def _apply(self, feature_msg, label_msg):
bridge = cv_bridge.CvBridge()
label = bridge.imgmsg_to_cv2(label_msg)
desc = np.array(feature_msg.descriptors)
desc = desc.reshape((-1, feature_msg.descriptor_dim))
pos = np.array(feature_msg.positions)
pos = pos.reshape((-1, 2))
if label.sum() == 0:
jsk_logdebug('Skip image with only background label')
return
decomposed = decompose_descriptors_with_label(descriptors=desc,
positions=pos,
label_img=label,
skip_zero_label=True)
X = np.array(decomposed.values())
if X.size == 0:
return
X = self.bof.transform(X)
normalize(X, copy=False)
self._pub.publish(
VectorArray(
header=feature_msg.header,
vector_dim=X.shape[1],
data=X.reshape(-1),
))
def _apply(self, msg):
bridge = cv_bridge.CvBridge()
mask = bridge.imgmsg_to_cv2(msg, desired_encoding='mono8')
if mask.size == 0:
jsk_logdebug('Skipping empty image')
return
label = np.zeros(mask.shape, dtype=np.int32)
label[mask == 0] = 0
label[mask == 255] = 1
label_msg = bridge.cv2_to_imgmsg(label, encoding='32SC1')
label_msg.header = msg.header
self._pub.publish(label_msg)
def subscribe(self):
self._sub_feature = message_filters.Subscriber('~input', Feature0D)
self._sub_label = message_filters.Subscriber('~input/label', Image)
use_async = rospy.get_param('~approximate_sync', False)
if use_async:
sync = message_filters.ApproximateTimeSynchronizer(
[self._sub_feature, self._sub_label], queue_size=10, slop=0.1)
else:
sync = message_filters.TimeSynchronizer(
[self._sub_feature, self._sub_feature], queue_size=10)
jsk_logdebug('~approximate_sync: {}'.format(use_async))
sync.registerCallback(self._apply)
def subscribe(self):
self._sub_feature = message_filters.Subscriber('~input', Feature0D)
self._sub_label = message_filters.Subscriber('~input/label', Image)
use_async = rospy.get_param('~approximate_sync', False)
if use_async:
sync = message_filters.ApproximateTimeSynchronizer(
[self._sub_feature, self._sub_label], queue_size=10, slop=0.1)
else:
sync = message_filters.TimeSynchronizer(
[self._sub_feature, self._sub_feature], queue_size=10)
jsk_logdebug('~approximate_sync: {}'.format(use_async))
sync.registerCallback(self._apply)
def _apply(self, feature_msg, label_msg):
bridge = cv_bridge.CvBridge()
label = bridge.imgmsg_to_cv2(label_msg)
desc = np.array(feature_msg.descriptors)
desc = desc.reshape((-1, feature_msg.descriptor_dim))
pos = np.array(feature_msg.positions)
pos = pos.reshape((-1, 2))
if label.sum() == 0:
jsk_logdebug('Skip image with only background label')
return
decomposed = decompose_descriptors_with_label(
descriptors=desc, positions=pos, label_img=label,
skip_zero_label=True)
X = self.bof.transform(np.array(decomposed.values()))
normalize(X, copy=False)
self._pub.publish(
VectorArray(
header=feature_msg.header,
vector_dim=X.shape[1],
data=X.reshape(-1),
))