def filter_aidpairs_by_tags(ibs,
has_any=None,
has_all=None,
min_num=None,
max_num=None,
am_rowids=None):
"""
list(zip(aid_pairs, undirected_tags))
"""
# annotmatch_rowids = ibs.get_annotmatch_rowids_from_aid(aid_list)
filtered_annotmatch_rowids = filter_annotmatch_by_tags(
ibs,
am_rowids,
has_any=has_any,
has_all=has_all,
min_num=min_num,
max_num=max_num,
)
aid1_list = np.array(ibs.get_annotmatch_aid1(filtered_annotmatch_rowids))
aid2_list = np.array(ibs.get_annotmatch_aid2(filtered_annotmatch_rowids))
aid_pairs = np.vstack([aid1_list, aid2_list]).T
# Dont double count
vt.get_undirected_edge_ids(aid_pairs)
xs = vt.find_best_undirected_edge_indexes(aid_pairs)
aid1_list = aid1_list.take(xs)
aid2_list = aid2_list.take(xs)
aid_pairs = np.vstack([aid1_list, aid2_list]).T
return aid_pairs
def filter_aid_pairs(aid1_list, aid2_list):
"""
TODO: move to results_organizer
"""
np.vstack((aid1_list, aid2_list)).T
import vtool as vt
index_list = vt.find_best_undirected_edge_indexes(
np.vstack((aid1_list, aid2_list)).T)
aid1_list = ut.list_take(aid1_list, index_list)
aid2_list = ut.list_take(aid2_list, index_list)
return aid1_list, aid2_list
def filter_aidpairs_by_tags(ibs, has_any=None, has_all=None, min_num=None, max_num=None):
"""
list(zip(aid_pairs, undirected_tags))
"""
filtered_annotmatch_rowids = filter_annotmatch_by_tags(
ibs, None, has_any=has_any, has_all=has_all, min_num=min_num,
max_num=max_num)
aid1_list = np.array(ibs.get_annotmatch_aid1(filtered_annotmatch_rowids))
aid2_list = np.array(ibs.get_annotmatch_aid2(filtered_annotmatch_rowids))
aid_pairs = np.vstack([aid1_list, aid2_list]).T
# Dont double count
vt.get_undirected_edge_ids(aid_pairs)
xs = vt.find_best_undirected_edge_indexes(aid_pairs)
aid1_list = aid1_list.take(xs)
aid2_list = aid2_list.take(xs)
aid_pairs = np.vstack([aid1_list, aid2_list]).T
return aid_pairs
def get_review_edges(cm_list, ibs=None, review_cfg={}):
r"""
Needs to be moved to a better file. Maybe something to do with
identification.
Returns a list of matches that should be inspected
This function is more lightweight than orgres or allres.
Used in id_review_api and interact_qres2
Args:
cm_list (list): list of chip match objects
ranks_top (int): put all ranks less than this number into the graph
directed (bool):
Returns:
tuple: review_edges = (qaid_arr, daid_arr, score_arr, rank_arr)
CommandLine:
python -m ibeis.gui.id_review_api get_review_edges:0
Example0:
>>> # ENABLE_DOCTEST
>>> from ibeis.gui.id_review_api import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qreq_ = ibeis.main_helpers.testdata_qreq_()
>>> cm_list = qreq_.execute()
>>> review_cfg = dict(ranks_top=5, directed=True, name_scoring=False,
>>> filter_true_matches=True)
>>> review_edges = get_review_edges(cm_list, ibs=ibs, review_cfg=review_cfg)
>>> print(review_edges)
Example1:
>>> # UNSTABLE_DOCTEST
>>> from ibeis.gui.id_review_api import * # NOQA
>>> import ibeis
>>> cm_list, qreq_ = ibeis.testdata_cmlist('PZ_MTEST', a='default:qsize=5,dsize=20')
>>> review_cfg = dict(ranks_top=5, directed=True, name_scoring=False,
>>> filter_reviewed=False, filter_true_matches=True)
>>> review_edges = get_review_edges(cm_list, review_cfg=review_cfg, ibs=ibs)
>>> print(review_edges)
Example3:
>>> # UNSTABLE_DOCTEST
>>> from ibeis.gui.id_review_api import * # NOQA
>>> import ibeis
>>> cm_list, qreq_ = ibeis.testdata_cmlist('PZ_MTEST', a='default:qsize=1,dsize=100')
>>> review_cfg = dict(ranks_top=1, directed=False, name_scoring=False,
>>> filter_reviewed=False, filter_true_matches=True)
>>> review_edges = get_review_edges(cm_list, review_cfg=review_cfg, ibs=ibs)
>>> print(review_edges)
Example4:
>>> # UNSTABLE_DOCTEST
