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 people_msg_callback(self, people, classes): bboxes = BoundingBoxArray(header=people.header) for p in people.poses: b = BoundingBox() for i, n in enumerate(p.limb_names): if n in ["Neck", "Nose", "REye", "LEye", "REar", "LEar"]: b.header = people.header b.pose = p.poses[i] break if not b.header.frame_id: b.header = people.header b.pose = b.poses[0] self.box_msg_callback(bboxes, classes)
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 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 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 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 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 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 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 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 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 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 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 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 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 talker(): pub = rospy.Publisher('/segmentation_decomposer/boxes', BoundingBoxArray, queue_size=10) rospy.init_node('segmentation_talker', anonymous=True) rate = rospy.Rate(10) while not rospy.is_shutdown(): b = BoundingBoxArray() b.header.frame_id = "/head_mount_kinect_rgb_optical_frame" b.header.stamp = rospy.get_rostime() b1 = BoundingBox() # b1.header.frame_id = "/head_mount_kinect_rgb_optical_frame" # b1.header.stamp = rospy.get_rostime() b1.header = b.header b1.pose.position.x = 0.1 b1.pose.position.y = 0.1 b1.pose.position.z = 0.1 b1.dimensions.x = 0.15 b1.dimensions.y = 0.06 b1.dimensions.z = 0.06 b.boxes = [b1] pub.publish(b) rate.sleep()
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)