def load_internal_data():
"""
ibeis TestResult --db Oxford \
-p smk:nWords=[64000],nAssign=[1],SV=[False],can_match_sameimg=True,dim_size=None \
-a oxford \
--dev-mode
ibeis TestResult --db GZ_Master1 \
-p smk:nWords=[64000],nAssign=[1],SV=[False],fg_on=False \
-a ctrl:qmingt=2 \
--dev-mode
"""
# from ibeis.algo.smk.smk_pipeline import * # NOQA
import ibeis
qreq_ = ibeis.testdata_qreq_(
defaultdb='Oxford',
a='oxford',
p='smk:nWords=[64000],nAssign=[1],SV=[False],can_match_sameimg=True,dim_size=None'
)
cm_list = qreq_.execute()
ave_precisions = [cm.get_annot_ave_precision() for cm in cm_list]
mAP = np.mean(ave_precisions)
print('mAP = %.3f' % (mAP, ))
cm = cm_list[-1]
return qreq_, cm
def testdata_match_interact(**kwargs):
"""
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-testdata_match_interact --show --db PZ_MTEST --qaid 3
Example:
>>> # VIZ_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> import plottool as pt
>>> kwargs = {}
>>> self = testdata_match_interact(**kwargs)
>>> pt.show_if_requested()
"""
import ibeis
qreq_ = ibeis.testdata_qreq_(defaultdb='testdb1', t=['default:Knorm=3'])
ibs = qreq_.ibs
cm = qreq_.execute()[0]
cm.sortself()
aid2 = None
self = MatchInteraction(ibs,
cm,
aid2,
mode=1,
dodraw=False,
qreq_=qreq_,
**kwargs)
return self
def benchmark_knn():
r"""
CommandLine:
python ~/code/ibeis/ibeis/algo/hots/tests/bench.py benchmark_knn --profile
Example:
>>> # DISABLE_DOCTEST
>>> from bench import * # NOQA
>>> result = benchmark_knn()
>>> print(result)
"""
from ibeis.algo.hots import _pipeline_helpers as plh
from ibeis.algo.hots.pipeline import nearest_neighbors
import ibeis
verbose = True
qreq_ = ibeis.testdata_qreq_(
defaultdb='PZ_PB_RF_TRAIN',
t='default:K=3,requery=True,can_match_samename=False',
a='default:qsize=100',
verbose=1)
locals_ = plh.testrun_pipeline_upto(qreq_, 'nearest_neighbors')
Kpad_list, impossible_daids_list = ut.dict_take(
locals_, ['Kpad_list', 'impossible_daids_list'])
nns_list1 = nearest_neighbors(qreq_,
Kpad_list,
impossible_daids_list,
verbose=verbose)
def testdata_pre_baselinefilter(defaultdb='testdb1', qaid_list=None, daid_list=None, codename='vsmany'):
cfgdict = dict(codename=codename)
import ibeis
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, default_qaids=qaid_list, default_daids=daid_list, p=p)
locals_ = testrun_pipeline_upto(qreq_, 'baseline_neighbor_filter')
nns_list, impossible_daids_list = ut.dict_take(locals_, ['nns_list', 'impossible_daids_list'])
return qreq_, nns_list, impossible_daids_list
def train_featscore_normalizer():
r"""
CommandLine:
python -m ibeis --tf train_featscore_normalizer --show
# Write Encoder
python -m ibeis --tf train_featscore_normalizer --db PZ_MTEST -t best -a default --fsvx=0 --threshx=1 --show
# Visualize encoder score adjustment
python -m ibeis --tf TestResult.draw_feat_scoresep --db PZ_MTEST -a timectrl -t best:lnbnn_normer=lnbnn_fg_featscore --show --nocache --nocache-hs
# Compare ranking with encoder vs without
python -m ibeis --tf draw_rank_cmc --db PZ_MTEST -a timectrl -t best:lnbnn_normer=[None,wulu] --show
