def pose_msg_callback(self, pose, classes):
bboxes = BoundingBoxArray(header=pose.header)
for p in pose.poses:
b = BoundingBox(header=pose.header)
b.pose = p
bboxes.boxes.append(b)
self.box_msg_callback(bboxes, classes)
def callback(msg):
if not candidate_pose:
return
target_array = BoundingBoxArray()
target_array.header.stamp = msg.header.stamp
target_array.header.frame_id = "world"
target = BoundingBox()
target.header.stamp = msg.header.stamp
target.header.frame_id = "world"
target.pose = candidate_pose
target.dimensions.x = 0.2
target.dimensions.y = 0.2
target.dimensions.z = 0.2
target_array.boxes = [target]
pub_target.publish(target_array)
candidate_array = BoundingBoxArray()
candidate_array.header.stamp = msg.header.stamp
candidate_array.header.frame_id = "world"
for x in [-0.2, -0.1, 0.0, 0.1, 0.2]:
for y in [-0.2, -0.1, 0.0, 0.1, 0.2]:
for z in [-0.2, -0.1, 0.0, 0.1, 0.2]:
candidate = BoundingBox()
candidate.header.stamp = msg.header.stamp
candidate.header.frame_id = "world"
candidate.pose.position.z = 2 + z
candidate.pose.position.x = x
candidate.pose.position.y = y
candidate.pose.orientation.w = 1.0
candidate.dimensions.x = 0.1
candidate.dimensions.y = 0.1
candidate.dimensions.z = 0.1
candidate_array.boxes.append(candidate)
pub_candidate.publish(candidate_array)
def callback(msg):
if not candidate_pose:
return
target_array = BoundingBoxArray()
target_array.header.stamp = msg.header.stamp
target_array.header.frame_id = "world"
target = BoundingBox()
target.header.stamp = msg.header.stamp
target.header.frame_id = "world"
target.pose = candidate_pose
target.dimensions.x = 0.2
target.dimensions.y = 0.2
target.dimensions.z = 0.2
target_array.boxes = [target]
pub_target.publish(target_array)
candidate_array = BoundingBoxArray()
candidate_array.header.stamp = msg.header.stamp
candidate_array.header.frame_id = "world"
for x in [-0.2, -0.1, 0.0, 0.1, 0.2]:
for y in [-0.2, -0.1, 0.0, 0.1, 0.2]:
for z in [-0.2, -0.1, 0.0, 0.1, 0.2]:
candidate = BoundingBox()
candidate.header.stamp = msg.header.stamp
candidate.header.frame_id = "world"
candidate.pose.position.z = 2 + z
candidate.pose.position.x = x
candidate.pose.position.y = y
candidate.pose.orientation.w = 1.0
candidate.dimensions.x = 0.1
candidate.dimensions.y = 0.1
candidate.dimensions.z = 0.1
candidate_array.boxes.append(candidate)
pub_candidate.publish(candidate_array)
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 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 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 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 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 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 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 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 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 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 get_box(msg):
box = BoundingBox()
get_box_pose_srv = rospy.ServiceProxy("/transformable_server_sample/get_pose", GetTransformableMarkerPose)
resp = get_box_pose_srv(target_name=msg.data)
box.pose = resp.pose_stamped.pose
box.header = resp.pose_stamped.header
get_box_dim_srv = rospy.ServiceProxy("/transformable_server_sample/get_dimensions", GetMarkerDimensions)
resp2 = get_box_dim_srv(target_name=msg.data)
box.dimensions.x = resp2.dimensions.x
box.dimensions.y = resp2.dimensions.y
box.dimensions.z = resp2.dimensions.z
return box
def labeled_pose_callback(self, pose_msg):
self.header = pose_msg.header
self.qatm_boxes = BoundingBoxArray()
self.qatm_boxes.header = pose_msg.header
for pose in pose_msg.poses:
tmp_box = BoundingBox()
tmp_box.header = pose_msg.header
tmp_box.pose = pose.pose
tmp_box.dimensions.x = 0.03
tmp_box.dimensions.y = 0.03
tmp_box.dimensions.z = 0.03
tmp_box.label = self.label_lst.index(pose.label)
self.qatm_boxes.boxes.append(tmp_box)
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 get_box(msg):
box = BoundingBox()
get_box_pose_srv = rospy.ServiceProxy(
"/transformable_server_sample/get_pose", GetTransformableMarkerPose)
resp = get_box_pose_srv(target_name=msg.data)
box.pose = resp.pose_stamped.pose
box.header = resp.pose_stamped.header
