',
38: 'kite',
39: 'baseballbat',
40: 'baseballglove',
41: 'skateboard',
42: 'surfboard',
43: 'tennisracket',
44: 'bottle',
46: 'wineglass',
47: 'cup',
48: 'fork',
49: 'knife',
50: 'spoon',
51: 'bowl',
52: 'banana',
53: 'apple',
54: 'sandwich',
55: 'orange',
56: 'broccoli',
57: 'carrot',
58: 'hotdog',
59: 'pizza',
60: 'donut',
61: 'cake',
62: 'chair',
63: 'couch',
64: 'pottedplant',
65: 'bed',
67: 'diningtable',
70: 'toilet',
72: 'tv',
73: 'laptop',
74: 'mouse',
75: 'remote',
76: 'keyboard',
77: 'cellphone',
78: 'microwave',
79: 'oven',
80: 'toaster',
81: 'sink',
82: 'refrigerator',
84: 'book',
85: 'clock',
86: 'vase',
87: 'scissors',
88: 'teddybear',
89: 'hairdrier',
90: 'toothbrush'
}
# Mapping from visual to depth data
VISUAL_SOURCE_TO_DEPTH_MAP_SOURCE = {
'frontleft_fisheye_image': 'frontleft_depth_in_visual_frame',
'frontright_fisheye_image': 'frontright_depth_in_visual_frame'
}
ROTATION_ANGLES = {
'back_fisheye_image': 0,
'frontleft_fisheye_image': -78,
'frontright_fisheye_image': -102,
'left_fisheye_image': 0,
'right_fisheye_image': 180
}
def _update_thread(async_task):
while True:
async_task.update()
time.sleep(0.01)
class AsyncImage(AsyncPeriodicQuery):
"""Grab image."""
def __init__(self, image_client, image_sources):
# Period is set to be about 15 FPS
super(AsyncImage, self).__init__('images', image_client, LOGGER, period_sec=0.067)
self.image_sources = image_sources
def _start_query(self):
return self._client.get_image_from_sources_async(self.image_sources)
class AsyncRobotState(AsyncPeriodicQuery):
"""Grab robot state."""
def __init__(self, robot_state_client):
# period is set to be about the same rate as detections on the CORE AI
super(AsyncRobotState, self).__init__('robot_state', robot_state_client, LOGGER,
period_sec=0.02)
def _start_query(self):
return self._client.get_robot_state_async()
def get_source_list(image_client):
"""Gets a list of image sources and filters based on config dictionary
Args:
image_client: Instantiated image client
"""
# We are using only the visual images with their corresponding depth sensors
sources = image_client.list_image_sources()
source_list = []
for source in sources:
if source.image_type == ImageSource.IMAGE_TYPE_VISUAL:
# only append if sensor has corresponding depth sensor
if source.name in VISUAL_SOURCE_TO_DEPTH_MAP_SOURCE:
source_list.append(source.name)
source_list.append(VISUAL_SOURCE_TO_DEPTH_MAP_SOURCE[source.name])
return source_list
def capture_images(image_task, sleep_between_capture):
""" Captures images and places them on the queue
Args:
image_task (AsyncImage): Async task that provides the images response to use
sleep_between_capture (float): Time to sleep between each image capture
"""
while not SHUTDOWN_FLAG.value:
get_im_resp = image_task.proto
start_time = time.time()
if not get_im_resp:
continue
depth_responses = {
img.source.name: img
for img in get_im_resp
if img.source.image_type == ImageSource.IMAGE_TYPE_DEPTH
}
entry = {}
for im_resp in get_im_resp:
if im_resp.source.image_type == ImageSource.IMAGE_TYPE_VISUAL:
source = im_resp.source.name
depth_source = VISUAL_SOURCE_TO_DEPTH_MAP_SOURCE[source]
depth_image = depth_responses[depth_source]
acquisition_time = im_resp.shot.acquisition_time
image_time = acquisition_time.seconds + acquisition_time.nanos * 1e-9
try:
image = Image.open(io.BytesIO(im_resp.shot.image.data))
source = im_resp.source.name
image = ndimage.rotate(image, ROTATION_ANGLES[source])
if im_resp.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_GREYSCALE_U8:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) # Converted to RGB for TF
tform_snapshot = im_resp.shot.transforms_snapshot
frame_name = im_resp.shot.frame_name_image_sensor
world_tform_cam = get_a_tform_b(tform_snapshot, VISION_FRAME_NAME, frame_name)
world_tform_gpe = get_a_tform_b(tform_snapshot, VISION_FRAME_NAME,
GROUND_PLANE_FRAME_NAME)
entry[source] = {
'source': source,
'world_tform_cam': world_tform_cam,
'world_tform_gpe': world_tform_gpe,
'raw_image_time': image_time,
'cv_image': image,
'visual_dims': (im_resp.shot.image.cols, im_resp.shot.image.rows),
'depth_image': depth_image,
'system_cap_time': start_time,
'image_queued_time': time.time()
}
except Exception as exc: # pylint: disable=broad-except
print(f'Exception occurred during image capture {exc}')
try:
RAW_IMAGES_QUEUE.put_nowait(entry)
except Full as exc:
print(f'RAW_IMAGES_QUEUE is full: {exc}')
time.sleep(sleep_between_capture)
def start_tensorflow_processes(num_processes, model_path, detection_class, detection_threshold,
max_processing_delay):
"""Starts Tensorflow processes in parallel.
