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_bin', None)
object_list = jsk_apc2015_common.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 = jsk_2015_apc_common.data.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 _apply(self, msg):
target_bin = rospy.get_param('target_bin', '')
if (not target_bin) or (target_bin not in 'abcdefghijkl'):
return
# get candidates probabilities
if target_bin not in self.bin_contents:
rospy.logerr("target_bin '{0}' is not found in json: {1}".format(
target_bin, self.bin_contents))
return
candidates = self.bin_contents[target_bin] + ['no_object']
label_to_proba = dict(zip(msg.target_names, msg.probabilities))
candidates_proba = [label_to_proba[label] for label in candidates]
candidates_proba = np.array(candidates_proba)
candidates_proba = candidates_proba / candidates_proba.sum()
# compose output message
top_index = np.argmax(candidates_proba)
out_msg = ObjectRecognition(
header=msg.header,
matched=candidates[top_index],
probability=candidates_proba[top_index],
candidates=candidates,
probabilities=candidates_proba,
)
self.pub.publish(out_msg)
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 = jsk_2015_apc_common.data.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 _apply(self, msg):
# get candidates probabilities
candidates = [
obj for obj in self.tote_contents if obj not in self.blacklist
]
candidates = ['no_object'] + candidates[:]
label_to_proba = dict(zip(msg.target_names, msg.probabilities))
candidates_proba = [label_to_proba[label] for label in candidates]
candidates_proba = np.array(candidates_proba)
candidates_proba = candidates_proba / candidates_proba.sum()
# compose output message
top_index = np.argmax(candidates_proba)
out_msg = ObjectRecognition(
header=msg.header,
matched=candidates[top_index],
probability=candidates_proba[top_index],
candidates=candidates,
probabilities=candidates_proba,
)
self.pub.publish(out_msg)
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 = jsk_2015_apc_common.data.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.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 = jsk_2015_apc_common.data.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)
