def run_until_exemplar_decision_signal(ibs, cm, qreq_, incinfo=None):
"""
DECISION STEP 3)
Either the system or the user decides if the query should be added to the
database as an exemplar.
"""
qaid = cm.qaid
exemplar_confidence_thresh = ut.get_sys_maxfloat()
exmplr_suggestion = automatch_suggestor.get_system_exemplar_suggestion(
ibs, qaid)
(autoexemplar_msg, exemplar_decision,
exemplar_condience) = exmplr_suggestion
print('autoexemplar_msg=')
print(autoexemplar_msg)
# HACK: Disable asking users a about exemplars
need_user_input = False and (
incinfo.get('interactive', False)
and exemplar_condience < exemplar_confidence_thresh)
if need_user_input:
user_dialogs.wait_for_user_exemplar_decision(autoexemplar_msg,
exemplar_decision,
exemplar_condience)
else:
# May need to execute callback whereas whatever the interaction was
# would issue it otherwise
exec_exemplar_decision_and_continue(exemplar_decision,
ibs,
cm,
qreq_,
incinfo=incinfo)
def run_until_exemplar_decision_signal(ibs, cm, qreq_, incinfo=None):
"""
DECISION STEP 3)
Either the system or the user decides if the query should be added to the
database as an exemplar.
"""
qaid = cm.qaid
exemplar_confidence_thresh = ut.get_sys_maxfloat()
exmplr_suggestion = automatch_suggestor.get_system_exemplar_suggestion(ibs, qaid)
(autoexemplar_msg, exemplar_decision, exemplar_condience) = exmplr_suggestion
print('autoexemplar_msg=')
print(autoexemplar_msg)
# HACK: Disable asking users a about exemplars
need_user_input = False and (incinfo.get('interactive', False) and exemplar_condience < exemplar_confidence_thresh)
if need_user_input:
user_dialogs.wait_for_user_exemplar_decision(
autoexemplar_msg, exemplar_decision, exemplar_condience)
else:
# May need to execute callback whereas whatever the interaction was
# would issue it otherwise
exec_exemplar_decision_and_continue(exemplar_decision, ibs, cm, qreq_, incinfo=incinfo)
def get_system_name_suggestion(ibs, choicetup):
"""
Suggests a decision based on the current choices
Args:
ibs (IBEISController):
qaid (int): query annotation id
cm (QueryResult): object of feature correspondences and scores
metatup (None):
Returns:
tuple: (autoname_msg, autoname_func)
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-test_incremental_queries:0
python -m ibeis.algo.hots.automated_matcher --test-test_incremental_queries:1
python -m ibeis.algo.hots.automated_matcher --test-get_system_name_suggestion
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automatch_suggestor import * # NOQA
>>> import ibeis
>>> # build test data
>>> cm, qreq_ = ibeis.testdata_cm()
>>> ibs = qreq_.ibs
>>> choicetup = get_qres_name_choices(ibs, cm)
>>> (autoname_msg, name, name_confidence) = get_system_name_suggestion(ibs, choicetup)
>>> # verify results
>>> result = str((autoname_msg, name, name_confidence))
>>> print(result)
"""
threshold = ut.get_sys_maxfloat()
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids, sorted_ascores) = choicetup
autoname_msg_list = []
if len(sorted_nids) == 0:
autoname_msg_list.append('Unable to find any matches')
# if we can't find any matches then we must be sure it is a
# new name. A false negative here can only be fixed with a merge
name_confidence = 1.0
nid, score, rank = None, None, None
else:
name_confidence = 0
candidate_indexes = np.where(sorted_nscore > threshold)[0]
if len(candidate_indexes) == 0:
rank = None
autoname_msg_list.append('No candidates above threshold')
else:
sortx = sorted_nscore[candidate_indexes].argsort()[::-1]
rank = sortx[0]
multiple_candidates = len(candidate_indexes) > 1
if multiple_candidates:
autoname_msg_list.append('Multiple candidates above threshold')
else:
autoname_msg_list.append('Single candidate above threshold')
# Get system suggested nid and message
if rank is not None:
score = sorted_nscore[rank]
nid = sorted_nids[rank]
rawscore = sorted_rawscore[rank][0]
msg_fmtstr = 'suggesting nid=%r, score=%.2f, rank=%r, rawscore=%.2f'
msg = msg_fmtstr % (nid, score, rank, rawscore)
autoname_msg_list.append(msg)
else:
nid, score, rawscore = None, None, None
autoname_msg_list.append('suggesting new name')
autoname_msg = '\n'.join(autoname_msg_list)
name = ibs.get_name_texts(nid) if nid is not None else None
if name is None:
chosen_names = []
else:
chosen_names = [name]
system_name_suggest_tup = (autoname_msg, chosen_names, name_confidence,)
return system_name_suggest_tup
def get_system_name_suggestion(ibs, choicetup):
"""
Suggests a decision based on the current choices
Args:
ibs (IBEISController):
qaid (int): query annotation id
cm (QueryResult): object of feature correspondences and scores
metatup (None):
Returns:
tuple: (autoname_msg, autoname_func)
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-test_incremental_queries:0
python -m ibeis.algo.hots.automated_matcher --test-test_incremental_queries:1
python -m ibeis.algo.hots.automated_matcher --test-get_system_name_suggestion
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automatch_suggestor import * # NOQA
>>> import ibeis
>>> # build test data
>>> cm, qreq_ = ibeis.testdata_cm()
>>> ibs = qreq_.ibs
>>> choicetup = get_qres_name_choices(ibs, cm)
>>> (autoname_msg, name, name_confidence) = get_system_name_suggestion(ibs, choicetup)
>>> # verify results
>>> result = str((autoname_msg, name, name_confidence))
>>> print(result)
"""
threshold = ut.get_sys_maxfloat()
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids,
sorted_ascores) = choicetup
autoname_msg_list = []
if len(sorted_nids) == 0:
autoname_msg_list.append('Unable to find any matches')
# if we can't find any matches then we must be sure it is a
# new name. A false negative here can only be fixed with a merge
name_confidence = 1.0
nid, score, rank = None, None, None
else:
name_confidence = 0
candidate_indexes = np.where(sorted_nscore > threshold)[0]
if len(candidate_indexes) == 0:
rank = None
autoname_msg_list.append('No candidates above threshold')
else:
sortx = sorted_nscore[candidate_indexes].argsort()[::-1]
rank = sortx[0]
multiple_candidates = len(candidate_indexes) > 1
if multiple_candidates:
autoname_msg_list.append('Multiple candidates above threshold')
else:
autoname_msg_list.append('Single candidate above threshold')
# Get system suggested nid and message
if rank is not None:
score = sorted_nscore[rank]
nid = sorted_nids[rank]
rawscore = sorted_rawscore[rank][0]
msg_fmtstr = 'suggesting nid=%r, score=%.2f, rank=%r, rawscore=%.2f'
msg = msg_fmtstr % (nid, score, rank, rawscore)
autoname_msg_list.append(msg)
else:
nid, score, rawscore = None, None, None
autoname_msg_list.append('suggesting new name')
autoname_msg = '\n'.join(autoname_msg_list)
name = ibs.get_name_texts(nid) if nid is not None else None
if name is None:
chosen_names = []
else:
chosen_names = [name]
system_name_suggest_tup = (
autoname_msg,
chosen_names,
name_confidence,
)
return system_name_suggest_tup