python -m ibeis --tf draw_rank_cmc --db PZ_MTEST -a default -t best:lnbnn_normer=[None,wulu] --show
# Compare in ipynb
python -m ibeis --tf autogen_ipynb --ipynb --db PZ_MTEST -a default -t best:lnbnn_normer=[None,lnbnn_fg_0.9__featscore]
# Big Test
python -m ibeis --tf draw_rank_cmc --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,lovb],lnbnn_norm_thresh=.5 --show
python -m ibeis --tf draw_rank_cmc --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,jypz],lnbnn_norm_thresh=.1 --show
python -m ibeis --tf draw_rank_cmc --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,jypz],lnbnn_norm_thresh=0 --show
# Big Train
python -m ibeis --tf learn_featscore_normalizer --db PZ_Master1 -a timectrl -t best:K=1 --fsvx=0 --threshx=1 --show
python -m ibeis --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=1 --fsvx=0 --threshx=1 --show --ainfo
python -m ibeis --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=1 --fsvx=0 --threshx=1 --show
python -m ibeis --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=3 --fsvx=0 --threshx=1 --show
Example:
>>> # SCRIPT
>>> from ibeis.algo.hots.scorenorm import * # NOQA
>>> encoder = train_featscore_normalizer()
>>> encoder.visualize(figtitle=encoder.get_cfgstr())
>>> ut.show_if_requested()
"""
import ibeis
# TODO: training / loading / general external models
qreq_ = ibeis.testdata_qreq_(defaultdb='PZ_MTEST',
a=['default'],
p=['default'])
datakw = NormFeatScoreConfig.from_argv_dict()
#datakw = dict(
# disttype=None,
# namemode=ut.get_argval('--namemode', default=True),
# fsvx=ut.get_argval('--fsvx', type_='fuzzy_subset',
# default=slice(None, None, None)),
# threshx=ut.get_argval('--threshx', type_=int, default=None),
# thresh=ut.get_argval('--thresh', type_=float, default=.9),
#)
encoder = learn_featscore_normalizer(qreq_, datakw=datakw)
encoder.save()
return encoder
def train_featscore_normalizer():
r"""
CommandLine:
python -m ibeis --tf train_featscore_normalizer --show
# Write Encoder
python -m ibeis --tf train_featscore_normalizer --db PZ_MTEST -t best -a default --fsvx=0 --threshx=1 --show
# Visualize encoder score adjustment
python -m ibeis --tf TestResult.draw_feat_scoresep --db PZ_MTEST -a timectrl -t best:lnbnn_normer=lnbnn_fg_featscore --show --nocache --nocache-hs
# Compare ranking with encoder vs without
python -m ibeis --tf draw_rank_cdf --db PZ_MTEST -a timectrl -t best:lnbnn_normer=[None,wulu] --show
python -m ibeis --tf draw_rank_cdf --db PZ_MTEST -a default -t best:lnbnn_normer=[None,wulu] --show
# Compare in ipynb
python -m ibeis --tf autogen_ipynb --ipynb --db PZ_MTEST -a default -t best:lnbnn_normer=[None,lnbnn_fg_0.9__featscore]
# Big Test
python -m ibeis --tf draw_rank_cdf --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,lovb],lnbnn_norm_thresh=.5 --show
python -m ibeis --tf draw_rank_cdf --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,jypz],lnbnn_norm_thresh=.1 --show
python -m ibeis --tf draw_rank_cdf --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,jypz],lnbnn_norm_thresh=0 --show