It does not keep track of the processes once they are started because they run indefinitely
and are never joined back to the main process.
Args:
num_processes (int): Number of Tensorflow processes to start in parallel.
model_path (str): Filepath to the Tensorflow model to use.
detection_class (int): Detection class to detect
detection_threshold (float): Detection threshold to apply to all Tensorflow detections.
max_processing_delay (float): Allowed delay before processing an incoming image.
"""
processes = []
for _ in range(num_processes):
process = Process(
target=process_images, args=(
model_path,
detection_class,
detection_threshold,
max_processing_delay,
), daemon=True)
process.start()
processes.append(process)
return processes
def process_images(model_path, detection_class, detection_threshold, max_processing_delay):
"""Starts Tensorflow and detects objects in the incoming images.
Args:
model_path (str): Filepath to the Tensorflow model to use.
detection_class (int): Detection class to detect
detection_threshold (float): Detection threshold to apply to all Tensorflow detections.
max_processing_delay (float): Allowed delay before processing an incoming image.
"""
odapi = DetectorAPI(path_to_ckpt=model_path)
num_processed_skips = 0
if TENSORFLOW_PROCESS_BARRIER is None:
return
try:
TENSORFLOW_PROCESS_BARRIER.wait()
except BrokenBarrierError as exc:
print(f'Error waiting for Tensorflow processes to initialize: {exc}')
return False
while not SHUTDOWN_FLAG.value:
try:
entry = RAW_IMAGES_QUEUE.get_nowait()
except Empty:
time.sleep(0.1)
continue
for _, capture in entry.items():
start_time = time.time()
processing_delay = time.time() - capture['raw_image_time']
if processing_delay > max_processing_delay:
num_processed_skips += 1
print(f'skipped image because it took {processing_delay}')
continue # Skip image due to delay
image = capture['cv_image']
boxes, scores, classes, _ = odapi.process_frame(image)
confident_boxes = []
confident_object_classes = []
confident_scores = []
if len(boxes) == 0:
print('no detections founds')
continue
for box, score, box_class in sorted(zip(boxes, scores, classes), key=lambda x: x[1],
reverse=True):
if score > detection_threshold and box_class == detection_class:
confident_boxes.append(box)
confident_object_classes.append(COCO_CLASS_DICT[box_class])
confident_scores.append(score)
image = cv2.rectangle(image, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 2)
capture['processed_image_start_time'] = start_time
capture['processed_image_end_time'] = time.time()
capture['boxes'] = confident_boxes
capture['classes'] = confident_object_classes
capture['scores'] = confident_scores
capture['cv_image'] = image
try:
PROCESSED_BOXES_QUEUE.put_nowait(entry)
except Full as exc:
print(f'PROCESSED_BOXES_QUEUE is full: {exc}')
print('tf process ending')
return True
def get_go_to(world_tform_object, robot_state, mobility_params, dist_margin=0.5):
"""Gets trajectory command to a goal location
Args:
world_tform_object (SE3Pose): Transform from vision frame to target object
robot_state (RobotState): Current robot state
mobility_params (MobilityParams): Mobility parameters
dist_margin (float): Distance margin to target
"""
vo_tform_robot = get_vision_tform_body(robot_state.kinematic_state.transforms_snapshot)
print(f'robot pos: {vo_tform_robot}')
delta_ewrt_vo = np.array(
[world_tform_object.x - vo_tform_robot.x, world_tform_object.y - vo_tform_robot.y, 0])
norm = np.linalg.norm(delta_ewrt_vo)
if norm == 0:
return None
