def publish_visible(self, *args, **kwargs):
features = self.visible_landmarks()[0]
if not features.shape[0]: return
features = np.array(np.hstack((features, np.zeros((features.shape[0], 1)))), order='C')
# Convert north-east-z to x-y-z
vehicle_xyz = self.vehicle.position
relative_position = girona500.feature_relative_position(vehicle_xyz, self.vehicle.orientation, features)
# Convert xyz to PointCloud message
pcl_msg = pointclouds.xyz_array_to_pointcloud2(relative_position,
rospy.Time.now(), "slamsim")
# and publish visible landmarks
self.pcl_publisher.publish(pcl_msg)
print "Absolute:"
print features
"""
def extract_clusters_f(self):
clusters = rospy.wait_for_message(self.clusters_topic, MarkerArray);
rospy.loginfo("Clusters %d", clusters.markers.__len__())
cluster_list = []
for i in range(clusters.markers.__len__()):
# rospy.loginfo("Clusters' header %s", clusters.markers[i].header.frame_id)
# rospy.loginfo("Clusters' points %s", clusters.markers[i].points.__len__())
arr_xyz = []
for points in range(clusters.markers[i].points.__len__()):
arr_xyz.append([clusters.markers[i].points[points].x, clusters.markers[i].points[points].y, clusters.markers[i].points[points].z])
pc = PointCloud2()
pc = pointclouds.xyz_array_to_pointcloud2(np.asarray([arr_xyz]), clusters.markers[i].header.stamp, clusters.markers[i].header.frame_id)
# rospy.sleep(1.0)
self.pub.publish(pc)
cluster_list.append(pc)
return cluster_list
def callback(self, data):
rospy.loginfo("Objects %d", data.objects.__len__())
table_corners = []
# obtain table_offset and table_pose
to = rospy.wait_for_message(self.table_topic, MarkerArray);
# obtain Table corners ...
rospy.loginfo("Tables hull size %d", to.markers.__len__())
if not to.markers:
rospy.loginfo("No tables detected")
return
else:
# NB - D says that ORK has this set to filter based on height.
# IIRC, it's 0.6-0.8m above whatever frame is set as the floor
point_array_size = 4 # for the 4 corners of the bounding box
for i in range (0, point_array_size):
p = Point()
p.x = to.markers[0].points[i].x
p.y = to.markers[0].points[i].y
p.z = to.markers[0].points[i].z
table_corners.append(p)
# this is a table pose at the edge close to the robot, in the center of x axis
table_pose = PoseStamped()
table_pose.header = to.markers[0].header
table_pose.pose = to.markers[0].pose
# Determine table dimensions
rospy.loginfo('calculating table pose bounding box in frame: %s' % table_pose.header.frame_id)
min_x = sys.float_info.max
min_y = sys.float_info.max
max_x = -sys.float_info.max
max_y = -sys.float_info.max
for i in range (table_corners.__len__()):
if table_corners[i].x > max_x:
max_x = table_corners[i].x
if table_corners[i].y > max_y:
max_y = table_corners[i].y
if table_corners[i].x < min_x:
min_x = table_corners[i].x
if table_corners[i].y < min_y:
min_y = table_corners[i].y
table_dim = Point()
# TODO: if we don't (can't!) compute the height, should we at least give it non-zero depth?
# (would also require shifting the observed centroid down by table_dim.z/2)
table_dim.z = 0.0
table_dim.x = abs(max_x - min_x)
table_dim.y = abs(max_y - min_y)
print "Dimensions: ", table_dim.x, table_dim.y
# Temporary frame used for transformations
table_link = 'table_link'
# centroid of a table in table_link frame
centroid = PoseStamped()
centroid.header.frame_id = table_link
centroid.header.stamp = table_pose.header.stamp
centroid.pose.position.x = (max_x + min_x)/2.
centroid.pose.position.y = (max_y + min_y)/2.
