def to_msg(self, boxes, class_ids, header):
box_arr = BoundingBoxArray()
box_arr.header = header
for i in range(len(boxes)):
if str(self.classes[class_ids[i]]) == "person":
x, y, w, h = boxes[i]
box = BoundingBox()
box.label = i
box.value = 0
box.pose.position.x = x
box.pose.position.y = y
box.pose.position.z = 0
box.pose.orientation.x = 0
box.pose.orientation.y = 0
box.pose.orientation.x = 0
box.pose.orientation.w = 0
box.dimensions.x = w
box.dimensions.y = h
box.dimensions.z = 0
box_arr.boxes.append(box)
return box_arr
def init_boundingboxarray(self, num_boxes=30):
self.boundingBoxArray_object = BoundingBoxArray()
h = std_msgs.msg.Header()
h.stamp = rospy.Time.now(
) # Note you need to call rospy.init_node() before this will work
h.frame_id = "world"
self.boundingBoxArray_object.header = h
self.minimum_dimension = 0.2
self.init_x_position = 1.0
for i in range(num_boxes):
new_box = BoundingBox()
new_box.header = h
new_box.pose = Pose()
new_box.pose.position.x = self.init_x_position + i * self.minimum_dimension
new_box.dimensions = Vector3()
new_box.dimensions.x = self.minimum_dimension
new_box.dimensions.y = self.minimum_dimension
new_box.dimensions.z = self.minimum_dimension
new_box.label = i
new_box.value = i * self.minimum_dimension
self.boundingBoxArray_object.boxes.append(new_box)
self.publish_once(self.boundingBoxArray_object)
def create_bb_msg(self, cur_bb, is_map_coord=False):
bb_corners = np.array([cur_bb['upper_left_px'], cur_bb['lower_right_px']])
if is_map_coord is True:
bb_corners_map_coord = bb_corners
else:
bb_corners_px_coord = bb_corners
bb_corners_map_coord = self.img2map.pixel2map(bb_corners_px_coord)
bb_corners_mean = np.mean(bb_corners_map_coord, axis=0)
bb_corners_diff = np.abs(bb_corners_map_coord[0, :] - bb_corners_map_coord[1, :])
cur_bb_msg = BoundingBox()
cur_bb_msg.header.frame_id = 'map'
cur_bb_msg.header.stamp = rospy.Time.now()
cur_bb_msg.pose.position.x = bb_corners_mean[0]
cur_bb_msg.pose.position.y = bb_corners_mean[1]
cur_bb_msg.pose.position.z = 0
cur_bb_msg.pose.orientation.x = 0
cur_bb_msg.pose.orientation.y = 0
cur_bb_msg.pose.orientation.z = 0
cur_bb_msg.pose.orientation.w = 1
cur_bb_msg.dimensions.x = bb_corners_diff[0]
cur_bb_msg.dimensions.y = bb_corners_diff[1]
cur_bb_msg.dimensions.z = 0.1
cur_bb_msg.value = 1.0
cur_bb_msg.label = 1
return cur_bb_msg
def rslidar_callback(msg):
t_t = time.time()
#calib = getCalibfromFile(calib_file)
#calib = getCalibfromROS(calibmsg)
frame = msg.header.seq
arr_bbox = BoundingBoxArray()
msg_cloud = ros_numpy.point_cloud2.pointcloud2_to_array(msg)
np_p = get_xyz_points(msg_cloud, True)
print(" ")
#scores, dt_box_lidar, types = proc_1.run(np_p)
scores, dt_box_lidar, types, pred_dict = proc_1.run(np_p, calib, frame)
annos_sorted = sortbydistance(dt_box_lidar, scores, types)
#pp_AB3DMOT_list = anno_to_AB3DMOT(pred_dict, msg)
pp_AB3DMOT_list = anno_to_AB3DMOT(dt_box_lidar, scores, types, msg)
pp_list = anno_to_sort(dt_box_lidar, scores, types)
pp_3D_list = anno_to_3Dsort(annos_sorted, types)
MarkerArray_list = anno_to_rviz(dt_box_lidar, scores, types, msg)
if scores.size != 0:
for i in range(scores.size):
if scores[i] > threshold:
bbox = BoundingBox()
bbox.header.frame_id = msg.header.frame_id
bbox.header.stamp = rospy.Time.now()
q = yaw2quaternion(float(dt_box_lidar[i][6]))
bbox.pose.orientation.x = q[1]
bbox.pose.orientation.y = q[2]