delta_ewrt_vo_norm = delta_ewrt_vo / norm
heading = _get_heading(delta_ewrt_vo_norm)
vo_tform_goal = np.array([
world_tform_object.x - delta_ewrt_vo_norm[0] * dist_margin,
world_tform_object.y - delta_ewrt_vo_norm[1] * dist_margin
])
se2_pose = geo.SE2Pose(position=geo.Vec2(x=vo_tform_goal[0], y=vo_tform_goal[1]), angle=heading)
tag_cmd = RobotCommandBuilder.synchro_se2_trajectory_command(se2_pose,
frame_name=VISION_FRAME_NAME,
params=mobility_params)
return tag_cmd
def _get_heading(xhat):
zhat = [0.0, 0.0, 1.0]
yhat = np.cross(zhat, xhat)
mat = np.array([xhat, yhat, zhat]).transpose()
return Quat.from_matrix(mat).to_yaw()
def set_default_body_control():
"""Set default body control params to current body position"""
footprint_R_body = geometry.EulerZXY()
position = geo.Vec3(x=0.0, y=0.0, z=0.0)
rotation = footprint_R_body.to_quaternion()
pose = geo.SE3Pose(position=position, rotation=rotation)
point = trajectory_pb2.SE3TrajectoryPoint(pose=pose)
traj = trajectory_pb2.SE3Trajectory(points=[point])
return spot_command_pb2.BodyControlParams(base_offset_rt_footprint=traj)
def get_mobility_params():
"""Gets mobility parameters for following"""
vel_desired = .75
speed_limit = geo.SE2VelocityLimit(
max_vel=geo.SE2Velocity(linear=geo.Vec2(x=vel_desired, y=vel_desired), angular=.25))
body_control = set_default_body_control()
mobility_params = spot_command_pb2.MobilityParams(vel_limit=speed_limit, obstacle_params=None,
body_control=body_control,
locomotion_hint=spot_command_pb2.HINT_TROT)
return mobility_params
def depth_to_xyz(depth, pixel_x, pixel_y, focal_length, principal_point):
"""Calculate the transform to point in image using camera intrinsics and depth"""
x = depth * (pixel_x - principal_point.x) / focal_length.x
y = depth * (pixel_y - principal_point.y) / focal_length.y
z = depth
return x, y, z
def remove_ground_from_depth_image(raw_depth_image, focal_length, principal_point, world_tform_cam,
world_tform_gpe, ground_tolerance=0.04):
""" Simple ground plane removal algorithm. Uses ground height
and does simple z distance filtering.
Args:
raw_depth_image (np.array): Depth image
focal_length (Vec2): Focal length of camera that produced the depth image
principal_point (Vec2): Principal point of camera that produced the depth image
world_tform_cam (SE3Pose): Transform from VO to camera frame
world_tform_gpe (SE3Pose): Transform from VO to GPE frame
ground_tolerance (float): Distance in meters to add to the ground plane
"""
new_depth_image = raw_depth_image
# same functions as depth_to_xyz, but converted to np functions
indices = np.indices(raw_depth_image.shape)
xs = raw_depth_image * (indices[1] - principal_point.x) / focal_length.x
ys = raw_depth_image * (indices[0] - principal_point.y) / focal_length.y
zs = raw_depth_image
# create xyz point cloud
camera_tform_points = np.stack([xs, ys, zs], axis=2)
# points in VO frame
world_tform_points = world_tform_cam.transform_cloud(camera_tform_points)
# array of booleans where True means the point was below the ground plane plus tolerance
world_tform_points_mask = (world_tform_gpe.z - world_tform_points[:, :, 2]) < ground_tolerance
# remove data below ground plane
new_depth_image[world_tform_points_mask] = 0
return new_depth_image
def get_distance_to_closest_object_depth(x_min, x_max, y_min, y_max, depth_scale, raw_depth_image,
histogram_bin_size=0.50, minimum_number_of_points=10,
max_distance=8.0):
"""Make a histogram of distances to points in the cloud and take the closest distance with
enough points.
