def callback_with_cluster_box(self, cluster_boxes_msg, instance_boxes_msg, instance_label_msg):
labeled_cluster_boxes = BoundingBoxArray()
labeled_instance_boxes = BoundingBoxArray()
labeled_cluster_boxes.header = cluster_boxes_msg.header
labeled_instance_boxes.header = instance_boxes_msg.header
for index, box in enumerate(cluster_boxes_msg.boxes):
if not box.pose.position.x == 0.0:
tmp_box = BoundingBox()
tmp_box.header = box.header
tmp_box.pose = box.pose
tmp_box.dimensions = box.dimensions
# TODO fix index indent, jsk_pcl_ros_utils/label_to_cluster_point_indices_nodelet.cpp
tmp_box.label = index + 1
labeled_cluster_boxes.boxes.append(tmp_box)
for box, label in zip(instance_boxes_msg.boxes, instance_label_msg.labels):
tmp_box = BoundingBox()
tmp_box.header = box.header
tmp_box.pose = box.pose
tmp_box.dimensions = box.dimensions
tmp_box.label = label.id
labeled_instance_boxes.boxes.append(tmp_box)
self.labeled_cluster_boxes_pub.publish(labeled_cluster_boxes)
self.labeled_instance_boxes_pub.publish(labeled_instance_boxes)
def get_nearest_box(self, req):
distance = 100
has_request_item = False
nearest_box = BoundingBox()
for index, box in enumerate(self.boxes.boxes):
if box.pose.position.x == 0 or \
box.pose.position.y == 0 or \
box.pose.position.z == 0:
rospy.logwarn('boxes has (0, 0, 0) position box')
continue
if self.label_lst[box.label] == req.label:
has_request_item = True
ref_point = np.array([box.pose.position.x + (box.dimensions.x * 0.5),
box.pose.position.y + (box.dimensions.y * 0.5),
box.pose.position.z + (box.dimensions.z * 0.5)])
target_point = np.array([req.target.x,
req.target.y,
req.target.z])
if np.linalg.norm(ref_point - target_point) < distance:
nearest_box.pose = box.pose
nearest_box.dimensions = box.dimensions
distance = np.linalg.norm(ref_point - target_point)
return nearest_box, has_request_item
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 viz_bbox(position, q, size, i):
bbox = BoundingBox()
bbox.pose.position = position
bbox.pose.orientation = Quaternion(*q)
bbox.dimensions = size
bbox.label = i
return bbox
def callback(msg):
box_array = BoundingBoxArray()
box_array.header = msg.header
for footstep in msg.footsteps:
box = BoundingBox()
box.header = msg.header
box.pose = footstep.pose
box.dimensions = footstep.dimensions
box_array.boxes.append(box)
pub.publish(box_array)
def callback(msg):
box_array = BoundingBoxArray()
box_array.header = msg.header
for footstep in msg.footsteps:
box = BoundingBox()
box.header = msg.header
box.pose = footstep.pose
box.dimensions = footstep.dimensions
box_array.boxes.append(box)
pub.publish(box_array)
def callback(msg):
box_array = BoundingBoxArray()
box_array.header = msg.header
for footstep in msg.footsteps:
box = BoundingBox()
box.header = msg.header
box.pose = footstep.pose
box.dimensions = footstep.dimensions
box.pose.position.z += (z_max + z_min) / 2.0
box.dimensions.z = z_max - z_min
box_array.boxes.append(box)
pub.publish(box_array)
def callback(msg):
box_array = BoundingBoxArray()
box_array.header = msg.header
for footstep in msg.footsteps:
box = BoundingBox()
box.header = msg.header
box.pose = footstep.pose
box.dimensions = footstep.dimensions
box.pose.position.z += (z_max + z_min) / 2.0
box.dimensions.z = z_max - z_min
box_array.boxes.append(box)
pub.publish(box_array)
def callback(self, instance_boxes_msg, instance_label_msg):
labeled_instance_boxes = BoundingBoxArray()
labeled_instance_boxes.header = instance_boxes_msg.header
for box, label in zip(instance_boxes_msg.boxes, instance_label_msg.labels):
