# -*- coding: utf-8 -*-
"""
Reports decisions and confidences about names (identifications) and
exemplars using query results objects.
"""
from __future__ import absolute_import, division, print_function
import utool as ut
import numpy as np
import random
from collections import namedtuple
#print, print_, printDBG, rrr, profile = ut.inject(__name__, '[suggest]')
# ---- GLOBALS ----
ChoiceTuple = namedtuple(
'ChoiceTuple',
('sorted_nids', 'sorted_nscore', 'sorted_rawscore',
'sorted_aids', 'sorted_ascores')
)
# An exmplar decision adds and removes exemplars
ExemplarDecision = namedtuple(
'ExemplarDecision',
('new_exemplar_aids', 'remove_exemplar_aids')
)
[docs]def get_qres_name_choices(ibs, cm):
r"""
returns all possible decision a user could make
TODO: Return the possiblity of a merge.
TODO: Ensure that the total probability of each possible choice sums to 1.
This will define a probability density function that we can take advantage
of
Args:
ibs (IBEISController): ibeis controller object
cm (QueryResult): object of feature correspondences and scores
Returns:
ChoiceTuple: choicetup
CommandLine:
python -m ibeis.algo.hots.automatch_suggestor --test-get_qres_name_choices:0
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.automatch_suggestor import * # NOQA
>>> import ibeis # NOQA
>>> # build test data
>>> cm, qreq_ = ibeis.testdata_cm()
>>> ibs = qreq_.ibs
>>> choicetup = get_qres_name_choices(ibs, cm)
>>> print(choicetup)
>>> result = ut.numpy_str(choicetup.sorted_nids[0:1], force_dtype=False)
>>> print(result)
np.array([1])
"""
if cm is None:
nscoretup = list(map(np.array, ([], [], [], [])))
(sorted_nids, sorted_nscore, sorted_aids, sorted_ascores) = nscoretup
else:
print('cm= %r' % (cm,))
nscoretup = cm.get_nscoretup()
(sorted_nids, sorted_nscore, sorted_aids, sorted_ascores) = nscoretup
sorted_rawscore = [cm.get_annot_scores(aids) for aids in sorted_aids]
choicetup = ChoiceTuple(sorted_nids, sorted_nscore, sorted_rawscore,
sorted_aids, sorted_ascores)
return choicetup
[docs]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
[docs]def get_system_exemplar_suggestion(ibs, qaid):
""" hotspotter returns an exemplar suggestion
TODO:
do a vsone query between all of the exemplars to see if this one is good
enough to be added.
TODO:
build a complete graph of exemplar scores and only add if this one is
lower than any other edge
SeeAlso:
ibsfuncs.set_exemplars_from_quality_and_viewpoint
CommandLine:
python -m ibeis.algo.hots.automatch_suggestor --test-get_system_exemplar_suggestion:1
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.automatch_suggestor import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> qaid = 2
>>> # execute function
>>> (autoexmplr_msg, exemplar_decision, exemplar_confidence) = get_system_exemplar_suggestion(ibs, qaid)
>>> # verify results
>>> result = str((autoexmplr_msg, exemplar_decision, exemplar_confidence))
>>> print(result)
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.automatch_suggestor import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> qaid = 1
>>> # execute function
>>> (autoexmplr_msg, exemplar_decision, exemplar_confidence) = get_system_exemplar_suggestion(ibs, qaid)
>>> # verify results
>>> result = str((autoexmplr_msg, exemplar_decision, exemplar_confidence))
>>> print(result)
"""
if ut.VERBOSE:
print('[suggest_exemplar] Deciding if adding qaid=%r as an exemplar' % (qaid,))
# Need a good criteria here
max_exemplars = ibs.cfg.other_cfg.max_exemplars
#max_exemplars = 2
print('[suggest_exemplar] max_exemplars = %r' % max_exemplars)
other_exemplars = ibs.get_annot_groundtruth(qaid, is_exemplar=True)
num_other_exemplars = len(other_exemplars)
#
is_already_exemplar = ibs.get_annot_exemplar_flags(qaid)
have_max_exemplars = num_other_exemplars >= max_exemplars
if ut.VERBOSE:
print('[suggest_exemplar] num_other_exemplars = %r' % num_other_exemplars)
print('[suggest_exemplar] is_already_exemplar = %r' % is_already_exemplar)
if is_already_exemplar:
#ut.embed()
print('[suggest_exemplar] WARNING is_already_exemplar = %r' % is_already_exemplar)
exemplar_confidence = 0
if is_already_exemplar:
exemplar_decision = ExemplarDecision([], [])
autoexmplr_msg = 'WARNING exemplar suggestion should not be in this state'
elif num_other_exemplars == 0:
# Always add the first exemplar
exemplar_decision = ExemplarDecision([qaid], [])
exemplar_confidence = 1.0
autoexmplr_msg = 'First exemplar of this name.'