>>> from ibeis.gui.id_review_api import * # NOQA
>>> import ibeis
>>> cm_list, qreq_ = ibeis.testdata_cmlist('PZ_MTEST', a='default:qsize=10,dsize=10')
>>> ranks_top = 3
>>> review_cfg = dict(ranks_top=3, directed=False, name_scoring=False,
>>> filter_reviewed=False, filter_true_matches=True)
>>> review_edges = get_review_edges(cm_list, review_cfg=review_cfg, ibs=ibs)
>>> print(review_edges)
"""
import vtool as vt
from ibeis.algo.hots import chip_match
automatch_kw = REVIEW_CFG_DEFAULTS.copy()
automatch_kw = ut.update_existing(automatch_kw, review_cfg)
print('[resorg] get_review_edges(%s)' % (ut.repr2(automatch_kw)))
print('[resorg] len(cm_list) = %d' % (len(cm_list)))
qaids_stack = []
daids_stack = []
ranks_stack = []
scores_stack = []
# For each QueryResult, Extract inspectable candidate matches
if isinstance(cm_list, dict):
cm_list = list(cm_list.values())
if len(cm_list) == 0:
return ([], [], [], [])
for cm in cm_list:
if isinstance(cm, chip_match.ChipMatch):
daids = cm.get_top_aids(ntop=automatch_kw['ranks_top'])
scores = cm.get_top_scores(ntop=automatch_kw['ranks_top'])
ranks = np.arange(len(daids))
qaids = np.full(daids.shape, cm.qaid, dtype=daids.dtype)
else:
(qaids, daids, scores, ranks) = cm.get_match_tbldata(
ranks_top=automatch_kw['ranks_top'],
name_scoring=automatch_kw['name_scoring'],
ibs=ibs)
qaids_stack.append(qaids)
daids_stack.append(daids)
scores_stack.append(scores)
ranks_stack.append(ranks)
# Stack them into a giant array
qaid_arr = np.hstack(qaids_stack)
daid_arr = np.hstack(daids_stack)
score_arr = np.hstack(scores_stack)
rank_arr = np.hstack(ranks_stack)
# Sort by scores
sortx = score_arr.argsort()[::-1]
qaid_arr = qaid_arr[sortx]
daid_arr = daid_arr[sortx]
score_arr = score_arr[sortx]
rank_arr = rank_arr[sortx]
# IS_REVIEWED DOES NOT WORK
if automatch_kw['filter_reviewed']:
_is_reviewed = ibs.get_annot_pair_is_reviewed(qaid_arr.tolist(),
daid_arr.tolist())
is_unreviewed = ~np.array(_is_reviewed, dtype=np.bool)
qaid_arr = qaid_arr.compress(is_unreviewed)
daid_arr = daid_arr.compress(is_unreviewed)
score_arr = score_arr.compress(is_unreviewed)
rank_arr = rank_arr.compress(is_unreviewed)
# Remove directed edges
if not automatch_kw['directed']:
#nodes = np.unique(directed_edges.flatten())
directed_edges = np.vstack((qaid_arr, daid_arr)).T
#idx1, idx2 = vt.intersect2d_indices(directed_edges, directed_edges[:, ::-1])
unique_rowx = vt.find_best_undirected_edge_indexes(directed_edges,
score_arr)
qaid_arr = qaid_arr.take(unique_rowx)
daid_arr = daid_arr.take(unique_rowx)
score_arr = score_arr.take(unique_rowx)
rank_arr = rank_arr.take(unique_rowx)
# Filter Double Name Matches
if automatch_kw['filter_duplicate_true_matches']:
# filter_dup_namepairs
qnid_arr = ibs.get_annot_nids(qaid_arr)
dnid_arr = ibs.get_annot_nids(daid_arr)
if not automatch_kw['directed']:
directed_name_edges = np.vstack((qnid_arr, dnid_arr)).T
unique_rowx2 = vt.find_best_undirected_edge_indexes(
directed_name_edges, score_arr)
else:
namepair_id_list = np.array(vt.compute_unique_data_ids_(
list(zip(qnid_arr, dnid_arr))))
unique_namepair_ids, namepair_groupxs = vt.group_indices(namepair_id_list)
score_namepair_groups = vt.apply_grouping(score_arr, namepair_groupxs)
unique_rowx2 = np.array(sorted([
groupx[score_group.argmax()]
for groupx, score_group in zip(namepair_groupxs, score_namepair_groups)
]), dtype=np.int32)
qaid_arr = qaid_arr.take(unique_rowx2)
daid_arr = daid_arr.take(unique_rowx2)
score_arr = score_arr.take(unique_rowx2)
rank_arr = rank_arr.take(unique_rowx2)
# Filter all true matches
if automatch_kw['filter_true_matches']:
qnid_arr = ibs.get_annot_nids(qaid_arr)
dnid_arr = ibs.get_annot_nids(daid_arr)