tmp_box = BoundingBox()
tmp_box.header = box.header
tmp_box.pose = box.pose
tmp_box.dimensions = box.dimensions
tmp_box.label = label.id
labeled_instance_boxes.boxes.append(tmp_box)
self.labeled_instance_boxes_pub.publish(labeled_instance_boxes)
def readXML(file):
tree = ET.parse(file)
root = tree.getroot()
item = root.findall('./tracklets/item')
d = {}
pictograms = {}
for i, v in enumerate(item):
h = float(v.find('h').text)
w = float(v.find('w').text)
l = float(v.find('l').text)
frame = int(v.find('first_frame').text)
size = Vector3(l, w, h)
label = v.find('objectType').text
pose = v.findall('./poses/item')
for j, p in enumerate(pose):
tx = float(p.find('tx').text)
ty = float(p.find('ty').text)
tz = float(p.find('tz').text)
rz = float(p.find('rz').text)
q = tf.transformations.quaternion_from_euler(0.0, 0.0, rz)
b = BoundingBox()
b.pose.position = Vector3(tx, ty, tz/2.0)
b.pose.orientation = Quaternion(*q)
b.dimensions = size
b.label = i
picto_text = Pictogram()
picto_text.mode = Pictogram.STRING_MODE
picto_text.pose.position = Vector3(tx, ty, -tz/2.0)
q = tf.transformations.quaternion_from_euler(0.7, 0.0, -0.7)
picto_text.pose.orientation = Quaternion(0.0, -0.5, 0.0, 0.5)
picto_text.size = 1
picto_text.color = std_msgs.msg.ColorRGBA(1, 1, 1, 1)
picto_text.character = label
if d.has_key(frame + j) == True:
d[frame + j].append(b)
pictograms[frame + j].append(picto_text)
else:
d[frame + j] = [b]
pictograms[frame + j] = [picto_text]
return d, pictograms
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 prepareDetectionRegistration(self, centroid, now):
obj_det = BucketDetection()
obj_det.image = self.cv_bridge.cv2_to_imgmsg(self.stereo_left, "bgr8")
obj_det.tag = "object_tags/gate"
bbox_3d = BoundingBox()
bbox_3d.dimensions = Vector3(self.gate_dimensions[0], self.gate_dimensions[1], self.gate_dimensions[2])
bbox_pose = Pose()
x, y, z = list((np.squeeze(centroid)).T)
obj_det.position = Point(x, y, z)
bbox_pose.position = Point(x, y, z)
bbox_3d.pose = bbox_pose
bbox_header = Header()
bbox_header.frame_id = "duo3d_optical_link_front"
bbox_header.stamp = now
bbox_3d.header = bbox_header
obj_det.bbox_3d = bbox_3d
obj_det.header = Header()
obj_det.header.frame_id = bbox_header.frame_id
obj_det.header.stamp = now
return obj_det
def publish(self, event):
bbox_array_msg = BoundingBoxArray()
bbox_array_msg.header.seq = self.seq
bbox_array_msg.header.frame_id = self.frame_id
bbox_array_msg.header.stamp = event.current_real
for box in self.boxes:
pos = box['position']
rot = box.get('rotation', [0, 0, 0])
qua = quaternion_from_euler(*rot)
dim = box['dimension']
bbox_msg = BoundingBox()
bbox_msg.header.seq = self.seq
bbox_msg.header.frame_id = self.frame_id
bbox_msg.header.stamp = event.current_real
bbox_msg.pose.position = Point(*pos)
bbox_msg.pose.orientation = Quaternion(*qua)
bbox_msg.dimensions = Vector3(*dim)
bbox_array_msg.boxes.append(bbox_msg)
self.pub.publish(bbox_array_msg)
def publish(self, event):
bbox_array_msg = BoundingBoxArray()
bbox_array_msg.header.seq = self.seq
bbox_array_msg.header.frame_id = self.frame_id
bbox_array_msg.header.stamp = event.current_real
for box in self.boxes:
pos = box['position']
rot = box.get('rotation', [0, 0, 0])
qua = quaternion_from_euler(*rot)
dim = box['dimension']
bbox_msg = BoundingBox()
bbox_msg.header.seq = self.seq
bbox_msg.header.frame_id = self.frame_id
bbox_msg.header.stamp = event.current_real
bbox_msg.pose.position = Point(*pos)
bbox_msg.pose.orientation = Quaternion(*qua)
bbox_msg.dimensions = Vector3(*dim)
bbox_array_msg.boxes.append(bbox_msg)