bbox.pose.orientation.z = q[3]
bbox.pose.orientation.w = q[0]
bbox.pose.position.x = float(
dt_box_lidar[i][0]) - movelidarcenter
bbox.pose.position.y = float(dt_box_lidar[i][1])
bbox.pose.position.z = float(dt_box_lidar[i][2])
bbox.dimensions.x = float(dt_box_lidar[i][3])
bbox.dimensions.y = float(dt_box_lidar[i][4])
bbox.dimensions.z = float(dt_box_lidar[i][5])
bbox.value = scores[i]
bbox.label = int(types[i])
arr_bbox.boxes.append(bbox)
print("total callback time: ", time.time() - t_t)
arr_bbox.header.frame_id = msg.header.frame_id
arr_bbox.header.stamp = msg.header.stamp
if len(arr_bbox.boxes) is not 0:
pub_arr_bbox.publish(arr_bbox)
arr_bbox.boxes = []
else:
arr_bbox.boxes = []
pub_arr_bbox.publish(arr_bbox)
pubRviz.publish(MarkerArray_list)
pubSort.publish(pp_list)
pub3DSort.publish(pp_3D_list)
pubAB3DMOT.publish(pp_AB3DMOT_list)
def rslidar_callback(msg):
t_t = time.time()
arr_bbox = BoundingBoxArray()
#t = time.time()
msg_cloud = ros_numpy.point_cloud2.pointcloud2_to_array(msg)
np_p = get_xyz_points(msg_cloud, True)
print(" ")
#print("prepare cloud time: ", time.time() - t)
#t = time.time()
scores, dt_box_lidar, types = proc_1.run(np_p)
#print("network forward time: ", time.time() - t)
# filter_points_sum = []
#t = time.time()
if scores.size != 0:
for i in range(scores.size):
bbox = BoundingBox()
bbox.header.frame_id = msg.header.frame_id
bbox.header.stamp = rospy.Time.now()
q = yaw2quaternion(float(dt_box_lidar[i][6]))
bbox.pose.orientation.x = q[0]
bbox.pose.orientation.y = q[1]
bbox.pose.orientation.z = q[2]
bbox.pose.orientation.w = q[3]
bbox.pose.position.x = float(dt_box_lidar[i][0])
bbox.pose.position.y = float(dt_box_lidar[i][1])
bbox.pose.position.z = float(dt_box_lidar[i][2])
bbox.dimensions.x = float(dt_box_lidar[i][3])
bbox.dimensions.y = float(dt_box_lidar[i][4])
bbox.dimensions.z = float(dt_box_lidar[i][5])
bbox.value = scores[i]
bbox.label = types[i]
arr_bbox.boxes.append(bbox)
# filter_points = np_p[point_indices[:,i]]
# filter_points_sum.append(filter_points)
#print("publish time cost: ", time.time() - t)
# filter_points_sum = np.concatenate(filter_points_sum, axis=0)
# filter_points_sum = filter_points_sum[:, :3]
# print("output of concatenate:", filter_points_sum)
# filter_points_sum = np.arange(24).reshape(8,3)
# cluster_cloud = xyz_array_to_pointcloud2(filter_points_sum, stamp=rospy.Time.now(), frame_id=msg.header.frame_id)
# pub_segments.publish(cluster_cloud)
print("total callback time: ", time.time() - t_t)
arr_bbox.header.frame_id = msg.header.frame_id
arr_bbox.header.stamp = rospy.Time.now()
if len(arr_bbox.boxes) is not 0:
pub_arr_bbox.publish(arr_bbox)
arr_bbox.boxes = []
else:
arr_bbox.boxes = []
pub_arr_bbox.publish(arr_bbox)
def callback(self, box_msg):
labeled_boxes = {}
label_buf = []
orientation = Pose().orientation
for box in box_msg.boxes:
if box.label in label_buf:
labeled_boxes[box.label] += [self.get_points(box)]
orientation = box.pose.orientation
else:
labeled_boxes[box.label] = [self.get_points(box)]
label_buf.append(box.label)
bounding_box_msg = BoundingBoxArray()
for label, boxes in zip(labeled_boxes.keys(), labeled_boxes.values()):
thresh = self.thresh
if self.label_lst[label] == 'shelf_flont':
thresh = 2.0
clustering = Clustering()
boxes = np.array(boxes)
result = clustering.clustering_wrapper(boxes, thresh)
for cluster in result:
