La police utilise de plus en plus de robots pour les aider à mener à bien certaines de ses opérations. Après la ville de New York, c’est au tour des forces de maintien de l’ordre d’Atlanta de mettre à contribution le robot autonome Knightscope K5 encore connu sous le nom Robocop. Ce dernier est équipé d’une caméra géante en guise de visage et est capable de fournir des informations en temps réel sur la meilleure façon de déployer des agents humains en cas d’incident. Il est également équipé de plusieurs caméras, d’un bouton permettant de mettre le public en contact avec une personne réelle et d’un haut-parleur pour une communication audio en direct. Il est néanmoins sujet à controverse sur divers aspects dont celui de la vie privée.Du point de vue du développeur informatique, les robots policiers sont des kits matériels programmables via des moyens fournis par les constructeurs
La police néerlandaise par exemple met à contribution des chiens-robots autonomes lors des descentes dans les laboratoires de drogue afin de protéger les agents contre les risques criminels, les produits chimiques dangereux et les explosions. Du point de vue du développeur informatique, il s’agit d’un kit matériel - à la présentation visuelle similaire à celle d’un chien sur pattes – programmable via une API fournie par le constructeur Boston Dynamics. C’est au travers de cette dernière, ainsi que d’une série de modules d’extensions, que le développeur peut aller à l’essentiel de l’application à mettre en œuvre.
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.
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#
# 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) |
« Il s'agit d'une caméra de sécurité qui peut se déplacer lentement, à condition que la chaussée soit parfaitement lisse et sans bordures. Pour le même prix, vous pourriez installer des centaines de caméras de sécurité statiques dans tout le quartier. Il est plus amusant pour la police d'acheter un jouet que d'augmenter le nombre de patrouilles ou d'améliorer la formation. Si j'étais adolescent et que je voyais cela dans mon quartier, j'achèterais des masques de clown à tous mes amis et nous ferions des simulations de crime élaborées devant cet engin. À Atlanta, il servira probablement d'entraînement au tir », souligne un observateur.
« Comment jeter l'argent des contribuables dans les égouts. Cette chose ne servira à rien », ajoute un autre.
« Définissez ce que vous entendez par « menace », s'il vous plaît », souligne un autre en réponse à la présentation du robot qui veut qu’il circule dans les rues à la recherche de menaces.
« La ligne de code suivante permet de détecter les menaces. Si le niveau de mélanine est élevé : alarm() », lance un autre.
📰 The stretch... Atlanta City Council member @AmirForATL describes as an otherwise "vibrant, walkable, thriving neighborhood."
— Atlanta City Council (@atlcouncil) August 26, 2024
Council member Farokhi believes the Knightscope K5 robot, packed with AI capabilities to detect anomalies, could be the leading edge of a new… pic.twitter.com/XYLCOR9EXP
Les robots policiers apparaissent plus aux yeux des défenseurs des droits de l’Homme comme une menace pour la vie privée
L'introduction de cette technologie policière suscite l'inquiétude de certains défenseurs de la réforme de la police et du droit, qui s'interrogent sur le respect de la vie privée et sur le budget, et qui craignent que les New-Yorkais ne veuillent pas vivre dans une ville patrouillée par des robots.
En juin 2023, la Legal Aid Society a demandé au département des enquêtes de la ville de New York d'enquêter sur le déploiement des technologies de surveillance, estimant qu'elles violaient la loi sur la surveillance des technologies de surveillance par la police (Police Oversight of Surveillance Technology Act). Dans un communiqué de presse, elle a déclaré que le public ne voulait surtout pas des Digidogs, une technologie qu'elle a qualifiée de « robot dystopique ».
Le 11 avril 2023, la police de New York a annoncé de nouvelles technologies de surveillance qu'elle commencerait à utiliser immédiatement, notamment un robot de sécurité autonome K5, le DigiDog robotique de Boston Dynamics et les pistolets de repérage GPS StarChase. D'autres technologies récemment annoncées comprennent également une technologie de numérisation des empreintes digitales qui permettra aux agents de la police de New York de numériser les empreintes digitales directement à partir de leur téléphone portable et une nouvelle technologie de « réalité augmentée » disponible sur les téléphones de certains agents de la police de New York qui leur permettra de « mieux visualiser les données [contenues dans le système de connaissance du domaine] », un vaste réseau urbain de capteurs, de bases de données, d'appareils, de logiciels et d'infrastructures que la police utilise pour ses enquêtes.
Selon la Legal Aid Society, ces technologies n'ont fait l'objet d'aucune nouvelle politique en matière d'impact et d'utilisation, et le délai obligatoire de 45 jours accordé au public pour formuler des avis n'a pas été respecté. Dans le cas précis du DigiDog, les New-Yorkais ont déjà rejeté cette technologie. « La police de New York l'a achetée et rendue en 2021 après l'avoir utilisée pour cibler et terrifier des personnes dans des logements sociaux. Le public ne veut clairement pas de ce robot dystopique », déclare la Legal Aid Society. La police de New York tente de contourner les étapes requises par la loi afin de l'ajouter à son arsenal de surveillance sans l'avis de la communauté. « La police de New York a introduit cinq nouvelles technologies en 2023, en violation flagrante de la loi POST », a déclaré Shane Ferro, avocat de l'unité de criminalistique numérique de la Legal Aid Society.
NYPD introduces K5 Security Robot by Knightscope - cost-effective, mobile CCTV, assists subway riders, privacy-friendly, Times Square deployment, tech integration, security dynamics, urban safety. pic.twitter.com/8cScK6g9AY
— Interesting Engineering (@IntEngineering) January 16, 2024
Et vous ?
Considérez-vous les cas d’utilisation des robots dans des applications de ce type comme parmi les plus pertinents ? Quels sont les autres cas d’utilisation pertinents des robots combinés à l’intelligence artificielle ? Quels sont ceux qui ne le sont pas de votre point de vue ?Voir aussi :
Boston Dynamics veut vendre ses chien-robots SpotMini aux ménages et aux entreprises le succès sera-t-il au rendez-vous ? Boston Dynamics apporte une mise à jour majeure à son robot ATLAS qui fait de lui « l'un des humanoïdes les plus avancés à l'existence » Boston Dynamics a commencé à tester ses robots pour la livraison de colis l'entreprise cherche des applications commerciales pour ses machines Au cours d'une conférence, Boston Dynamics a présenté un petit extrait vidéo où son robot SpotMini danse sur un morceau de musique 
