Le nouveau pape a choisi son nom en fonction des menaces que l'intelligence artificielle fait peser sur la dignité humaine

Déjà malmenée sur les champs de bataille du fait de l'utilisation de robots

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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)