max_candidates = [
boxes[i][0] + (boxes[i][1] * 0.5) for i in cluster.indices
]
min_candidates = [
boxes[i][0] - (boxes[i][1] * 0.5) for i in cluster.indices
]
candidates = np.array(max_candidates + min_candidates)
dimension = candidates.max(axis=0) - candidates.min(axis=0)
center = candidates.min(axis=0) + (dimension * 0.5)
distances = self.get_distances(
np.array([boxes[i][0] for i in cluster.indices]), [
np.linalg.norm(boxes[i][1]) * 0.5
for i in cluster.indices
])
tmp_box = BoundingBox()
tmp_box.header = box_msg.header
tmp_box.dimensions.x = dimension[0]
tmp_box.dimensions.y = dimension[1]
tmp_box.dimensions.z = dimension[2]
tmp_box.pose.position.x = center[0]
tmp_box.pose.position.y = center[1]
tmp_box.pose.position.z = center[2]
tmp_box.pose.orientation = orientation
tmp_box.label = label
tmp_box.value = distances.mean()
bounding_box_msg.boxes.append(tmp_box)
bounding_box_msg.header = box_msg.header
self.box_pub.publish(bounding_box_msg)
def convert(header, xx_cube):
boundingboxarray_msg = BoundingBoxArray()
boundingboxarray_msg.header = header
num_xx = int(xx_cube.shape[0] / 8)
for i in range(num_xx):
cube_raw = xx_cube[range(8 * i, 8 * i + 8), :]
bb_in_camera = np.column_stack((cube_raw, np.ones((8, 1))))
bb_in_lidar = np.linalg.inv(calib.lidar_to_cam).dot(bb_in_camera.T).T
boundingbox_msg = BoundingBox()
boundingbox_msg.header = boundingboxarray_msg.header
# boundingbox中心点位置
boundingbox_msg.pose.position.x = bb_in_lidar[:, 0].mean()
boundingbox_msg.pose.position.y = bb_in_lidar[:, 1].mean()
boundingbox_msg.pose.position.z = bb_in_lidar[:, 2].mean()
# 寻找y坐标最小的顶点，计算相邻两个顶点的旋转角及边长
bb_bottom = bb_in_lidar[:4]
min_idx = np.where(bb_bottom[:, 1] == bb_bottom[:, 1].min())[0][0]
theta = math.atan2(
bb_bottom[(min_idx + 1) % 4, 1] - bb_bottom[min_idx, 1],
bb_bottom[(min_idx + 1) % 4, 0] - bb_bottom[min_idx, 0])
b_1 = (
(bb_bottom[(min_idx + 1) % 4, 1] - bb_bottom[min_idx, 1])**2 +
(bb_bottom[(min_idx + 1) % 4, 0] - bb_bottom[min_idx, 0])**2)**0.5
b_2 = (
(bb_bottom[(min_idx + 3) % 4, 1] - bb_bottom[min_idx, 1])**2 +
(bb_bottom[(min_idx + 3) % 4, 0] - bb_bottom[min_idx, 0])**2)**0.5
if theta < 90 * math.pi / 180:
rotation_angle = theta
dimension_x = b_1
dimension_y = b_2
else:
rotation_angle = theta - 90 * math.pi / 180
dimension_x = b_2
dimension_y = b_1
# boundingbox旋转角四元数
boundingbox_msg.pose.orientation.x = 0
boundingbox_msg.pose.orientation.y = 0
boundingbox_msg.pose.orientation.z = math.sin(0.5 * rotation_angle)
boundingbox_msg.pose.orientation.w = math.cos(0.5 * rotation_angle)
# boundingbox尺寸
boundingbox_msg.dimensions.x = dimension_x
boundingbox_msg.dimensions.y = dimension_y
boundingbox_msg.dimensions.z = bb_in_lidar[:, 2].max(
) - bb_in_lidar[:, 2].min()
boundingbox_msg.value = 0
boundingbox_msg.label = 0
boundingboxarray_msg.boxes.append(boundingbox_msg)
return boundingboxarray_msg
def detector_callback(self, pcl_msg):
start = time.time()
# rospy.loginfo('Processing Pointcloud with PointRCNN')
arr_bbox = BoundingBoxArray()
seq = pcl_msg.header.seq
stamp = pcl_msg.header.stamp
# in message pointcloud has x pointing forward, y pointing to the left and z pointing upward
pts_lidar = np.array([[
p[0], p[1], p[2], p[3]
] for p in pc2.read_points(
pcl_msg, skip_nans=True, field_names=("x", "y", "z", "intensity"))
],
dtype=np.float32)
scores, dt_box_lidar, types = self.run_model(pts_lidar)