self.pub.publish(bbox_array_msg)
def publish(self, event):
bbox_array_msg = BoundingBoxArray()
bbox_array_msg.header.seq = self.seq
bbox_array_msg.header.frame_id = self.frame_id
bbox_array_msg.header.stamp = event.current_real
for i_box in xrange(self.n_boxes):
pos = self.positions[i_box]
rot = self.rotations[i_box]
qua = quaternion_from_euler(*rot)
dim = self.dimensions[i_box]
bbox_msg = BoundingBox()
bbox_msg.header.seq = self.seq
bbox_msg.header.frame_id = self.frame_id
bbox_msg.header.stamp = event.current_real
bbox_msg.pose.position = Point(*pos)
bbox_msg.pose.orientation = Quaternion(*qua)
bbox_msg.dimensions = Vector3(*dim)
bbox_array_msg.boxes.append(bbox_msg)
self.pub.publish(bbox_array_msg)
def prepare_detection_registration(self, centroid, det, now):
obj_det = BucketDetection()
obj_det.image = self.cv_bridge.cv2_to_imgmsg(self.stereo_left, "bgr8")
obj_det.tag = str("object_tags/" + self.classes[det[0]])
bbox_dims = np.asarray([1.0, 1.0, 1.0])
if rospy.has_param("object_tags/" + self.classes[det[0]] + "/dimensions"):
bbox_dims = np.asarray(rospy.get_param("object_tags/" + self.classes[det[0]] + "/dimensions")).astype(float)
bbox_3d = BoundingBox()
bbox_3d.dimensions = Vector3(bbox_dims[0], bbox_dims[1], bbox_dims[2])
bbox_pose = Pose()
x, y, z = list((np.squeeze(centroid)).T)
obj_det.position = Point(x, y, z)
bbox_pose.position = Point(x, y, z)
bbox_3d.pose = bbox_pose
bbox_header = Header()
bbox_header.frame_id = "duo3d_optical_link_front"
bbox_header.stamp = now
bbox_3d.header = bbox_header
obj_det.bbox_3d = bbox_3d
obj_det.header = Header()
obj_det.header.frame_id = bbox_header.frame_id
obj_det.header.stamp = now
return obj_det
def callback(self, front_MarkerArray, back_MarkerArray, TwistStamped):
# print("front",len(front_MarkerArray.markers)/4)
# print("back",len(back_MarkerArray.markers)/4)
# # Concat front and back MarkerArray Messages
add_MarkerArray = copy.deepcopy(front_MarkerArray)
for i in range(len(back_MarkerArray.markers)):
add_MarkerArray.markers.append(back_MarkerArray.markers[i])
# print("add",len(add_MarkerArray.markers)/4)
# print("done")
if len(add_MarkerArray.markers) == 0:
return
header = add_MarkerArray.markers[0].header
frame = header.seq
boxes = BoundingBoxArray() #3D Boxes with JSK
boxes.header = header
texts = PictogramArray() #Labels with JSK
texts.header = header
obj_ori_arrows = MarkerArray() #arrow with visualization_msgs
velocity_markers = MarkerArray() #text with visualization_msgs
obj_path_markers = MarkerArray() # passed path
warning_line_markers = MarkerArray()
dets = np.zeros((0, 9)) # (None, 9) : 9는 사용할 3d bbox의 파라미터 개수
obj_box_info = np.empty((0, 7))
obj_label_info = np.empty((0, 2))
# frame을 rviz에 출력
overlayTxt = OverlayText()
overlayTxt.left = 10
overlayTxt.top = 10
overlayTxt.width = 1200
overlayTxt.height = 1200
overlayTxt.fg_color.a = 1.0
overlayTxt.fg_color.r = 1.0
overlayTxt.fg_color.g = 1.0
overlayTxt.fg_color.b = 1.0
overlayTxt.text_size = 12
overlayTxt.text = "Frame_seq : {}".format(frame)
det_boxes = BoundingBoxArray() #3D Boxes with JSK
det_boxes.header = header
# Receive each objects info in this frame
for object_info in add_MarkerArray.markers:
#extract info [ frame,type(label),tx,ty,tz,h,w,l,ry ]
if object_info.ns == "/detection/lidar_detector/box_markers":
tx = object_info.pose.position.x
ty = object_info.pose.position.y
tz = object_info.pose.position.z
l = object_info.scale.x
w = object_info.scale.y
h = object_info.scale.z
quaternion_xyzw = [object_info.pose.orientation.x, object_info.pose.orientation.y, \