Args:
x_min (int): minimum x coordinate (column) of object to find
x_max (int): maximum x coordinate (column) of object to find
y_min (int): minimum y coordinate (row) of object to find
y_max (int): maximum y coordinate (row) of object to find
depth_scale (float): depth scale of the image to convert from sensor value to meters
raw_depth_image (np.array): matrix of depth pixels
histogram_bin_size (float): size of each bin of distances
minimum_number_of_points (int): minimum number of points before returning depth
max_distance (float): maximum distance to object in meters
"""
num_bins = math.ceil(max_distance / histogram_bin_size)
# get a sub-rectangle of the bounding box out of the whole image, then flatten
obj_depths = (raw_depth_image[y_min:y_max, x_min:x_max]).flatten()
obj_depths = obj_depths / depth_scale
obj_depths = obj_depths[obj_depths != 0]
hist, hist_edges = np.histogram(obj_depths, bins=num_bins, range=(0, max_distance))
edges_zipped = zip(hist_edges[:-1], hist_edges[1:])
# Iterate over the histogram and return the first distance with enough points.
for entry, edges in zip(hist, edges_zipped):
if entry > minimum_number_of_points:
filtered_depths = obj_depths[(obj_depths > edges[0]) & (obj_depths < edges[1])]
if len(filtered_depths) == 0:
continue
return np.mean(filtered_depths)
return max_distance
def rotate_about_origin_degrees(origin, point, angle):
"""
Rotate a point counterclockwise by a given angle around a given origin.
Args:
origin (tuple): Origin to rotate the point around
point (tuple): Point to rotate
angle (float): Angle in degrees
"""
return rotate_about_origin(origin, point, math.radians(angle))
def rotate_about_origin(origin, point, angle):
"""
Rotate a point counterclockwise by a given angle around a given origin.
Args:
origin (tuple): Origin to rotate the point around
point (tuple): Point to rotate
angle (float): Angle in radians
"""
orig_x, orig_y = origin
pnt_x, pnt_y = point
ret_x = orig_x + math.cos(angle) * (pnt_x - orig_x) - math.sin(angle) * (pnt_y - orig_y)
ret_y = orig_y + math.sin(angle) * (pnt_x - orig_x) + math.cos(angle) * (pnt_y - orig_y)
return int(ret_x), int(ret_y)
def get_object_position(world_tform_cam, world_tform_gpe, visual_dims, depth_image, bounding_box,
rotation_angle):
"""
Extract the bounding box, then find the mode in that region.
Args:
world_tform_cam (SE3Pose): SE3 transform from world to camera frame
visual_dims (Tuple): (cols, rows) tuple from the visual image
depth_image (ImageResponse): From a depth camera corresponding to the visual_image
bounding_box (list): Bounding box from tensorflow
rotation_angle (float): Angle (in degrees) to rotate depth image to match cam image rotation
"""
# Make sure there are two images.
if visual_dims is None or depth_image is None:
# Fail.
return
# Rotate bounding box back to original frame
points = [(bounding_box[1], bounding_box[0]), (bounding_box[3], bounding_box[0]),
(bounding_box[3], bounding_box[2]), (bounding_box[1], bounding_box[2])]
origin = (visual_dims[0] / 2, visual_dims[1] / 2)
points_rot = [rotate_about_origin_degrees(origin, point, rotation_angle) for point in points]
# Get the bounding box corners.
y_min = max(0, min([point[1] for point in points_rot]))
x_min = max(0, min([point[0] for point in points_rot]))
y_max = min(visual_dims[1], max([point[1] for point in points_rot]))
x_max = min(visual_dims[0], max([point[0] for point in points_rot]))
# Check that the bounding box is valid.
if (x_min < 0 or y_min < 0 or x_max > visual_dims[0] or y_max > visual_dims[1]):
print(f'Bounding box is invalid: ({x_min}, {y_min}) | ({x_max}, {y_max})')
print(f'Bounds: ({visual_dims[0]}, {visual_dims[1]})')