get_box_dim_srv = rospy.ServiceProxy(
"/transformable_server_sample/get_dimensions", GetMarkerDimensions)
resp2 = get_box_dim_srv(target_name=msg.data)
box.dimensions.x = resp2.dimensions.x
box.dimensions.y = resp2.dimensions.y
box.dimensions.z = resp2.dimensions.z
return box
def _pub_bboxes_callback(self, event):
bbox_array_msg = BoundingBoxArray()
bbox_array_msg.header.frame_id = self.config['boxes'][0]['frame_id']
bbox_array_msg.header.stamp = event.current_real
for box in self.config['boxes']:
bbox_msg = BoundingBox()
pose = self.object_poses[box['name']]
bbox_msg.header.frame_id = bbox_array_msg.header.frame_id
bbox_msg.header.stamp = bbox_array_msg.header.stamp
bbox_msg.pose = pose
dimensions = self.object_dimensions[box['name']]
bbox_msg.dimensions.x = dimensions.x
bbox_msg.dimensions.y = dimensions.y
bbox_msg.dimensions.z = dimensions.z
bbox_array_msg.boxes.append(bbox_msg)
self.pub_bboxes.publish(bbox_array_msg)
def _pub_bboxes_callback(self, event):
bbox_array_msg = BoundingBoxArray()
bbox_array_msg.header.frame_id = self.config['boxes'][0]['frame_id']
bbox_array_msg.header.stamp = event.current_real
for box in self.config['boxes']:
bbox_msg = BoundingBox()
pose = self.object_poses[box['name']]
bbox_msg.header.frame_id = bbox_array_msg.header.frame_id
bbox_msg.header.stamp = bbox_array_msg.header.stamp
bbox_msg.pose = pose
dimensions = self.object_dimensions[box['name']]
bbox_msg.dimensions.x = dimensions.x
bbox_msg.dimensions.y = dimensions.y
bbox_msg.dimensions.z = dimensions.z
bbox_array_msg.boxes.append(bbox_msg)
self.pub_bboxes.publish(bbox_array_msg)
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 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 __init__(self):
self.srv = Server(DynamicParamsConfig,
self.dynamic_reconfigure_callback)
self.sub = rospy.Subscriber(
"~input", PolygonArray, self.polygon_callback)
self.box = BoundingBox()
self.frame = rospy.get_param("~frame", "/base_link")
self.box.header.frame_id = self.frame
self.pub = rospy.Publisher("~output", BoundingBox, queue_size=1)
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 inference_image(self, data):
try:
image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print(e)
return
#if image captured
image_expanded = np.expand_dims(image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes,
num) = self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: image_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
self.category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.9)
if len(boxes[0]) > 0 and scores[0][0] > 0.95:
[min_x, min_y, max_x, max_y] = boxes[0][0]
height, width = image.shape[:2]
# print(min_x * height, min_y * width, max_x * height, max_y * width)
# cv2.rectangle(image, (int(min_y * width), int(min_x * height)), (int(max_y * width), int(max_x * height)), (255, 0, 0), 2)
boundingbox = BoundingBox()
boundingbox.header = data.header
boundingbox.pose.position.x = int(min_y * width)
boundingbox.pose.position.y = int(min_x * height)
boundingbox.dimensions.x = int((max_y - min_y) * width)
boundingbox.dimensions.y = int((max_x - min_x) * height)
# print(boundingbox)
self.pub_boundingbox.publish(boundingbox)
# All the results have been drawn on image. Now display the image.
cv2.imshow('Object detector', image)
if cv2.waitKey(10) == ord('q'):
return
def candidateBoxes(header, model_index):
box_array = BoundingBoxArray()
box_array.header.stamp = header.stamp
box_array.header.frame_id = "odom"
dx = 0.1
for y in np.arange(0.0, 0.6, dx):
for z in np.arange(0.7, 1.0, dx):
for x in np.arange(0.0, -0.5, -dx):
box = BoundingBox()
box.header = box_array.header
box.pose.orientation.w = 1.0
box.pose.position.x = x
box.pose.position.y = y
box.pose.position.z = z
box.dimensions.x = 0.1
box.dimensions.y = 0.1
box.dimensions.z = 0.1
box_array.boxes.append(box)
return box_array
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 candidateBoxes(header, model_index):
box_array = BoundingBoxArray()
box_array.header.stamp = header.stamp
box_array.header.frame_id = "odom"
dx = 0.1
for y in np.arange(0.0, 0.6, dx):
for z in np.arange(0.7, 1.0, dx):
for x in np.arange(0.0, -0.5, -dx):
box = BoundingBox()
box.header = box_array.header
