def spin_once(self):
if self.bof_data is None or self.cfeature is None:
return
stamp, bof_objects_proba = self.bof_data
stamp, cfeature_objects_proba = self.cfeature
weight = self.weight
target_bin = rospy.get_param('target', None)
object_list = get_object_list()
all_proba = [
(o,
(weight[o]['bof'] * bof_objects_proba[o]) +
(weight[o]['color'] * cfeature_objects_proba[o])
) for o in object_list
]
# verification result for debug
candidates = self.bin_contents.get(target_bin, None)
if candidates is None:
candidates = object_list
matched = sorted(all_proba, key=lambda x: x[1])[-1][0]
# compose msg
msg = ObjectRecognition()
msg.header.stamp = stamp
msg.matched = matched
msg.probability = dict(all_proba)[matched] / sum(dict(all_proba).values())
msg.candidates = candidates
msg.probabilities = np.array([dict(all_proba)[c] for c in candidates])
msg.probabilities /= msg.probabilities.sum()
self.pub_debug.publish(msg)
# verification result with json target
if target_bin is None or target_bin == '':
return
proba = [
(c,
(weight[c]['bof'] * bof_objects_proba[c]) +
(weight[c]['color'] * cfeature_objects_proba[c])
) for c in candidates
]
matched = sorted(proba, key=lambda x: x[1])[-1][0]
# compose msg
msg = ObjectRecognition()
msg.header.stamp = stamp
msg.matched = matched
msg.probability = dict(proba)[matched] / sum(dict(proba).values())
msg.candidates = candidates
msg.probabilities = np.array([dict(proba)[c] for c in candidates])
msg.probabilities /= msg.probabilities.sum()
self.pub.publish(msg)
def predict_now(self):
query_image = self.query_image
object_list = get_object_list()
probs = self.match(object_list)
matched_idx = np.argmax(probs)
# prepare message
res = ObjectRecognition()
res.header.stamp = query_image.header.stamp
res.matched = object_list[matched_idx]
res.probability = probs[matched_idx]
res.candidates = object_list
res.probabilities = probs
return res
def predict_now(self):
query_features = self.query_features
if not len(query_features.descriptors) > 0:
return
descs = np.array(query_features.descriptors)
X = self.bof.transform([descs])
normalize(X, copy=False)
self._pub_debug.publish(Histogram(header=query_features.header, histogram=X[0]))
object_list = get_object_list()
proba = self.clf.predict_proba(X)[0]
matched_idx = np.argmax(proba)
# prepare message
res = ObjectRecognition()
res.header.stamp = query_features.header.stamp
res.matched = object_list[matched_idx]
res.probability = proba[matched_idx]
res.candidates = object_list
res.probabilities = proba
return res
def predict_now(self):
query_features = self.query_features
if not len(query_features.descriptors) > 0:
return
descs = np.array(query_features.descriptors)
X = self.bof.transform([descs])
normalize(X, copy=False)
self._pub_debug.publish(
Histogram(header=query_features.header, histogram=X[0]))
object_list = get_object_list()
proba = self.clf.predict_proba(X)[0]
matched_idx = np.argmax(proba)
# prepare message
res = ObjectRecognition()
res.header.stamp = query_features.header.stamp
res.matched = object_list[matched_idx]
res.probability = proba[matched_idx]
res.candidates = object_list
res.probabilities = proba
return res
def spin_once(self):
if self.cfeatures is None:
rospy.loginfo('color feature is None')
return
stamp, objects_proba = self.cfeatures
target_bin = rospy.get_param('/target', None)
if target_bin is None:
rospy.loginfo('target bin is None')
return
candidates = self.bin_contents[target_bin]
proba = [(c, objects_proba[c]) for c in candidates]
matched = sorted(proba, key=lambda x: x[1])[-1][0]
# compose msg
msg = ObjectRecognition()
msg.header.stamp = stamp
msg.matched = matched
msg.probability = objects_proba[matched]
msg.candidates = candidates
msg.probabilities = np.array([objects_proba[c] for c in candidates])
msg.probabilities /= msg.probabilities.sum()
self.pub.publish(msg)