return
# Unpack the images.
try:
if depth_image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_DEPTH_U16:
dtype = np.uint16
else:
dtype = np.uint8
img = np.fromstring(depth_image.shot.image.data, dtype=dtype)
if depth_image.shot.image.format == image_pb2.Image.FORMAT_RAW:
img = img.reshape(depth_image.shot.image.rows, depth_image.shot.image.cols)
else:
img = cv2.imdecode(img, -1)
depth_image_pixels = img
depth_image_pixels = remove_ground_from_depth_image(
depth_image_pixels, depth_image.source.pinhole.intrinsics.focal_length,
depth_image.source.pinhole.intrinsics.principal_point, world_tform_cam, world_tform_gpe)
# Get the depth data from the region in the bounding box.
max_distance = 8.0
depth = get_distance_to_closest_object_depth(x_min, x_max, y_min, y_max,
depth_image.source.depth_scale,
depth_image_pixels, max_distance=max_distance)
if depth >= max_distance:
# Not enough depth data.
print('Not enough depth data.')
return False
else:
print(f'distance to object: {depth}')
center_x = round((x_max - x_min) / 2.0 + x_min)
center_y = round((y_max - y_min) / 2.0 + y_min)
tform_x, tform_y, tform_z = depth_to_xyz(
depth, center_x, center_y, depth_image.source.pinhole.intrinsics.focal_length,
depth_image.source.pinhole.intrinsics.principal_point)
camera_tform_obj = SE3Pose(tform_x, tform_y, tform_z, Quat())
return world_tform_cam * camera_tform_obj
except Exception as exc: # pylint: disable=broad-except
print(f'Error getting object position: {exc}')
return
def _check_model_path(model_path):
if model_path is None or \
not os.path.exists(model_path) or \
not os.path.isfile(model_path):
print(f'ERROR, could not find model file {model_path}')
return False
return True
def _check_and_load_json_classes(config_path):
if os.path.isfile(config_path):
with open(config_path) as json_classes:
global COCO_CLASS_DICT # pylint: disable=global-statement
COCO_CLASS_DICT = json.load(json_classes)
def _find_highest_conf_source(processed_boxes_entry):
highest_conf_source = None
max_score = 0
for key, capture in processed_boxes_entry.items():
if 'scores' in capture.keys():
if len(capture['scores']) > 0 and capture['scores'][0] > max_score:
highest_conf_source = key
max_score = capture['scores'][0]
return highest_conf_source
def signal_handler(signal, frame):
print('Interrupt caught, shutting down')
SHUTDOWN_FLAG.value = 1
def main():
"""Command line interface."""
parser = argparse.ArgumentParser()
parser.add_argument(
'--model-path', default='/model.pb', help=
('Local file path to the Tensorflow model, example pre-trained models can be found at '
'https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md'
))
parser.add_argument('--classes', default='/classes.json', type=str,
help='File containing json mapping of object class IDs to class names')
parser.add_argument('--number-tensorflow-processes', default=1, type=int,
help='Number of Tensorflow processes to run in parallel')
parser.add_argument('--detection-threshold', default=0.7, type=float,
help='Detection threshold to use for Tensorflow detections')
parser.add_argument(
'--sleep-between-capture', default=0.2, type=float,
help=('Seconds to sleep between each image capture loop iteration, which captures '
'an image from all cameras'))
parser.add_argument(
'--detection-class', default=1, type=int,
help=('Detection classes to use in the Tensorflow model.'
