Des experts lancent un appel global à la mise sur pied de lignes rouges en matière de développement d’IA

**Patrick Ruiz** · 26/09/2025, 12h51

Des experts lancent un appel global à la mise sur pied de lignes rouges en matière de développement d’IA
Dix lauréats du prix Nobel de la paix figurent parmi les signataires de l’appel

Des experts de divers domaines parmi lesquels une dizaine de prix Nobel de la paix viennent de lancer un appel global à la mise sur pied de lignes rouges en matière de développement d’intelligence artificielle. Cet appel, comme de précédents aux contenus similaires, tombent dans un contexte de perception de l’intelligence artificielle comme un moyen de dominer sur les nations. Le tableau suscite des questionnements sur la possibilité de la mise sur pied de lignes rouges en matière de développement d’intelligence artificielle et leur applicabilité à l’échelle globale.

L’on assiste en effet à la militarisation de l’intelligence artificielle comme le souligne le président ukrainien Volodymyr Zelenskyy

Le président Volodymyr Zelenskyy a profité de son discours à l'ONU pour avertir les dirigeants mondiaux que nous vivons actuellement « la course aux armements la plus destructrice de l'histoire de l'humanité ». Le président de l'Ukraine a profité de son discours devant l'Assemblée pour évoquer l'évolution rapide des technologies militaires et la menace que cela représente pour les populations du monde entier. Il a déclaré à l'ONU que cette course aux armements était d'autant plus destructrice qu'elle était alimentée par l'intelligence artificielle et que le monde ne faisait pas assez pour se protéger contre cette menace.

Robots-chiens, drones, etc. : Tous les pays sont lancés dans le développement d’armes animées par l’intelligence artificielle

Les robots-chiens par exemple font partie des centres d’intérêt de plusieurs armées de par le monde. Ces robots s’appuient à la base sur des applications de détection et suivi d’objets. Dans ce cas, il y a au préalable collecte des images provenant de caméras avant puis détection d’objet sur une classe spécifiée. Cette détection utilise Tensorflow via le tensorflow_object_detector. Le robot 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 Python :

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# 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)

🇺🇦🇬🇧 Ukraine deployed 30 british robots dogs in Donetsk region, replacing soldiers for perilous missions like spying on Russian trenches or detecting mines.
The Robots are guided by an operator. They move silently at a speed of 15 km/h and operate within a 3.5 km radius.
— BILD. pic.twitter.com/I8cX3clXm4
— Geo News (@GeoTienou) August 14, 2024

Après le système d’intelligence artificielle dénommé « Gospel », celui dénommé « Lavender » a fait surface dans une glaçante enquête sur la mise à contribution de l’intelligence artificielle par Israël contre les militants du Hamas. Lavender permet d’opérer la traque et la frappe des dirigeants du Hamas. Le système n’est pas exempt d’erreur et provoque la mort de 15 à 20 civils par frappe. Israël le déploie néanmoins en l’état, ce qui suggère que les dégâts en lien avec les faux positifs du système sont pris en compte comme dommages collatéraux.

EXPOSE | ‘LAVENDAR’: THE AI MACHINE DIRECTING ISRAEL'S BOMBING SPREE IN GAZA

Lavender is an AI-driven program developed by the Israeli military to generate targets for airstrikes in Gaza.
It marks individuals suspected of affiliation with Hamas and Palestinian Islamic Jihad… pic.twitter.com/ODrR2IALHw
— Suppressed News. (@SuppressedNws) April 3, 2024

Le tableau suscite des questionnements sur la possibilité de la mise sur pied de lignes rouges en matière de développement d’intelligence artificielle et leur applicabilité à l’échelle globale.

En effet, des acteurs de poids de la scène mondiale comme Vladimir Poutine considèrent l’intelligence artificielle comme un moyen de dominer sur les nations. Il n’empêche néanmoins que des initiatives comme celle des USA, de l’Europe et du Royaume-Uni aboutissent à des accords contraignants en matière de développement d’intelligence artificielle. En établissant des normes juridiquement contraignantes, ce traité vise à garantir que l’IA est développée et utilisée de manière éthique et responsable, tout en protégeant les valeurs fondamentales de la société.

Source : red lines

Et vous ?

Quels rôles les organisations internationales, comme l’ONU, devraient-elles jouer dans la régulation de l’IA ?

Voir aussi :

L'Ukraine s'empresse de créer des drones de guerre dotés de capacités d'IA, qui l'aideront à surmonter le brouillage des signaux par les Russes et permettront aux drones de travailler en groupes plus importants

Les USA, la Chine et une soixantaine d'autres pays appellent à une utilisation « responsable » de l'IA militaire. Les critiques notent que cette déclaration n'est pas juridiquement contraignante

L'attaque massive de drones menée par l'Ukraine contre la Russie a été réalisée à l'aide d'un logiciel open source de pilotage automatique avancé, il a été développé à l'origine pour les systèmes Arduino