centroid.pose.position.z = 0.0
centroid.pose.orientation.x = 0.0
centroid.pose.orientation.y = 0.0
centroid.pose.orientation.z = 0.0
centroid.pose.orientation.w = 1.0
# generate transform from table_pose to our newly-defined table_link
tt = TransformStamped()
tt.header = table_pose.header
tt.child_frame_id = table_link
tt.transform.translation = table_pose.pose.position
tt.transform.rotation = table_pose.pose.orientation
self.tl.setTransform(tt)
self.tl.waitForTransform(table_link, table_pose.header.frame_id, table_pose.header.stamp, rospy.Duration(3.0))
if self.tl.canTransform(table_pose.header.frame_id, table_link, table_pose.header.stamp):
centroid_table_pose = self.tl.transformPose(table_pose.header.frame_id, centroid)
self.pose_pub.publish(centroid_table_pose)
else:
rospy.logwarn("No transform between %s and %s possible",table_pose.header.frame_id, table_link)
return
# transform each object into desired frame; add to list of clusters
cluster_list = []
for i in range (data.objects.__len__()):
rospy.loginfo("Point clouds %s", data.objects[i].point_clouds.__len__())
pc = PointCloud2()
pc = data.objects[i].point_clouds[0]
arr = pointclouds.pointcloud2_to_array(pc, 1)
arr_xyz = pointclouds.get_xyz_points(arr)
arr_xyz_trans = []
for j in range (arr_xyz.__len__()):
ps = PointStamped();
ps.header.frame_id = table_link
ps.header.stamp = table_pose.header.stamp
ps.point.x = arr_xyz[j][0]
ps.point.y = arr_xyz[j][1]
ps.point.z = arr_xyz[j][2]
if self.tl.canTransform(table_pose.header.frame_id, table_link, table_pose.header.stamp):
ps_in_kinect_frame = self.tl.transformPoint(table_pose.header.frame_id, ps)
else:
rospy.logwarn("No transform between %s and %s possible",table_pose.header.frame_id, table_link)
return
arr_xyz_trans.append([ps_in_kinect_frame.point.x, ps_in_kinect_frame.point.y, ps_in_kinect_frame.point.z])
pc_trans = PointCloud2()
pc_trans = pointclouds.xyz_array_to_pointcloud2(np.asarray([arr_xyz_trans]),
table_pose.header.stamp, table_pose.header.frame_id)
self.pub.publish(pc_trans)
cluster_list.append(pc_trans)
rospy.loginfo("cluster size %d", cluster_list.__len__())
# finally - save all data in the object that'll be sent in response to actionserver requests
with self.result_lock:
self._result.objects = cluster_list
self._result.table_dims = table_dim
self._result.table_pose = centroid_table_pose
self.has_data = True
def callback(self, data):
rospy.loginfo("Recognized objects %d", data.objects.__len__())
table_corners = []
# obtain table_offset and table_pose
to = rospy.wait_for_message(self.table_topic, MarkerArray)
# obtain Table corners ...
rospy.loginfo("Tables hull size %d", to.markers.__len__())
if not to.markers:
rospy.loginfo("No tables detected")
return
else:
# NB - D says that ORK has this set to filter based on height.
# IIRC, it's 0.6-0.8m above whatever frame is set as the floor
point_array_size = 4 # for the 4 corners of the bounding box
for i in range (0, point_array_size):
p = Point()
p.x = to.markers[0].points[i].x
p.y = to.markers[0].points[i].y
p.z = to.markers[0].points[i].z
table_corners.append(p)
# this is a table pose at the edge close to the robot, in the center of x axis
table_pose = PoseStamped()
table_pose.header = to.markers[0].header
table_pose.pose = to.markers[0].pose
# Determine table dimensions
rospy.loginfo('calculating table pose bounding box in frame: %s' % table_pose.header.frame_id)
min_x = sys.float_info.max
min_y = sys.float_info.max
max_x = -sys.float_info.max
max_y = -sys.float_info.max
for i in range (table_corners.__len__()):
if table_corners[i].x > max_x:
max_x = table_corners[i].x
if table_corners[i].y > max_y:
max_y = table_corners[i].y
if table_corners[i].x < min_x:
min_x = table_corners[i].x
if table_corners[i].y < min_y:
min_y = table_corners[i].y
table_dim = Point()
# TODO: if we don't (can't!) compute the height, should we at least give it non-zero depth?
# (would also require shifting the observed centroid down by table_dim.z/2)
table_dim.z = 0.0
table_dim.x = abs(max_x - min_x)
table_dim.y = abs(max_y - min_y)
print "Dimensions: ", table_dim.x, table_dim.y
# Temporary frame used for transformations
table_link = 'table_link'
# centroid of a table in table_link frame
centroid = PoseStamped()
centroid.header.frame_id = table_link
centroid.header.stamp = table_pose.header.stamp
centroid.pose.position.x = (max_x + min_x)/2.