'Default is to use 1, which is a person in the Coco dataset'))
parser.add_argument(
'--max-processing-delay', default=7.0, type=float,
help=('Maximum allowed delay for processing an image. '
'Any image older than this value will be skipped'))
parser.add_argument('--test-mode', action='store_true',
help='Run application in test mode, don\'t execute commands')
bosdyn.client.util.add_base_arguments(parser)
bosdyn.client.util.add_payload_credentials_arguments(parser)
options = parser.parse_args()
signal.signal(signal.SIGINT, signal_handler)
try:
# Make sure the model path is a valid file
if not _check_model_path(options.model_path):
return False
# Check for classes json file, otherwise use the COCO class dictionary
_check_and_load_json_classes(options.classes)
global TENSORFLOW_PROCESS_BARRIER # pylint: disable=global-statement
TENSORFLOW_PROCESS_BARRIER = Barrier(options.number_tensorflow_processes + 1)
# Start Tensorflow processes
tf_processes = start_tensorflow_processes(options.number_tensorflow_processes,
options.model_path, options.detection_class,
options.detection_threshold,
options.max_processing_delay)
# sleep to give the Tensorflow processes time to initialize
try:
TENSORFLOW_PROCESS_BARRIER.wait()
except BrokenBarrierError as exc:
print(f'Error waiting for Tensorflow processes to initialize: {exc}')
return False
# Start the API related things
# Create robot object with a world object client
sdk = bosdyn.client.create_standard_sdk('SpotFollowClient')
robot = sdk.create_robot(options.hostname)
if options.payload_credentials_file:
robot.authenticate_from_payload_credentials(
*bosdyn.client.util.get_guid_and_secret(options))
else:
bosdyn.client.util.authenticate(robot)
# Time sync is necessary so that time-based filter requests can be converted
robot.time_sync.wait_for_sync()
# Verify the robot is not estopped and that an external application has registered and holds
# an estop endpoint.
assert not robot.is_estopped(), 'Robot is estopped. Please use an external E-Stop client,' \
' such as the estop SDK example, to configure E-Stop.'
# Create the sdk clients
robot_state_client = robot.ensure_client(RobotStateClient.default_service_name)
robot_command_client = robot.ensure_client(RobotCommandClient.default_service_name)
lease_client = robot.ensure_client(LeaseClient.default_service_name)
image_client = robot.ensure_client(ImageClient.default_service_name)
source_list = get_source_list(image_client)
image_task = AsyncImage(image_client, source_list)
robot_state_task = AsyncRobotState(robot_state_client)
task_list = [image_task, robot_state_task]
_async_tasks = AsyncTasks(task_list)
print('Detect and follow client connected.')
lease = lease_client.take()
lease_keep = LeaseKeepAlive(lease_client)
# Power on the robot and stand it up
resp = robot.power_on()
try:
blocking_stand(robot_command_client)
except CommandFailedError as exc:
print(f'Error ({exc}) occurred while trying to stand. Check robot surroundings.')
return False
except CommandTimedOutError as exc:
print(f'Stand command timed out: {exc}')
return False
print('Robot powered on and standing.')
params_set = get_mobility_params()
# This thread starts the async tasks for image and robot state retrieval
update_thread = Thread(target=_update_thread, args=[_async_tasks])
update_thread.daemon = True
update_thread.start()
# Wait for the first responses.
while any(task.proto is None for task in task_list):
time.sleep(0.1)
# Start image capture process
image_capture_thread = Process(target=capture_images,
args=(image_task, options.sleep_between_capture),
daemon=True)
image_capture_thread.start()
while not SHUTDOWN_FLAG.value:
# This comes from the tensorflow processes and limits the rate of this loop
try:
entry = PROCESSED_BOXES_QUEUE.get_nowait()
except Empty:
continue
# find the highest confidence bounding box
highest_conf_source = _find_highest_conf_source(entry)
if highest_conf_source is None:
# no boxes or scores found
continue
capture_to_use = entry[highest_conf_source]
raw_time = capture_to_use['raw_image_time']
time_gap = time.time() - raw_time
if time_gap > options.max_processing_delay:
continue # Skip image due to delay
# Find the transform to the highest confidence object using the depth sensor
get_object_position_start = time.time()
robot_state = robot_state_task.proto
world_tform_gpe = get_a_tform_b(robot_state.kinematic_state.transforms_snapshot,
VISION_FRAME_NAME, GROUND_PLANE_FRAME_NAME)
world_tform_object = get_object_position(
capture_to_use['world_tform_cam'], world_tform_gpe, capture_to_use['visual_dims'],
capture_to_use['depth_image'], capture_to_use['boxes'][0],
ROTATION_ANGLES[capture_to_use['source']])
get_object_position_end = time.time()
print(f'system_cap_time: {capture_to_use["system_cap_time"]}, '
f'image_queued_time: {capture_to_use["image_queued_time"]}, '
f'processed_image_start_time: {capture_to_use["processed_image_start_time"]}, '
f'processed_image_end_time: {capture_to_use["processed_image_end_time"]}, '
f'get_object_position_start_time: {get_object_position_start}, '
f'get_object_position_end_time: {get_object_position_end}, ')
# get_object_position can fail if there is insufficient depth sensor information
if not world_tform_object:
continue
scores = capture_to_use['scores']
print(f'Position of object with confidence {scores[0]}: {world_tform_object}')
print(f'Process latency: {time.time() - capture_to_use["system_cap_time"]}')
tag_cmd = get_go_to(world_tform_object, robot_state, params_set)
end_time = 15.0
if tag_cmd is not None:
if not options.test_mode:
print('executing command')
robot_command_client.robot_command(lease=None, command=tag_cmd,
end_time_secs=time.time() + end_time)
else:
print('Running in test mode, skipping command.')