object_info.pose.orientation.z, object_info.pose.orientation.w]
rz = tf.transformations.euler_from_quaternion(
quaternion_xyzw)[2]
obj_box_info = np.append(
obj_box_info,
[[-ty, -tz, tx - 0.27, h, w, l, -rz + np.pi / 2]],
axis=0)
size_det = Vector3(l, w, h)
det_box = BoundingBox()
det_box.header = header
det_box.pose.position = Point(tx, ty, tz)
q_det_box = tf.transformations.quaternion_from_euler(
0.0, 0.0, rz) # 어쩔 수 없이 끝단에서만 90도 돌림
det_box.pose.orientation = Quaternion(*q_det_box)
det_box.dimensions = size_det
det_boxes.boxes.append(det_box)
elif object_info.ns == "/detection/lidar_detector/label_markers":
label = object_info.text.strip()
if label == '':
label = 'None'
obj_label_info = np.append(obj_label_info, [[frame, label]],
axis=0)
dets = np.concatenate((obj_label_info, obj_box_info), axis=1)
self.pub_det_markerarray.publish(det_boxes)
del current_id_list[:]
# All Detection Info in one Frame
bboxinfo = dets[dets[:, 0] == str(frame),
2:9] # [ tx, ty, tz, h, w, l, rz ]
additional_info = dets[dets[:, 0] == str(frame), 0:2] # frame, labe
reorder = [3, 4, 5, 0, 1, 2,
6] # [tx,ty,tz,h,w,l,ry] -> [h,w,l,tx,ty,tz,theta]
reorder_back = [3, 4, 5, 0, 1, 2,
6] # [h,w,l,tx,ty,tz,theta] -> [tx,ty,tz,h,w,l,ry]
reorder2velo = [2, 0, 1, 3, 4, 5, 6]
bboxinfo = bboxinfo[:,
reorder] # reorder bboxinfo parameter [h,w,l,x,y,z,theta]
bboxinfo = bboxinfo.astype(np.float64)
dets_all = {'dets': bboxinfo, 'info': additional_info}
# ObjectTracking from Detection
trackers = self.mot_tracker.update(dets_all) # h,w,l,x,y,z,theta
trackers_bbox = trackers[:, 0:7]
trackers_info = trackers[:, 7:10] # id, frame, label
trackers_bbox = trackers_bbox[:,
reorder_back] # reorder_back bboxinfo parameter [tx,ty,tz,h,w,l,ry]
trackers_bbox = trackers_bbox[:,
reorder2velo] # reorder coordinate system cam to velo
trackers_bbox = trackers_bbox.astype(np.float64)
trackers_bbox[:, 0] = trackers_bbox[:, 0]
trackers_bbox[:, 1] = trackers_bbox[:, 1] * -1
trackers_bbox[:, 2] = trackers_bbox[:, 2] * -1
trackers_bbox[:, 6] = trackers_bbox[:, 6] * -1
# for문을 통해 각 objects들의 정보를 추출하여 사용
for b, info in zip(trackers_bbox, trackers_info):
bbox = BoundingBox()
bbox.header = header
# parameter 뽑기 [tx,ty,tz,h,w,l,rz]
tx_trk, ty_trk, tz_trk = float(b[0]), float(b[1]), float(b[2])
rz_trk = float(b[6])
size_trk = Vector3(float(b[5]), float(b[4]), float(b[3]))
obj_id = info[0]
label_trk = info[2]
bbox_color = colorCategory20(int(obj_id))
odom_mat = get_odom(self.tf2, "velo_link", "map")
xyz = np.array(b[:3]).reshape(1, -1)
points = np.array((0, 3), float)
if odom_mat is not None:
points = get_transformation(odom_mat, xyz)
# 이전 x frame 까지 지나온 points들을 저장하여 반환하는 함수
# obj_id와 bbox.label은 단지 type차이만 날뿐 같은 데이터
# path_points_list = points_path(tx_trk, ty_trk, tz_trk, obj_id)
path_points_list = points_path(points[0, 0], points[0, 1],
points[0, 2], obj_id)
map_header = copy.deepcopy(header)
map_header.frame_id = "/map"
bbox_color = colorCategory20(int(obj_id))
path_marker = Marker(
type=Marker.LINE_STRIP,
id=int(obj_id),
lifetime=rospy.Duration(0.5),
# pose=Pose(Point(0,0,0), Quaternion(0, 0, 0, 1)), # origin point position
scale=Vector3(0.1, 0.0, 0.0), # line width
header=map_header,
color=bbox_color)
path_marker.points = path_points_list
obj_path_markers.markers.append(path_marker)
# Tracker들의 BoundingBoxArray 설정
bbox.pose.position = Point(tx_trk, ty_trk, tz_trk / 2.0)
q_box = tf.transformations.quaternion_from_euler(
0.0, 0.0, rz_trk + np.pi / 2) # 어쩔 수 없이 끝단에서만 90도 돌림
bbox.pose.orientation = Quaternion(*q_box)
bbox.dimensions = size_trk
bbox.label = int(obj_id)