# Big Train
python -m ibeis --tf learn_featscore_normalizer --db PZ_Master1 -a timectrl -t best:K=1 --fsvx=0 --threshx=1 --show
python -m ibeis --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=1 --fsvx=0 --threshx=1 --show --ainfo
python -m ibeis --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=1 --fsvx=0 --threshx=1 --show
python -m ibeis --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=3 --fsvx=0 --threshx=1 --show
Example:
>>> # SCRIPT
>>> from ibeis.algo.hots.scorenorm import * # NOQA
>>> encoder = train_featscore_normalizer()
>>> encoder.visualize(figtitle=encoder.get_cfgstr())
>>> ut.show_if_requested()
"""
import ibeis
# TODO: training / loading / general external models
qreq_ = ibeis.testdata_qreq_(
defaultdb='PZ_MTEST', a=['default'], p=['default'])
datakw = NormFeatScoreConfig.from_argv_dict()
#datakw = dict(
# disttype=None,
# namemode=ut.get_argval('--namemode', default=True),
# fsvx=ut.get_argval('--fsvx', type_='fuzzy_subset',
# default=slice(None, None, None)),
# threshx=ut.get_argval('--threshx', type_=int, default=None),
# thresh=ut.get_argval('--thresh', type_=float, default=.9),
#)
encoder = learn_featscore_normalizer(qreq_, datakw=datakw)
encoder.save()
return encoder
def testdata_pre_sver(defaultdb='PZ_MTEST', qaid_list=None, daid_list=None):
"""
>>> from ibeis.algo.hots._pipeline_helpers import * # NOQA
"""
#from ibeis.algo import Config
cfgdict = dict()
import ibeis
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, default_qaids=qaid_list, default_daids=daid_list, p=p)
ibs = qreq_.ibs
locals_ = testrun_pipeline_upto(qreq_, 'spatial_verification')
cm_list = locals_['cm_list_FILT']
#nnfilts_list = locals_['nnfilts_list']
return ibs, qreq_, cm_list
def testdata_pre(stopnode,
defaultdb='testdb1',
p=['default'],
a=['default:qindex=0:1,dindex=0:5'],
**kwargs):
"""
New (1-1-2016) generic pipeline node testdata getter
Args:
stopnode (str): name of pipeline function to be tested
defaultdb (str): (default = u'testdb1')
p (list): (default = [u'default:'])
a (list): (default = [u'default:qsize=1,dsize=4'])
**kwargs: passed to testdata_qreq_
qaid_override, daid_override
Returns:
tuple: (ibs, qreq_, args)
CommandLine:
python -m ibeis.algo.hots._pipeline_helpers --exec-testdata_pre --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots._pipeline_helpers import * # NOQA
>>> stopnode = 'build_chipmatches'
>>> defaultdb = 'testdb1'
>>> p = ['default:']
>>> a = ['default:qindex=0:1,dindex=0:5']
>>> qreq_, args = testdata_pre(stopnode, defaultdb, p, a)
"""
import ibeis
from ibeis.algo.hots import pipeline
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, p=p, a=a, **kwargs)
locals_ = testrun_pipeline_upto(qreq_, stopnode)
if stopnode == 'end':
argnames = ['cm_list_SVER']
else:
func = getattr(pipeline, stopnode)
argnames = ut.get_argnames(func)
# Hack to ignore qreq_, and verbose
for ignore in ['qreq_', 'ibs', 'verbose']:
try:
argnames.remove(ignore)
except ValueError:
pass
tupname = '_Ret_' + stopnode.upper()
