Le Département de la sécurité intérieure a acheté un robot ressemblant à un chien. Il a modifié ce dernier avec un « réseau d'antennes » qui donne aux forces de l'ordre lancer des attaques par déni de service conte les réseaux domestiques des potentiels malfaiteurs, pour tenter de désactiver tous les appareils de l'internet des objets qu'ils possèdent. 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. 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.« Neo » est le nom de baptême dudit robot. Il permet aux agents du DHS de désactiver à distance les réseaux domestiques d'une maison ou d'un bâtiment dans lequel les forces de l'ordre effectuent une perquisition. NEO peut pénétrer dans un environnement potentiellement dangereux pour fournir un retour vidéo et audio aux agents avant l'entrée et leur permettre de communiquer avec les personnes présentes dans cet environnement. NEO est équipé d'un ordinateur de bord et d'un réseau d'antennes qui permettront aux agents de créer une attaque par déni de service afin de désactiver les dispositifs de connectés à Internet dont les malfaiteurs se servent pour anticiper sur les mouvements des forces de l’ordre ou les cibler.
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 caméras avant et une 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. Le processus principal communique avec le robot 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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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) |
Depuis 2021, les forces de police utilisent pour effectuer tous types d’activités parmi lesquelles on compte des raids antidrogues et de la surveillance. Le chien robotisé s'est avéré utile car il peut passer à travers des portes et d'autres petits obstacles. Ces dernières années, les robots quadrupèdes ont fait l'objet d'avancées technologiques significatives, leur permettant de naviguer sur différents terrains, de supporter des températures extrêmes et d'effectuer des tâches plus complexes. Cette évolution a incité certaines forces de l’ordre à envisager des opérations entièrement autonomes avec ces derniers.
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