Une compagnie ferroviaire japonaise lance un géant robot humanoïde pour réparer les lignes électriques

Et ravive le débat sur les tentatives de remplacement total des humains par des robots

L’entreprise West Japan Railways utilise un robot humanoïde géant pour entretenir les voies ferrées et effectuer d'autres tâches critiques. L’objectif : protéger le personnel contre les chutes et combler les pénuries de main d’œuvre dans un pays vieillissant. Le tableau ravive le débat sur les tentatives de mise à contribution des robots dans divers secteurs de la société.

Jinki Ittai Co, un développeur de technologies robotiques, et Nippon Signal Co, une entreprise spécialisée dans les technologies de l'information et l'électricité dans les infrastructures, ont développé conjointement la machine. Son opérateur, assis dans un cockpit sur le camion, voit à travers les yeux du robot grâce à des caméras et actionne à distance ses puissants membres et mains. Avec une portée verticale de 12 mètres, la machine peut utiliser divers accessoires afin de porter des objets pesant jusqu'à 40 kg, tenir un pinceau pour peindre ou utiliser une tronçonneuse.


Cette machine devrait permettre aux personnes de tous les sexes et de tous les âges de travailler à l'entretien des chemins de fer. La mécanisation contribue à l'objectif d'élimination des accidents du travail dus à des chocs électriques ou des chutes, et permet en sus d'améliorer la sécurité.

L’initiative n’est pas sans faire penser à l’utilisation de chiens-robots pour perquisitionner les laboratoires de drogue. C’est la vie humaine qui est protégée en mettant à contribution des kits matériels - à la présentation visuelle similaire à celle d’un chien sur pattes – programmables via une API fournie par le constructeur Boston Dynamics.

Ce type de mise en œuvre desdits robots consiste en général en de la détection et suivi d’objets. Dans ce cas, il y a collecte des images provenant de deux caméras avant et effectue 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 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]# 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_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.')[/image_task, robot_state_task]...