args = ut.dict_take_asnametup(locals_, argnames, name=tupname)
return qreq_, args
def testdata_post_sver(defaultdb='PZ_MTEST', qaid_list=None, daid_list=None, codename='vsmany', cfgdict=None):
"""
>>> from ibeis.algo.hots._pipeline_helpers import * # NOQA
"""
#from ibeis.algo import Config
if cfgdict is None:
cfgdict = dict(codename=codename)
import ibeis
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, default_qaids=qaid_list, default_daids=daid_list, p=p)
ibs = qreq_.ibs
locals_ = testrun_pipeline_upto(qreq_, 'vsone_reranking')
cm_list = locals_['cm_list_SVER']
#nnfilts_list = locals_['nnfilts_list']
return ibs, qreq_, cm_list
def testdata_inva():
"""
from ibeis.algo.smk.inverted_index import * # NOQA
"""
import ibeis
qreq_ = ibeis.testdata_qreq_(defaultdb='PZ_MTEST',
a='default',
p='default:proot=smk,nAssign=1,num_words=64')
aids = qreq_.daids
cls = InvertedAnnots
depc = qreq_.ibs.depc
vocab_aids = qreq_.daids
config = qreq_.qparams
inva = cls.from_depc(depc, aids, vocab_aids, config)
inva.wx_to_aids = inva.compute_inverted_list()
return qreq_, inva
def testdata_pre_vsonerr(defaultdb='PZ_MTEST', qaid_list=[1], daid_list='all'):
"""
>>> from ibeis.algo.hots._pipeline_helpers import * # NOQA
"""
cfgdict = dict(sver_output_weighting=True, codename='vsmany', rrvsone_on=True)
import ibeis
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, default_qaids=qaid_list, default_daids=daid_list, p=p)
ibs = qreq_.ibs
qaid_list = qreq_.get_external_qaids().tolist()
qaid = qaid_list[0]
#daid_list = qreq_.get_external_daids().tolist()
if len(ibs.get_annot_groundtruth(qaid)) == 0:
print('WARNING: qaid=%r has no groundtruth' % (qaid,))
locals_ = testrun_pipeline_upto(qreq_, 'vsone_reranking')
cm_list = locals_['cm_list_SVER']
return ibs, qreq_, cm_list, qaid_list
def testdata_pre_weight_neighbors(defaultdb='testdb1', qaid_list=[1, 2], daid_list=None, codename='vsmany', cfgdict=None):
"""
TODO: replace testdata_pre_weight_neighbors with
>>> qreq_, args = plh.testdata_pre('weight_neighbors', defaultdb='testdb1',
>>> a=['default:qindex=0:1,dindex=0:5,hackerrors=False'],
>>> p=['default:codename=vsmany,bar_l2_on=True,fg_on=False'], verbose=True)
"""
if cfgdict is None:
cfgdict = dict(codename=codename)
import ibeis
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, default_qaids=qaid_list, default_daids=daid_list, p=p)
ibs = qreq_.ibs
locals_ = testrun_pipeline_upto(qreq_, 'weight_neighbors')
nns_list, nnvalid0_list = ut.dict_take(locals_, ['nns_list', 'nnvalid0_list'])
# qreq_, args = testdata_pre('weight_neighbors', defaultdb=defaultdb, p=['default:bar_l2_on=True,fg_on=False'])
return ibs, qreq_, nns_list, nnvalid0_list
def compare_data(Y_list_):
import ibeis
qreq_ = ibeis.testdata_qreq_(
defaultdb='Oxford',
a='oxford',
p='smk:nWords=[64000],nAssign=[1],SV=[False],can_match_sameimg=True,dim_size=None'
)
qreq_.ensure_data()
gamma1s = []
gamma2s = []
print(len(Y_list_))
print(len(qreq_.daids))
dinva = qreq_.dinva
bady = []
for Y in Y_list_:
aid = Y.aid
gamma1 = Y.gamma