box.pose.orientation.w = 1.0
box.pose.position.x = x
box.pose.position.y = y
box.pose.position.z = z
box.dimensions.x = 0.1
box.dimensions.y = 0.1
box.dimensions.z = 0.1
box_array.boxes.append(box)
return box_array
def LeadCarUpdate(msg):
global lidar_array, filter_boxes
leadc_boxes = BoundingBoxArray()
leadc_boxes.header = filter_boxes.header
compare_boxes = BoundingBoxArray()
compare_boxes.header = filter_boxes.header
compare_boxes.boxes = filter_boxes.boxes[:]
if len(msg.data)==0:
print('no lead car')
else:
rc_x = msg.data[0].pos_x
rc_y = msg.data[0].pos_y
rc_v = msg.data[0].speed
distances = []
for i in range(len(compare_boxes.boxes)):
lidar_x = compare_boxes.boxes[i].pose.position.x
lidar_y = compare_boxes.boxes[i].pose.position.y
distance = np.sqrt((rc_x-lidar_x)**2+(rc_y-lidar_y)**2)
distances.append(distance)
print(min(distances))
if (min(distances)<5):
obj_i = distances.index(min(distances))
leadc_boxes.boxes.append(compare_boxes.boxes[obj_i])
else:
leadc_box = BoundingBox()
leadc_box.header = filter_boxes.boxes[1].header
leadc_box.pose.position.x = rc_x
leadc_box.pose.position.y = rc_y
leadc_box.pose.position.z = .5
leadc_box.pose.orientation.x = 0
leadc_box.pose.orientation.y = 0
leadc_box.pose.orientation.z = 0
leadc_box.pose.orientation.w = 0
leadc_box.dimensions.x = 5
leadc_box.dimensions.y = 5
leadc_box.dimensions.z = 5
leadc_boxes.boxes.append(leadc_box)
pub_leadc.publish(leadc_boxes)
def lidar_callback(self, msg):
if self.model.inference_ctx is None or self.model.inference_ctx.anchor_cache is None:
return
for field in msg.fields:
if field.name == "i" or field.name == "intensity":
intensity_fname = field.name
intensity_dtype = field.datatype
else:
intensity_fname = None
intensity_dtype = None
dtype_list = self._fields_to_dtype(msg.fields, msg.point_step)
pc_arr = np.frombuffer(msg.data, dtype_list)
if intensity_fname:
pc_arr = structured_to_unstructured(
pc_arr[["x", "y", "z", intensity_fname]]).copy()
if intensity_dtype == 2:
pc_arr[:, 3] = pc_arr[:, 3] / 255
else:
pc_arr = structured_to_unstructured(pc_arr[["x", "y", "z"]]).copy()
pc_arr = np.hstack((pc_arr, np.zeros((pc_arr.shape[0], 1))))
lidar_boxes = self.model.predcit(pc_arr)
num_detects = len(lidar_boxes)
arr_bbox = BoundingBoxArray()
for i in range(num_detects):
bbox = BoundingBox()
bbox.header.frame_id = msg.header.frame_id
bbox.header.stamp = rospy.Time.now()
bbox.pose.position.x = float(lidar_boxes[i][0])
bbox.pose.position.y = float(lidar_boxes[i][1])
bbox.pose.position.z = float(
lidar_boxes[i][2]) + float(lidar_boxes[i][5]) / 2
bbox.dimensions.x = float(lidar_boxes[i][3]) # width
bbox.dimensions.y = float(lidar_boxes[i][4]) # length
bbox.dimensions.z = float(lidar_boxes[i][5]) # height
q = Quaternion(axis=(0, 0, 1), radians=float(lidar_boxes[i][6]))
bbox.pose.orientation.x = q.x
bbox.pose.orientation.y = q.y
bbox.pose.orientation.z = q.z
bbox.pose.orientation.w = q.w
arr_bbox.boxes.append(bbox)
arr_bbox.header.frame_id = msg.header.frame_id
arr_bbox.header.stamp = rospy.Time.now()
print("Number of detections: {}".format(num_detects))
self.pub_bbox.publish(arr_bbox)
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 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
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 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 listen_transform(self, parent_frame, child_frame):
box = BoundingBox()
try:
self.listener.waitForTransform(
parent_frame, child_frame, rospy.Time(0), rospy.Duration(3.0))
(trans, rot) = self.listener.lookupTransform(
parent_frame, child_frame, rospy.Time(0))
box.pose.position = Point(trans[0], trans[1], trans[2])
box.pose.orientation = Quaternion(rot[0], rot[1], rot[2], rot[3])
return box
except:
rospy.logwarn('cannot lookup transform')
return box
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
#!/usr/bin/env python
import rospy
from jsk_recognition_msgs.msg import BoundingBoxArray, BoundingBox
from tf.transformations import *
rospy.init_node("bbox_sample")
pub = rospy.Publisher("bbox", BoundingBoxArray)
r = rospy.Rate(24)
counter = 0
while not rospy.is_shutdown():
box_a = BoundingBox()
box_b = BoundingBox()
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