boxes.boxes.append(bbox)
picto_text = Pictogram()
picto_text.header = header
picto_text.mode = Pictogram.STRING_MODE
picto_text.pose.position = Point(tx_trk, ty_trk, -tz_trk)
# q = tf.transformations.quaternion_from_euler(0.7, 0.0, -0.7)
picto_text.pose.orientation = Quaternion(0.0, -0.5, 0.0, 0.5)
picto_text.size = 4
picto_text.color = std_msgs.msg.ColorRGBA(1, 1, 1, 1)
picto_text.character = label_trk + ' ' + str(bbox.label)
texts.pictograms.append(picto_text)
# GPS sensor values
oxtLinear = TwistStamped.twist.linear
# oxtLinear = TwistStamped.twist.linear
# Tracker들의 속도 추정
obj_velo, dx_t, dy_t, dz_t = obj_velocity([tx_trk, ty_trk, tz_trk],
bbox.label, oxtLinear)
if obj_velo != None:
obj_velo = np.round_(obj_velo, 1) # m/s
obj_velo = obj_velo * 3.6 # km/h
obj_velo_scale = convert_velo2scale(obj_velo)
# # Tracker들의 Orientation
q_ori = tf.transformations.quaternion_from_euler(
0.0, 0.0, rz_trk + np.pi / 2) # 어쩔 수 없이 끝단에서만 90도 돌림
obj_ori_arrow = Marker(
type=Marker.ARROW,
id=bbox.label,
lifetime=rospy.Duration(0.2),
pose=Pose(Point(tx_trk, ty_trk, tz_trk / 2.0),
Quaternion(*q_ori)),
scale=Vector3(obj_velo_scale, 0.5, 0.5),
header=header,
# color=ColorRGBA(0.0, 1.0, 0.0, 0.8))
color=bbox_color)
obj_ori_arrows.markers.append(obj_ori_arrow)
obj_velo_marker = Marker(type=Marker.TEXT_VIEW_FACING,
id=bbox.label,
lifetime=rospy.Duration(0.5),
pose=Pose(Point(tx_trk, ty_trk, tz_trk),
Quaternion(0.0, -0.5, 0.0,
0.5)),
scale=Vector3(1.5, 1.5, 1.5),
header=header,
color=ColorRGBA(1.0, 1.0, 1.0, 1.0),
text="{}km/h".format(obj_velo))
velocity_markers.markers.append(obj_velo_marker)
current_id_list.append(bbox.label)
# Warning object line
warning_line = Marker(
type=Marker.LINE_LIST,
id=int(obj_id),
lifetime=rospy.Duration(0.2),
pose=Pose(Point(0, 0, 0),
Quaternion(0, 0, 0, 1)), # origin point position
scale=Vector3(0.2, 0.0, 0.0), # line width
header=header,
color=ColorRGBA(1.0, 0.0, 0.0, 1.0))
d = dist_from_objBbox(tx_trk, ty_trk, tz_trk, size_trk.x,
size_trk.y, size_trk.z)
if d < MIN_WARNING_DIST:
warning_line.points = Point(tx_trk, ty_trk,
tz_trk), Point(0.0, 0.0, 0.0)
warning_line_markers.markers.append(warning_line)
# Change Outer Circle Color
outer_circle_color = ColorRGBA(1.0 * 25 / 255, 1.0, 0.0, 1.0)
if len(warning_line_markers.markers) > 0:
outer_circle_color = ColorRGBA(1.0 * 255 / 255, 1.0 * 0 / 255,
1.0 * 0 / 255, 1.0)
# ego_vehicle's warning boundary
outer_circle = Marker(
type=Marker.CYLINDER,
id=int(obj_id),
lifetime=rospy.Duration(0.5),
pose=Pose(Point(0.0, 0.0, -2.0), Quaternion(0, 0, 0, 1)),
scale=Vector3(8.0, 8.0, 0.1), # line width
header=header,
color=outer_circle_color)
inner_circle = Marker(
type=Marker.CYLINDER,
id=int(obj_id),
lifetime=rospy.Duration(0.5),
pose=Pose(Point(0.0, 0.0, -1.8), Quaternion(0, 0, 0, 1)),
scale=Vector3(7.0, 7.0, 0.2), # line width
header=header,
color=ColorRGBA(0.22, 0.22, 0.22, 1.0))
# ego-vehicle velocity
selfvelo = np.sqrt(oxtLinear.x**2 + oxtLinear.y**2 + oxtLinear.z**2)
selfvelo = np.round_(selfvelo, 1) # m/s
selfvelo = selfvelo * 3.6 # km/h
oxtAngular = TwistStamped.twist.angular
q_gps = tf.transformations.quaternion_from_euler(
oxtAngular.x, oxtAngular.y, oxtAngular.z)
# # ego-vehicle 사진 출력
ego_car = Marker(type=Marker.MESH_RESOURCE,
id=0,
lifetime=rospy.Duration(0.5),
pose=Pose(Point(0.0, 0.0, -1.8),
Quaternion(0, 0, 0, 1)),
scale=Vector3(1.5, 1.5, 1.5),
header=header,
action=Marker.ADD,
mesh_resource=CAR_DAE_PATH,
color=ColorRGBA(1.0, 1.0, 1.0, 1.0))
# Self ego Velocity
text_marker = Marker(type=Marker.TEXT_VIEW_FACING,
id=0,