# TODO: question convert into torch tensors? torch.from_numpy(pts_lidar)
# move onto gpu if available
# TODO: check if needs translation/rotation to compensate for tilt etc.
if scores.size != 0:
for i in range(scores.size):
bbox = BoundingBox()
bbox.header.frame_id = pcl_msg.header.frame_id
bbox.header.stamp = rospy.Time.now()
# bbox.header.seq = pcl_msg.header.seq
q = yaw2quaternion(float(dt_box_lidar[i][6]))
bbox.pose.orientation.x = q[1]
bbox.pose.orientation.y = q[2]
bbox.pose.orientation.z = q[3]
bbox.pose.orientation.w = q[0]
bbox.pose.position.x = float(dt_box_lidar[i][0])
bbox.pose.position.y = float(dt_box_lidar[i][1])
bbox.pose.position.z = float(dt_box_lidar[i][2])
bbox.dimensions.x = float(dt_box_lidar[i][3])
bbox.dimensions.y = float(dt_box_lidar[i][4])
bbox.dimensions.z = float(dt_box_lidar[i][5])
bbox.value = scores[i]
bbox.label = int(types[i])
arr_bbox.boxes.append(bbox)
# rospy.loginfo("3D detector time: {}".format(time.time() - start))
arr_bbox.header.frame_id = pcl_msg.header.frame_id
arr_bbox.header.stamp = pcl_msg.header.stamp
arr_bbox.header.seq = pcl_msg.header.seq
if len(arr_bbox.boxes) != 0:
self.pub_arr_bbox.publish(arr_bbox)
arr_bbox.boxes = []
else:
arr_bbox.boxes = []
self.pub_arr_bbox.publish(arr_bbox)
def dummyBoundingBoxPublisher():
pub = rospy.Publisher('/dummy_bounding_box', BoundingBox, queue_size=1)
rospy.init_node('dummyBoundingBoxPublisher_node', anonymous=True)
rate = rospy.Rate(25)
boundingBox_object = BoundingBox()
i = 0
pose_object = Pose()
dimensions_object = Vector3()
minimum_dimension = 0.2
boundingBox_object.label = 1234
while not rospy.is_shutdown():
h = std_msgs.msg.Header()
h.stamp = rospy.Time.now(
) # Note you need to call rospy.init_node() before this will work
h.frame_id = "world"
boundingBox_object.header = h
sinus_value = math.sin(i / 10.0)
boundingBox_object.value = sinus_value
# Change Pose to see effects
pose_object.position.x = 1.0
pose_object.position.y = 0.0
pose_object.position.z = sinus_value
# ai, aj, ak == roll, pitch, yaw
quaternion = tf.transformations.quaternion_from_euler(ai=0,
aj=0,
ak=sinus_value)
pose_object.orientation.x = quaternion[0]
pose_object.orientation.y = quaternion[1]
pose_object.orientation.z = quaternion[2]
pose_object.orientation.w = quaternion[3]
dimensions_object.x = sinus_value / 10 + minimum_dimension
dimensions_object.y = minimum_dimension
dimensions_object.z = minimum_dimension
# Assign pose and dimension objects
boundingBox_object.pose = pose_object
boundingBox_object.dimensions = dimensions_object
pub.publish(boundingBox_object)
rate.sleep()
i += 1
def rslidar_callback(msg):
# t_t = time.time()
arr_bbox = BoundingBoxArray()
msg_cloud = ros_numpy.point_cloud2.pointcloud2_to_array(msg)
np_p = get_xyz_points(msg_cloud, True)
print(" ")
seq = msg.header.seq
stamp = msg.header.stamp
input_points = {
'stamp': stamp,
'seq': seq,
'points': np_p
}
if(proc_1.get_lidar_data(input_points)):
scores, dt_box_lidar, types = proc_1.run()
if scores.size != 0:
for i in range(scores.size):
bbox = BoundingBox()
bbox.header.frame_id = msg.header.frame_id
bbox.header.stamp = rospy.Time.now()
q = yaw2quaternion(float(dt_box_lidar[i][8]))
bbox.pose.orientation.x = q[1]
bbox.pose.orientation.y = q[2]
bbox.pose.orientation.z = q[3]
bbox.pose.orientation.w = q[0]