# Shutdown lease keep-alive and return lease gracefully.
lease_keep.shutdown()
lease_client.return_lease(lease)
return True
except Exception as exc: # pylint: disable=broad-except
LOGGER.error('Spot Tensorflow Detector threw an exception: %s', exc)
# Shutdown lease keep-alive and return lease gracefully.
return False
if __name__ == '__main__':
if not main():
sys.exit(1) |
Ce sont les tentatives d’utilisation des robots dans des filières comme celles des relations amoureuses qui soulèvent le plus de débats contradictoires
À Shenzhen, Starpery Technology, un important producteur de poupées sexuelles, est dans le processus de formation de ses produits grâce à l’intelligence artificielle. Ces poupées sexuelles aux capacités inédites - disponibles en version masculine ou féminine - seront bientôt commercialisées. La cible : les humains déçus par leur expérience des interactions avec des partenaires réels de sexe opposé.
« Nous développons une poupée sexuelle de nouvelle génération capable d'interagir vocalement et physiquement avec les utilisateurs, dont les prototypes sont attendus pour le mois d'août de cette année.
Il reste des défis technologiques à relever, notamment pour parvenir à une interaction humaine réaliste.
Si un simple dialogue est facile, la création de réponses interactives nécessite le développement de modèles complexes par des sociétés de logiciels spécialisées.
La nouvelle génération de poupées sexuelles, alimentées par des modèles d'IA et équipées de capteurs, peut réagir à la fois par des mouvements et par la parole, ce qui améliore considérablement l'expérience de l'utilisateur en mettant l'accent sur la connexion émotionnelle plutôt que sur les capacités de conversation de base », indiquent les responsables de l’entreprise.
L'entreprise, qui s'est concentrée sur le marché extérieur de la Chine, inclut désormais le marché chinois comme cible. Bien que la société chinoise soit largement conservatrice et réticente à aborder de tels le pays abrite le plus grand marché de poupées sexuelles, dépassant les ventes combinées des États-Unis, du Japon et de l'Allemagne.
La feuille de route de Starpery comprend le développement de robots capables d'effectuer des tâches ménagères, d'aider les personnes handicapées et de fournir des soins aux personnes âgées. D'ici à 2025, l'entreprise souhaite lancer son premier « robot de service intelligent », capable de fournir des services plus complexes aux personnes handicapées. D'ici à 2030, ces robots pourraient protéger les personnes des tâches dangereuses, selon le plan de l'entreprise.
Et vous ?
Que pensez-vous de la mise à contribution des robots dans la société ? Quels sont selon vous les cas d’utilisation les plus pertinents ?Voir aussi :
Un expert en IA affirme que les bébés virtuels seront monnaie courante pour les couples à l'avenir, ajoutant que le métavers devrait permettre de réduire les dépenses liées à un vrai bébé Elon Musk approche des chercheurs en IA pour créer son propre chatbot rival pour le monde entier, il semble que tous les géants de la technologie s'aventurent à lancer leurs chatbots respectifs Fortnite est peut-être un jeu virtuel, mais ses effets sont si réels et dangereux que les enfants consultent des médecins pour briser son emprise Xiaoice, le chatbot de Microsoft doté d'intelligence émotionnelle, séduit des millions d'hommes célibataires en Chine, il enregistre également leurs désirs et leurs émotions les plus intimes