lifetime=rospy.Duration(0.5),
pose=Pose(Point(-7.0, 0.0, 0.0),
Quaternion(0, 0, 0, 1)),
scale=Vector3(1.5, 1.5, 1.5),
header=header,
color=ColorRGBA(1.0, 1.0, 1.0, 1.0),
text="{}km/h".format(selfvelo))
for i in prior_trk_xyz.keys():
if i not in current_id_list:
prior_trk_xyz.pop(i)
self.pub_frame_seq.publish(overlayTxt)
self.pub_boxes.publish(boxes)
self.pub_pictograms.publish(texts)
self.pub_selfvelo_text.publish(text_marker)
# self.pub_selfveloDirection.publish(arrow_marker)
self.pub_objs_ori.publish(obj_ori_arrows)
self.pub_objs_velo.publish(velocity_markers)
self.pub_path.publish(obj_path_markers)
self.pub_warning_lines.publish(warning_line_markers)
self.pub_ego_outCircle.publish(outer_circle)
self.pub_ego_innerCircle.publish(inner_circle)
self.pub_ego_car.publish(ego_car)
def readXML(file):
tree = ET.parse(file)
root = tree.getroot()
item = root.findall('./tracklets/item')
d = {}
boxes_2d = {}
pictograms = {}
for i, v in enumerate(item):
h = float(v.find('h').text)
w = float(v.find('w').text)
l = float(v.find('l').text)
frame = int(v.find('first_frame').text)
size = Vector3(l, w, h)
label = v.find('objectType').text
pose = v.findall('./poses/item')
for j, p in enumerate(pose):
tx = float(p.find('tx').text)
ty = float(p.find('ty').text)
tz = float(p.find('tz').text)
rz = float(p.find('rz').text)
occlusion = float(p.find('occlusion').text)
q = tf.transformations.quaternion_from_euler(0.0, 0.0, rz)
b = BoundingBox()
b.pose.position = Vector3(tx, ty, tz / 2.0)
b.pose.orientation = Quaternion(*q)
b.dimensions = size
b.label = i
picto_text = Pictogram()
picto_text.mode = Pictogram.STRING_MODE
picto_text.pose.position = Vector3(tx, ty, -tz / 2.0)
q = tf.transformations.quaternion_from_euler(0.7, 0.0, -0.7)
picto_text.pose.orientation = Quaternion(0.0, -0.5, 0.0, 0.5)
picto_text.size = 5
picto_text.color = std_msgs.msg.ColorRGBA(1, 1, 1, 1)
picto_text.character = label
# Bounding Box corners
corner_x = np.array(
[l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2])
corner_y = np.array(
[w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2])
corner_z = np.array([0, 0, 0, 0, h, h, h, h])
rz = wrapToPi(rz)
###################
#create box on origin, then translate and rotate according to pose. finally, project into 2D image
# Rotate and translate 3D bounding box in velodyne coordinate system
R = np.array([[math.cos(rz), -math.sin(rz), 0],
[math.sin(rz), math.cos(rz), 0], [0, 0, 1]])
corner_3d = np.dot(R, np.array([corner_x, corner_y, corner_z]))
#Translate
corner_3d[0, :] = corner_3d[0, :] + tx
corner_3d[1, :] = corner_3d[1, :] + ty
corner_3d[2, :] = corner_3d[2, :] + tz
#Project to 2D
low_row = np.vstack(
[corner_3d,
np.ones(corner_3d.shape[1], dtype=np.float)])
corner_3d = np.dot(np.asarray(rt_matrix), low_row)
#################################
#Create an orientation vector
orientation_3d = np.dot(R, np.array([[0, 0.7 * l], [0, 0], [0,
0]]))
#Translate
orientation_3d[0, :] = orientation_3d[0, :] + tx
orientation_3d[1, :] = orientation_3d[1, :] + ty
orientation_3d[2, :] = orientation_3d[2, :] + tz
#Project
low_row = np.vstack([
orientation_3d,
np.ones(orientation_3d.shape[1], dtype=np.float)
])
orientation_3d = np.dot(rt_matrix, low_row)
K = np.asarray(cam_to_cam['P_rect_02']).reshape(3, 4)
K = K[:3, :3]
corners_2d = projectToImage(corner_3d, K)
orientation_2d = projectToImage(orientation_3d, K)
x1 = min(corners_2d[0, :])
x2 = max(corners_2d[0, :])
y1 = min(corners_2d[1, :])
y2 = max(corners_2d[1, :])
bbox_2d = image_rect()
bbox_2d.score = -10.0
if ((label == 'Car' or label == 'Truck' or label == 'Van')