bbox.pose.position.x = float(dt_box_lidar[i][0])
bbox.pose.position.y = float(dt_box_lidar[i][1])
bbox.pose.position.z = float(dt_box_lidar[i][2])
bbox.dimensions.x = float(dt_box_lidar[i][4])
bbox.dimensions.y = float(dt_box_lidar[i][3])
bbox.dimensions.z = float(dt_box_lidar[i][5])
bbox.value = scores[i]
bbox.label = int(types[i])
arr_bbox.boxes.append(bbox)
# print("total callback time: ", time.time() - t_t)
arr_bbox.header.frame_id = msg.header.frame_id
arr_bbox.header.stamp = msg.header.stamp
if len(arr_bbox.boxes) is not 0:
pub_arr_bbox.publish(arr_bbox)
arr_bbox.boxes = []
else:
arr_bbox.boxes = []
pub_arr_bbox.publish(arr_bbox)
def vis_callback(msg):
obj_bba = BoundingBoxArray()
obj_bba.header.frame_id = 'map'
for obj in msg.objects:
obj_bb = BoundingBox()
res = tosm.query_individual(obj.object_name + str(obj.ID))
obj_bb.header = obj.header
pose = res.pose[0].replace('[', '').replace(']', '').split(',')
size = res.size[0].replace('[', '').replace(']', '').split(',')
obj_bb.pose.position.x = float(pose[0])
obj_bb.pose.position.y = float(pose[1])
obj_bb.pose.position.z = float(pose[2]) + float(size[2]) / 2.0
obj_bb.pose.orientation.x = float(pose[3])
obj_bb.pose.orientation.y = float(pose[4])
obj_bb.pose.orientation.z = float(pose[5])
obj_bb.pose.orientation.w = float(pose[6])
# bounding box size
obj_bb.dimensions.x = float(size[0])
obj_bb.dimensions.y = float(size[1])
obj_bb.dimensions.z = float(size[2])
# likelihood
obj_bb.value = 1
# determine the color
if (obj.object_name == "hingeddoor"):
obj_bb.label = 1
elif (obj.object_name == "elevatordoor"):
obj_bb.label = 2
elif (obj.object_name == "A"):
obj_bb.label = 3
elif (obj.object_name == "B"):
obj_bb.label = 4
elif (obj.object_name == "C"):
obj_bb.label = 5
else:
obj_bb.label = 10
obj_bba.boxes.append(obj_bb)
ses_map_object_vis_pub.publish(obj_bba)
def to_msg(self):
msg = BoundingBox()
#msg.header = header
msg.label = self.id
msg.value = self.d_area
msg.pose.position.x = self.center[-1][0]
msg.pose.position.y = self.center[-1][1]
msg.pose.position.z = 0
msg.pose.orientation.x = self.direction[0]
msg.pose.orientation.y = self.direction[1]
msg.pose.orientation.z = 0
msg.pose.orientation.w = 0
msg.dimensions.x = self.withe
msg.dimensions.y = self.hight
msg.dimensions.z = 0
return msg
def to_msg(self):
msg = BoundingBox()
msg.label = self.id
msg.value = 0
msg.pose.position.x = self.bbox[0]
msg.pose.position.y = self.bbox[1]
msg.pose.position.z = 0
msg.pose.orientation.x = 0
msg.pose.orientation.y = 0
msg.pose.orientation.x = 0
msg.pose.orientation.w = 0
msg.dimensions.x = self.bbox[2] - self.bbox[0]
msg.dimensions.y = self.bbox[3] - self.bbox[1]
msg.dimensions.z = 0
#print (msg)
return msg
box_b.label = 5 box_arr = BoundingBoxArray() now = rospy.Time.now() box_a.header.stamp = now box_b.header.stamp = now box_arr.header.stamp = now box_a.header.frame_id = "map" box_b.header.frame_id = "map" box_arr.header.frame_id = "map" q = quaternion_about_axis((counter % 100) * math.pi * 2 / 100.0, [0, 0, 1]) box_a.pose.orientation.x = q[0] box_a.pose.orientation.y = q[1] box_a.pose.orientation.z = q[2] box_a.pose.orientation.w = q[3] box_b.pose.orientation.w = 1 box_b.pose.position.y = 2 box_b.dimensions.x = (counter % 10 + 1) * 0.1 box_b.dimensions.y = ((counter + 1) % 10 + 1) * 0.1 box_b.dimensions.z = ((counter + 2) % 10 + 1) * 0.1 box_a.dimensions.x = 1 box_a.dimensions.y = 1 box_a.dimensions.z = 1 box_a.value = (counter % 100) / 100.0 box_b.value = 1 - (counter % 100) / 100.0 box_arr.boxes.append(box_a) box_arr.boxes.append(box_b) pub.publish(box_arr) r.sleep() counter = counter + 1