valid_flags = qnid_arr != dnid_arr
qaid_arr = qaid_arr.compress(valid_flags)
daid_arr = daid_arr.compress(valid_flags)
score_arr = score_arr.compress(valid_flags)
rank_arr = rank_arr.compress(valid_flags)
if automatch_kw['filter_photobombs']:
unique_aids = ut.unique(ut.flatten([qaid_arr, daid_arr]))
#grouped_aids, unique_nids = ibs.group_annots_by_name(unique_aids)
invalid_nid_map = get_photobomber_map(ibs, qaid_arr)
nid2_aids = ut.group_items(unique_aids, ibs.get_annot_nids(unique_aids))
expanded_aid_map = ut.ddict(set)
for nid1, other_nids in invalid_nid_map.items():
for aid1 in nid2_aids[nid1]:
for nid2 in other_nids:
for aid2 in nid2_aids[nid2]:
expanded_aid_map[aid1].add(aid2)
expanded_aid_map[aid2].add(aid1)
valid_flags = [daid not in expanded_aid_map[qaid]
for qaid, daid in zip(qaid_arr, daid_arr)]
qaid_arr = qaid_arr.compress(valid_flags)
daid_arr = daid_arr.compress(valid_flags)
score_arr = score_arr.compress(valid_flags)
rank_arr = rank_arr.compress(valid_flags)
review_edges = (qaid_arr, daid_arr, score_arr, rank_arr)
return review_edges
def get_automatch_candidates(cm_list, ranks_lt=5, directed=True,
name_scoring=False, ibs=None, filter_reviewed=False,
filter_duplicate_namepair_matches=False):
"""
THIS IS PROBABLY ONE OF THE ONLY THINGS IN THIS FILE THAT SHOULD NOT BE
DEPRICATED
Returns a list of matches that should be inspected
This function is more lightweight than orgres or allres.
Used in inspect_gui and interact_qres2
Args:
qaid2_qres (dict): mapping from query annotaiton id to query result object
ranks_lt (int): put all ranks less than this number into the graph
directed (bool):
Returns:
tuple: candidate_matches = (qaid_arr, daid_arr, score_arr, rank_arr)
CommandLine:
python -m ibeis.expt.results_organizer --test-get_automatch_candidates:2
python -m ibeis.expt.results_organizer --test-get_automatch_candidates:0
Example0:
>>> # ENABLE_DOCTEST
>>> from ibeis.expt.results_organizer import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qreq_ = ibeis.main_helpers.testdata_qreq_()
>>> cm_list = ibs.query_chips(qreq_=qreq_, return_cm=True)
>>> ranks_lt = 5
>>> directed = True
>>> name_scoring = False
>>> candidate_matches = get_automatch_candidates(cm_list, ranks_lt, directed, ibs=ibs)
>>> print(candidate_matches)
Example1:
>>> # UNSTABLE_DOCTEST
>>> from ibeis.expt.results_organizer import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qaid_list = ibs.get_valid_aids()[0:5]
>>> daid_list = ibs.get_valid_aids()[0:20]
>>> cm_list = ibs.query_chips(qaid_list, daid_list, return_cm=True)
>>> ranks_lt = 5
>>> directed = False
>>> name_scoring = False
>>> filter_reviewed = False
>>> filter_duplicate_namepair_matches = True
>>> candidate_matches = get_automatch_candidates(
... cm_list, ranks_lt, directed, name_scoring=name_scoring,
... filter_reviewed=filter_reviewed,
... filter_duplicate_namepair_matches=filter_duplicate_namepair_matches,
... ibs=ibs)
>>> print(candidate_matches)
Example3:
>>> # UNSTABLE_DOCTEST
>>> from ibeis.expt.results_organizer import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qaid_list = ibs.get_valid_aids()[0:1]
>>> daid_list = ibs.get_valid_aids()[10:100]
>>> qaid2_cm = ibs.query_chips(qaid_list, daid_list, return_cm=True)
>>> ranks_lt = 1
>>> directed = False
>>> name_scoring = False
>>> filter_reviewed = False
>>> filter_duplicate_namepair_matches = True
>>> candidate_matches = get_automatch_candidates(
... cm_list, ranks_lt, directed, name_scoring=name_scoring,
... filter_reviewed=filter_reviewed,
... filter_duplicate_namepair_matches=filter_duplicate_namepair_matches,
... ibs=ibs)
>>> print(candidate_matches)
Example4:
>>> # UNSTABLE_DOCTEST