elif not have_max_exemplars:
exemplar_decision = ExemplarDecision([qaid], [])
exemplar_confidence = 1.0
autoexmplr_msg = 'This name has room for more exemplars'
elif have_max_exemplars:
METHOD = 2
if METHOD == 1:
exemplar_decision = exemplar_method1_distinctiveness(ibs, qaid, other_exemplars)
autoexmplr_msg = '[suggest_exemplar] computed bsed on distinctivness '
elif METHOD == 2:
exemplar_decision = exemplar_method2_randomness(qaid, other_exemplars)
autoexmplr_msg = '[suggest_exemplar] randomly chose '
elif METHOD == 3:
exemplar_decision = ExemplarDecision([], [])
autoexmplr_msg = '[suggest_exemplar] defaulting '
else:
raise AssertionError('Impossible State')
else:
raise AssertionError('Impossible State')
autoexmplr_msg += 'exemplar_decision=%r' % (exemplar_decision,)
return autoexmplr_msg, exemplar_decision, exemplar_confidence
[docs]def exemplar_method2_randomness(qaid, other_exemplars):
r"""
CommandLine:
python -m ibeis.algo.hots.automatch_suggestor --test-exemplar_method2_randomness
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.automatch_suggestor import * # NOQA
>>> # build test data
>>> random.seed(0)
>>> qaid = 4
>>> other_exemplars = [1, 2, 3, 5, 6, 9]
>>> # execute function
>>> exemplar_decision = exemplar_method2_randomness(qaid, other_exemplars)
>>> exemplar_decision_list = [exemplar_method2_randomness(qaid, other_exemplars) for _ in range(1000)]
>>> # verify results
>>> flat_others = ut.flatten(ut.get_list_column(exemplar_decision_list, 1))
>>> result = str(flat_others)
>>> print(result)
[3, 2, 6, 1, 3, 2, 9, 3, 6, 2, 1, 5, 1, 6, 1]
Ignore:
ibs = ut.search_stack_for_localvar('ibs')
"""
rand_thresh = .01
#rand_thresh = .1
#rand_thresh = .9
rand_float = random.random()
if rand_float < rand_thresh:
exemplar_decision = ExemplarDecision([qaid], [random.choice(other_exemplars)])
#ut.embed()
else:
exemplar_decision = ExemplarDecision([], [])
return exemplar_decision
[docs]def exemplar_method1_distinctiveness(ibs, qaid, other_exemplars):
"""
choose as exemplar if it is distinctive with respect to other exemplars
"""
# FIXME ExemplarDecision
print('[suggest_exemplar] Testing exemplar disinctiveness')
exemplar_distinctiveness_thresh = ibs.cfg.other_cfg.exemplar_distinctiveness_thresh
# Logic to choose query based on exemplar score distance
qaid_list = [qaid]
daid_list = other_exemplars
cfgdict = dict(codename='vsone_norm_csum')
cm = ibs.query_chips(qaid_list, daid_list, cfgdict=cfgdict, verbose=False)[0]
if cm is None:
exemplar_decision = True
else:
#ut.embed()
aid_arr, score_arr = cm.get_aids_and_scores()
exemplar_decision = np.all(aid_arr < exemplar_distinctiveness_thresh)
return exemplar_decision
if __name__ == '__main__':
"""
CommandLine:
python -m ibeis.algo.hots.automatch_suggestor
python -m ibeis.algo.hots.automatch_suggestor --allexamples
python -m ibeis.algo.hots.automatch_suggestor --allexamples --noface --nosrc
"""
import multiprocessing
multiprocessing.freeze_support() # for win32
import utool as ut # NOQA
ut.doctest_funcs()