def rslidar_callback(msg):
t_t = time.time()
arr_bbox = BoundingBoxArray()
msg_cloud = ros_numpy.point_cloud2.pointcloud2_to_array(msg)
np_p = get_xyz_points(msg_cloud, True)
print(" ")
scores, dt_box_lidar, types = proc_1.run(np_p)
MarkerArray_list = MarkerArray() ##CREO EL MENSAJE GENERAL
if scores.size != 0:
for i in range(scores.size):
if scores[i] > 0.4:
bbox = BoundingBox()
bbox.header.frame_id = msg.header.frame_id
bbox.header.stamp = rospy.Time.now()
q = yaw2quaternion(float(dt_box_lidar[i][6]))
bbox.pose.orientation.x = q[1]
bbox.pose.orientation.y = q[2]
bbox.pose.orientation.z = q[3]
bbox.pose.orientation.w = q[0]
bbox.pose.position.x = float(dt_box_lidar[i][0]) - 69.12 / 2
bbox.pose.position.y = float(dt_box_lidar[i][1])
bbox.pose.position.z = float(dt_box_lidar[i][2])
bbox.dimensions.x = float(dt_box_lidar[i][3])
bbox.dimensions.y = float(dt_box_lidar[i][4])
bbox.dimensions.z = float(dt_box_lidar[i][5])
bbox.value = scores[i]
bbox.label = int(types[i])
arr_bbox.boxes.append(bbox)
obj = Marker()
#obj.CUBE = 1
obj.header.stamp = rospy.Time.now()
obj.header.frame_id = msg.header.frame_id
obj.type = Marker.CUBE
obj.id = i
obj.lifetime = rospy.Duration.from_sec(1)
#obj.type = int(types[i])
obj.pose.position.x = float(dt_box_lidar[i][0]) - 69.12 / 2
obj.pose.position.y = float(dt_box_lidar[i][1])
obj.pose.position.z = float(dt_box_lidar[i][2])
q = yaw2quaternion(float(dt_box_lidar[i][6]))
obj.pose.orientation.x = q[1]
obj.pose.orientation.y = q[2]
obj.pose.orientation.z = q[3]
obj.pose.orientation.w = q[0]
obj.scale.x = float(dt_box_lidar[i][3])
obj.scale.y = float(dt_box_lidar[i][4])
obj.scale.z = float(dt_box_lidar[i][5])
obj.color.r = 255
obj.color.a = 0.5
MarkerArray_list.markers.append(obj)
print("total callback time: ", time.time() - t_t)
arr_bbox.header.frame_id = msg.header.frame_id
arr_bbox.header.stamp = msg.header.stamp
if len(arr_bbox.boxes) is not 0:
pub_arr_bbox.publish(arr_bbox)
arr_bbox.boxes = []
else:
arr_bbox.boxes = []
pub_arr_bbox.publish(arr_bbox)
pubMarker.publish(MarkerArray_list)
box_a.label = 2
box_b.label = 5
box_arr = BoundingBoxArray()
now = rospy.Time.now()
box_a.header.stamp = now
box_b.header.stamp = now
box_arr.header.stamp = now
box_a.header.frame_id = "map"
box_b.header.frame_id = "map"
box_arr.header.frame_id = "map"
q = quaternion_about_axis((counter % 100) * math.pi * 2 / 100.0, [0, 0, 1])
box_a.pose.orientation.x = q[0]
box_a.pose.orientation.y = q[1]
box_a.pose.orientation.z = q[2]
box_a.pose.orientation.w = q[3]
box_b.pose.orientation.w = 1
box_b.pose.position.y = 2
box_b.dimensions.x = (counter % 10 + 1) * 0.1
box_b.dimensions.y = ((counter + 1) % 10 + 1) * 0.1
box_b.dimensions.z = ((counter + 2) % 10 + 1) * 0.1
box_a.dimensions.x = 1
box_a.dimensions.y = 1
box_a.dimensions.z = 1
box_a.value = (counter % 100) / 100.0
box_b.value = 1 - (counter % 100) / 100.0
box_arr.boxes.append(box_a)
box_arr.boxes.append(box_b)
pub.publish(box_arr)
r.sleep()
counter = counter + 1