and np.any(corner_3d[2, :] >= 0.5)) and (
np.any(orientation_3d[2, :] >= 0.5) and x1 >= 0
and x2 >= 0 and y1 > 0 and y2 >= 0 and occlusion < 2):
bbox_2d.x = x1
bbox_2d.y = y1
bbox_2d.width = x2 - x1
bbox_2d.height = y2 - y1
bbox_2d.score = 1.0
if d.has_key(frame + j) == True:
d[frame + j].append(b)
boxes_2d[frame + j].append(bbox_2d)
pictograms[frame + j].append(picto_text)
else:
d[frame + j] = [b]
boxes_2d[frame + j] = [bbox_2d]
pictograms[frame + j] = [picto_text]
return d, boxes_2d, pictograms
def callback(self, msg):
target_frame = self.frame_id
input_frame = msg.header.frame_id
transform = self.tf_buffer.lookup_transform(
target_frame,
input_frame, #source frame
rospy.Time(0),
rospy.Duration(1.0))
print("pose array clipper callback")
posearray = msg.poses
x_min = self.initial_pos[0] - self.dimension_x / 2
x_max = self.initial_pos[0] + self.dimension_x / 2
y_min = self.initial_pos[1] - self.dimension_y / 2
y_max = self.initial_pos[1] + self.dimension_y / 2
z_min = self.initial_pos[2] - self.dimension_z / 2
z_max = self.initial_pos[2] + self.dimension_z / 2
pub_msg = PoseArray()
for input_pose in posearray:
pose_stamped = PoseStamped()
pose_stamped.pose = input_pose
tf_pose = tf2_geometry_msgs.do_transform_pose(
pose_stamped, transform)
x = tf_pose.pose.position.x
y = tf_pose.pose.position.y
z = tf_pose.pose.position.z
if (x >= x_min) and (x <= x_max):
if (y >= y_min) and (y <= y_max):
if (z >= z_min) and (z <= z_max):
pub_msg.poses.append(tf_pose.pose)
pub_msg.header.frame_id = self.frame_id
self.pub_output_poses.publish(pub_msg)
bbox = BoundingBox()
pose = Pose()
pose.position.x = self.initial_pos[0]
pose.position.y = self.initial_pos[1]
pose.position.z = self.initial_pos[2]
bbox.pose = pose
vec = Vector3()
vec.x = self.dimension_x
vec.y = self.dimension_y
vec.z = self.dimension_z
bbox.dimensions = vec
bbox.header.frame_id = self.frame_id
self.pub_clipper_bbox.publish(bbox)
"Clearing /accumulated_heightmap/reset due to change of robot_bbox")
srv = rospy.ServiceProxy("/accumulated_heightmap/reset", EmptySrv)
try:
srv()
except:
rospy.logerr("Failed to reset /accumulated_heightmap")
bbox_msg = BoundingBox()
bbox_msg.header.frame_id = 'body_on_odom'
if transport_object == "push_cart":
position = Point(0.75, 0.0, 1.25)
orientation = Quaternion(0.0, 0.0, 0.0, 0.0)
dimensions = Vector3(1.5, 1.2, 3.0)
elif transport_object == "wheelbarrow":
position = Point(1.0, 0.0, 1.25)
orientation = Quaternion(0.0, 0.0, 0.0, 0.0)
dimensions = Vector3(2.2, 1.5, 3.0)
else:
position = Point(0.0, 0.0, 1.25)
orientation = Quaternion(0.0, 0.0, 0.0, 0.0)
dimensions = Vector3(0.5, 1.0, 3.0)
bbox_msg.pose.position = position
bbox_msg.pose.orientation = orientation
bbox_msg.dimensions = dimensions
while not rospy.is_shutdown():
p.publish(bbox_msg)
r.sleep()
def readXML(file):
tree = ET.parse(file)
root = tree.getroot()
item = root.findall('./tracklets/item')
d = {}
boxes_2d = {}
pictograms = {}
for i, v in enumerate(item):
h = float(v.find('h').text)
w = float(v.find('w').text)
l = float(v.find('l').text)
frame = int(v.find('first_frame').text)
size = Vector3(l, w, h)
label = v.find('objectType').text
pose = v.findall('./poses/item')
for j, p in enumerate(pose):
tx = float(p.find('tx').text)
ty = float(p.find('ty').text)
tz = float(p.find('tz').text)
rz = float(p.find('rz').text)
occlusion = float(p.find('occlusion').text)
q = tf.transformations.quaternion_from_euler(0.0, 0.0, rz)
b = BoundingBox()
b.pose.position = Vector3(tx, ty, tz/2.0)
b.pose.orientation = Quaternion(*q)
b.dimensions = size