if aid in dinva.aid_to_idx:
idx = dinva.aid_to_idx[aid]
gamma2 = dinva.gamma_list[idx]
gamma1s.append(gamma1)
gamma2s.append(gamma2)
else:
bady += [Y]
print(Y.nid)
# print(Y.qual)
# ibs = qreq_.ibs
# z = ibs.annots([a.aid for a in bady])
import plottool as pt
ut.qtensure()
gamma1s = np.array(gamma1s)
gamma2s = np.array(gamma2s)
sortx = gamma1s.argsort()
pt.plot(gamma1s[sortx], label='script')
pt.plot(gamma2s[sortx], label='pipe')
pt.legend()
def testdata_match_interact(**kwargs):
"""
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-testdata_match_interact --show --db PZ_MTEST --qaid 3
Example:
>>> # VIZ_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> import plottool as pt
>>> kwargs = {}
>>> self = testdata_match_interact(**kwargs)
>>> pt.show_if_requested()
"""
import ibeis
qreq_ = ibeis.testdata_qreq_(defaultdb='testdb1', t=['default:Knorm=3'])
ibs = qreq_.ibs
cm = qreq_.execute()[0]
cm.sortself()
aid2 = None
self = MatchInteraction(ibs, cm, aid2, mode=1, dodraw=False, qreq_=qreq_, **kwargs)
return self
def testdata_pre(stopnode, defaultdb='testdb1', p=['default'],
a=['default:qindex=0:1,dindex=0:5'], **kwargs):
""" New (1-1-2016) generic pipeline node testdata getter
Args:
stopnode (str):
defaultdb (str): (default = u'testdb1')
p (list): (default = [u'default:'])
a (list): (default = [u'default:qsize=1,dsize=4'])
Returns:
tuple: (ibs, qreq_, args)
CommandLine:
python -m ibeis.algo.hots._pipeline_helpers --exec-testdata_pre --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots._pipeline_helpers import * # NOQA
>>> stopnode = 'build_chipmatches'
>>> defaultdb = 'testdb1'
>>> p = ['default:']
>>> a = ['default:qindex=0:1,dindex=0:5']
>>> qreq_, args = testdata_pre(stopnode, defaultdb, p, a)
"""
import ibeis
from ibeis.algo.hots import pipeline
qreq_ = ibeis.testdata_qreq_(defaultdb=defaultdb, p=p, a=a, **kwargs)
locals_ = testrun_pipeline_upto(qreq_, stopnode)
func = getattr(pipeline, stopnode)
argnames = ut.get_argnames(func)
# Hack to ignore qreq_, and verbose
for ignore in ['qreq_', 'ibs', 'verbose']:
try:
argnames.remove(ignore)
except ValueError:
pass
tupname = '_Ret_' + stopnode.upper()
args = ut.dict_take_asnametup(locals_, argnames, name=tupname)
return qreq_, args
def photobombing_subset():
"""
CommandLine:
python -m ibeis.scripts.script_vsone photobombing_subset
"""
import ibeis
# pair_sample = ut.odict([
# ('top_gt', 4), ('mid_gt', 2), ('bot_gt', 2), ('rand_gt', 2),
# ('top_gf', 3), ('mid_gf', 2), ('bot_gf', 1), ('rand_gf', 2),
# ])
qreq_ = ibeis.testdata_qreq_(
defaultdb='PZ_Master1',
a=':mingt=2,species=primary',
# t='default:K=4,Knorm=1,score_method=csum,prescore_method=csum',
t='default:K=4,Knorm=1,score_method=csum,prescore_method=csum,QRH=True',
)
ibs = qreq_.ibs