>>> from ibeis.expt.results_organizer import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qaid_list = ibs.get_valid_aids()[0:10]
>>> daid_list = ibs.get_valid_aids()[0:10]
>>> qres_list = ibs.query_chips(qaid_list, daid_list)
>>> ranks_lt = 3
>>> directed = False
>>> name_scoring = False
>>> filter_reviewed = False
>>> filter_duplicate_namepair_matches = True
>>> candidate_matches = get_automatch_candidates(
... qaid2_cm, ranks_lt, directed, name_scoring=name_scoring,
... filter_reviewed=filter_reviewed,
... filter_duplicate_namepair_matches=filter_duplicate_namepair_matches,
... ibs=ibs)
>>> print(candidate_matches)
"""
import vtool as vt
from ibeis.model.hots import chip_match
print(('[resorg] get_automatch_candidates('
'filter_reviewed={filter_reviewed},'
'filter_duplicate_namepair_matches={filter_duplicate_namepair_matches},'
'directed={directed},'
'ranks_lt={ranks_lt},'
).format(**locals()))
print('[resorg] len(cm_list) = %d' % (len(cm_list)))
qaids_stack = []
daids_stack = []
ranks_stack = []
scores_stack = []
# For each QueryResult, Extract inspectable candidate matches
if isinstance(cm_list, dict):
cm_list = list(cm_list.values())
for cm in cm_list:
if isinstance(cm, chip_match.ChipMatch2):
daids = cm.get_top_aids(ntop=ranks_lt)
scores = cm.get_top_scores(ntop=ranks_lt)
ranks = np.arange(len(daids))
qaids = np.full(daids.shape, cm.qaid, dtype=daids.dtype)
else:
(qaids, daids, scores, ranks) = cm.get_match_tbldata(
ranks_lt=ranks_lt, name_scoring=name_scoring, ibs=ibs)
qaids_stack.append(qaids)
daids_stack.append(daids)
scores_stack.append(scores)
ranks_stack.append(ranks)
# Stack them into a giant array
# utool.embed()
qaid_arr = np.hstack(qaids_stack)
daid_arr = np.hstack(daids_stack)
score_arr = np.hstack(scores_stack)
rank_arr = np.hstack(ranks_stack)
# Sort by scores
sortx = score_arr.argsort()[::-1]
qaid_arr = qaid_arr[sortx]
daid_arr = daid_arr[sortx]
score_arr = score_arr[sortx]
rank_arr = rank_arr[sortx]
if filter_reviewed:
_is_reviewed = ibs.get_annot_pair_is_reviewed(qaid_arr.tolist(), daid_arr.tolist())
is_unreviewed = ~np.array(_is_reviewed, dtype=np.bool)
qaid_arr = qaid_arr.compress(is_unreviewed)
daid_arr = daid_arr.compress(is_unreviewed)
score_arr = score_arr.compress(is_unreviewed)
rank_arr = rank_arr.compress(is_unreviewed)
# Remove directed edges
if not directed:
#nodes = np.unique(directed_edges.flatten())
directed_edges = np.vstack((qaid_arr, daid_arr)).T
#idx1, idx2 = vt.intersect2d_indices(directed_edges, directed_edges[:, ::-1])
unique_rowx = vt.find_best_undirected_edge_indexes(directed_edges, score_arr)
qaid_arr = qaid_arr.take(unique_rowx)
daid_arr = daid_arr.take(unique_rowx)
score_arr = score_arr.take(unique_rowx)
rank_arr = rank_arr.take(unique_rowx)
# Filter Double Name Matches
if filter_duplicate_namepair_matches:
qnid_arr = ibs.get_annot_nids(qaid_arr)
dnid_arr = ibs.get_annot_nids(daid_arr)
if not directed:
directed_name_edges = np.vstack((qnid_arr, dnid_arr)).T
unique_rowx2 = vt.find_best_undirected_edge_indexes(directed_name_edges, score_arr)
else:
namepair_id_list = np.array(vt.compute_unique_data_ids_(list(zip(qnid_arr, dnid_arr))))
unique_namepair_ids, namepair_groupxs = vt.group_indices(namepair_id_list)
score_namepair_groups = vt.apply_grouping(score_arr, namepair_groupxs)
unique_rowx2 = np.array(sorted([
groupx[score_group.argmax()]
for groupx, score_group in zip(namepair_groupxs, score_namepair_groups)
]), dtype=np.int32)
qaid_arr = qaid_arr.take(unique_rowx2)
daid_arr = daid_arr.take(unique_rowx2)
score_arr = score_arr.take(unique_rowx2)
rank_arr = rank_arr.take(unique_rowx2)
candidate_matches = (qaid_arr, daid_arr, score_arr, rank_arr)
return candidate_matches