b.label = i
picto_text = Pictogram()
picto_text.mode = Pictogram.STRING_MODE
picto_text.pose.position = Vector3(tx, ty, -tz/2.0)
q = tf.transformations.quaternion_from_euler(0.7, 0.0, -0.7)
picto_text.pose.orientation = Quaternion(0.0, -0.5, 0.0, 0.5)
picto_text.size = 5
picto_text.color = std_msgs.msg.ColorRGBA(1, 1, 1, 1)
picto_text.character = label
# Bounding Box corners
corner_x = np.array([l/2, l/2, -l/2, -l/2, l/2, l/2, -l/2, -l/2])
corner_y = np.array([w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2])
corner_z = np.array([0, 0, 0, 0, h, h, h, h])
rz = wrapToPi(rz)
###################
#create box on origin, then translate and rotate according to pose. finally, project into 2D image
# Rotate and translate 3D bounding box in velodyne coordinate system
R = np.array([ [math.cos(rz), -math.sin(rz), 0],
[math.sin(rz), math.cos(rz), 0],
[0, 0, 1]])
corner_3d = np.dot(R,np.array([corner_x, corner_y, corner_z]))
#Translate
corner_3d[0,:] = corner_3d[0,:] + tx
corner_3d[1,:] = corner_3d[1,:] + ty
corner_3d[2,:] = corner_3d[2,:] + tz
#Project to 2D
low_row = np.vstack([corner_3d, np.ones(corner_3d.shape[1], dtype=np.float)])
corner_3d = np.dot(np.asarray(rt_matrix), low_row)
#################################
#Create an orientation vector
orientation_3d = np.dot( R, np.array([[0,0.7*l],[0,0],[0,0]]) )
#Translate
orientation_3d[0,:] = orientation_3d[0,:] + tx
orientation_3d[1,:] = orientation_3d[1,:] + ty
orientation_3d[2,:] = orientation_3d[2,:] + tz
#Project
low_row = np.vstack([orientation_3d, np.ones(orientation_3d.shape[1], dtype=np.float)])
orientation_3d = np.dot(rt_matrix, low_row)
K = np.asarray(cam_to_cam['P_rect_02']).reshape(3,4)
K = K[:3,:3]
corners_2d = projectToImage(corner_3d, K)
orientation_2d = projectToImage(orientation_3d, K)
x1 = min(corners_2d[0,:])
x2 = max(corners_2d[0,:])
y1 = min(corners_2d[1,:])
y2 = max(corners_2d[1,:])
bbox_2d = ImageRect()
bbox_2d.score = -10.0
if ( (label == 'Car' or label=='Truck' or label=='Van') and np.any(corner_3d[2,:]>=0.5)) and (np.any(orientation_3d[2,:]>=0.5) and x1>=0 and x2>=0 and y1>0 and y2>=0 and occlusion <2):
bbox_2d.x = x1
bbox_2d.y = y1
bbox_2d.width = x2-x1
bbox_2d.height = y2-y1
bbox_2d.score = 1.0
if d.has_key(frame + j) == True:
d[frame + j].append(b)
boxes_2d[frame + j].append(bbox_2d)
pictograms[frame + j].append(picto_text)
else:
d[frame + j] = [b]
boxes_2d[frame + j] = [bbox_2d]
pictograms[frame + j]= [picto_text]
return d, boxes_2d, pictograms
import rospy
from jsk_recognition_msgs.msg import BoundingBox
from jsk_recognition_msgs.msg import BoundingBoxArray
from geometry_msgs.msg import Point, Quaternion, Vector3
rospy.init_node("robot_bbox_sf_wh_publisher", anonymous=True)
p = rospy.Publisher("/robot_bbox_sf_wh", BoundingBoxArray, queue_size=10)
r = rospy.Rate(100)
# BODY filter
body_bbox_msg = BoundingBox()
body_bbox_msg.header.frame_id = 'BODY'
body_bbox_msg.pose.position = Point(0.0, 0.0, 0.0)
body_bbox_msg.pose.orientation = Quaternion(0.0, 0.0, 0.0, 0.0)
body_bbox_msg.dimensions = Vector3(0.8, 1.6, 1.6)
# Right hand filter
rh_bbox_msg = BoundingBox()
rh_bbox_msg.header.frame_id = 'R_thk_palm'
rh_bbox_msg.pose.position = Point(0.15, 0.0, 0.0)
rh_bbox_msg.pose.orientation = Quaternion(0.0, 0.0, 0.0, 0.0)
rh_bbox_msg.dimensions = Vector3(0.3, 0.3, 0.15)
# Left hand filter
lh_bbox_msg = BoundingBox()
lh_bbox_msg.header.frame_id = 'L_thk_palm'
lh_bbox_msg.pose.position = Point(0.15, 0.0, 0.0)
lh_bbox_msg.pose.orientation = Quaternion(0.0, 0.0, 0.0, 0.0)
lh_bbox_msg.dimensions = Vector3(0.3, 0.3, 0.15)