centroid.pose.position.y = (max_y + min_y)/2.
centroid.pose.position.z = 0.0
centroid.pose.orientation.x = 0.0
centroid.pose.orientation.y = 0.0
centroid.pose.orientation.z = 0.0
centroid.pose.orientation.w = 1.0
# generate transform from table_pose to our newly-defined table_link
tt = TransformStamped()
tt.header = table_pose.header
tt.child_frame_id = table_link
tt.transform.translation = table_pose.pose.position
tt.transform.rotation = table_pose.pose.orientation
self.tl.setTransform(tt)
self.tl.waitForTransform(table_link, table_pose.header.frame_id, table_pose.header.stamp, rospy.Duration(3.0))
if self.tl.canTransform(table_pose.header.frame_id, table_link, table_pose.header.stamp):
centroid_table_pose = self.tl.transformPose(table_pose.header.frame_id, centroid)
self.pose_pub.publish(centroid_table_pose)
else:
rospy.logwarn("No transform between %s and %s possible",table_pose.header.frame_id, table_link)
return
# transform each object into desired frame; add to list of clusters
cluster_list = []
object_list = []
#if only clusters on the table should be extracted
if self.extract_clusters:
cluster_list = self.extract_clusters_f()
#else try to recognize objects
if self.recognize_objects:
for i in range (data.objects.__len__()):
# rospy.loginfo("Point clouds %s", data.objects[i].point_clouds.__len__())
pc = PointCloud2()
pc = data.objects[i].point_clouds[0]
arr = pointclouds.pointcloud2_to_array(pc, 1)
arr_xyz = pointclouds.get_xyz_points(arr)
arr_xyz_trans = []
for j in range (arr_xyz.__len__()):
ps = PointStamped();
ps.header.frame_id = table_link
ps.header.stamp = table_pose.header.stamp
ps.point.x = arr_xyz[j][0]
ps.point.y = arr_xyz[j][1]
ps.point.z = arr_xyz[j][2]
if self.tl.canTransform(table_pose.header.frame_id, table_link, table_pose.header.stamp):
ps_in_kinect_frame = self.tl.transformPoint(table_pose.header.frame_id, ps)
else:
rospy.logwarn("No transform between %s and %s possible",table_pose.header.frame_id, table_link)
return
arr_xyz_trans.append([ps_in_kinect_frame.point.x, ps_in_kinect_frame.point.y, ps_in_kinect_frame.point.z])
pc_trans = PointCloud2()
pc_trans = pointclouds.xyz_array_to_pointcloud2(np.asarray([arr_xyz_trans]),
table_pose.header.stamp, table_pose.header.frame_id)
#self.pub.publish(pc_trans)
object_list.append(pc_trans)
rospy.loginfo("object size %d", object_list.__len__())
cluster_centroids = []
object_centroids = []
for cloud in range (cluster_list.__len__()):
cluster_centroids.append(self.compute_centroid(cluster_list[cloud]))
for cloud in range (object_list.__len__()):
object_centroids.append(self.compute_centroid(object_list[cloud]))
recognized_objects = []
indices = []
for centroid in range (cluster_centroids.__len__()):
# dist = self.closest(np_cluster_centroids, np.asarray(cluster_centroids[centroid]))
if object_centroids:
indices = self.do_kdtree(np.asarray(object_centroids), np.asarray(cluster_centroids[centroid]), self.search_radius)
if not indices:
recognized_objects.append(0)
else:
recognized_objects.append(1)
print "recognized objects: ", recognized_objects
# finally - save all data in the object that'll be sent in response to actionserver requests
with self.result_lock:
self._result.objects = cluster_list
self._result.recognized_objects = recognized_objects
self._result.table_dims = table_dim
self._result.table_pose = centroid_table_pose
self.has_data = True