# cm_list = qreq_.execute()
# infr = ibeis.AnnotInference.from_qreq_(qreq_, cm_list, autoinit=True)
# aid_pairs_ = infr._cm_training_pairs(rng=np.random.RandomState(42),
# **pair_sample)
# # ut.dict_hist(ut.flatten(am_tags))
# am_rowids = ibs._get_all_annotmatch_rowids()
# am_tags = ibs.get_annotmatch_case_tags(am_rowids)
# am_flags = ut.filterflags_general_tags(am_tags, has_any=['photobomb'])
# am_rowids_ = ut.compress(am_rowids, am_flags)
# aids1 = ibs.get_annotmatch_aid1(am_rowids_)
# aids2 = ibs.get_annotmatch_aid2(am_rowids_)
# pb_aids_pairs = list(zip(aids1, aids2))
# # aids = unique_pb_aids = ut.unique(ut.flatten(pb_aids_pairs))
# # ut.compress(unique_pb_aids, ibs.is_aid_unknown(unique_pb_aids))
# assert len(pb_aids_pairs) > 0
# # Keep only a random subset
# subset_idxs = list(range(len(aid_pairs_)))
# rng = np.random.RandomState(3104855634)
# num_max = len(pb_aids_pairs)
# if num_max < len(subset_idxs):
# subset_idxs = rng.choice(subset_idxs, size=num_max, replace=False)
# subset_idxs = sorted(subset_idxs)
# aid_pairs_ = ut.take(aid_pairs_, subset_idxs)
# aid_pairs_ += pb_aids_pairs
# unique_aids = ut.unique(ut.flatten(aid_pairs_))
# a1 = ibs.filter_annots_general(unique_aids, is_known=True, verbose=True, min_pername=2, has_none=['photobomb'])
# a2 = ibs.filter_annots_general(unique_aids, has_any=['photobomb'], verbose=True, is_known=True)
# a = sorted(set(a1 + a2))
# ibs.print_annot_stats(a)
# len(a)
a = [8, 27, 30, 86, 87, 90, 92, 94, 99, 103, 104, 106, 111, 217, 218, 242,
298, 424, 425, 456, 464, 465, 472, 482, 529, 559, 574, 585, 588, 592,
598, 599, 601, 617, 630, 645, 661, 664, 667, 694, 723, 724, 759, 768,
843, 846, 861, 862, 866, 933, 934, 980, 987, 1000, 1003, 1005, 1011,
1017, 1020, 1027, 1059, 1074, 1076, 1080, 1095, 1096, 1107, 1108,
1192, 1203, 1206, 1208, 1220, 1222, 1223, 1224, 1256, 1278, 1293,
1294, 1295, 1296, 1454, 1456, 1474, 1484, 1498, 1520, 1521, 1548,
1563, 1576, 1593, 1669, 1675, 1680, 1699, 1748, 1751, 1811, 1813,
1821, 1839, 1927, 1934, 1938, 1952, 1992, 2003, 2038, 2054, 2066,
2080, 2103, 2111, 2170, 2171, 2175, 2192, 2216, 2227, 2240, 2250,
2253, 2266, 2272, 2288, 2292, 2314, 2329, 2341, 2344, 2378, 2397,
2417, 2429, 2444, 2451, 2507, 2551, 2552, 2553, 2581, 2628, 2640,
2642, 2646, 2654, 2667, 2686, 2733, 2743, 2750, 2759, 2803, 2927,
3008, 3054, 3077, 3082, 3185, 3205, 3284, 3306, 3334, 3370, 3386,
3390, 3393, 3401, 3448, 3508, 3542, 3597, 3614, 3680, 3684, 3695,
3707, 3727, 3758, 3765, 3790, 3812, 3813, 3818, 3858, 3860, 3874,
3875, 3887, 3892, 3915, 3918, 3924, 3927, 3929, 3933, 3941, 3952,
3955, 3956, 3959, 4004, 4059, 4073, 4076, 4089, 4094, 4124, 4126,
4128, 4182, 4189, 4217, 4222, 4229, 4257, 4266, 4268, 4288, 4289,
4296, 4306, 4339, 4353, 4376, 4403, 4428, 4455, 4487, 4494, 4515,
4517, 4524, 4541, 4544, 4556, 4580, 4585, 4597, 4604, 4629, 4639,
4668, 4671, 4672, 4675, 4686, 4688, 4693, 4716, 4730, 4731, 4749,
4772, 4803, 4820, 4823, 4832, 4833, 4836, 4900, 4902, 4909, 4924,
4936, 4938, 4939, 4944, 5004, 5006, 5034, 5043, 5044, 5055, 5064,
5072, 5115, 5131, 5150, 5159, 5165, 5167, 5168, 5174, 5218, 5235,
5245, 5249, 5309, 5319, 5334, 5339, 5344, 5347, 5378, 5379, 5384,
5430, 5447, 5466, 5509, 5546, 5587, 5588, 5621, 5640, 5663, 5676,
5682, 5685, 5687, 5690, 5707, 5717, 5726, 5732, 5733, 5791, 5830,
5863, 5864, 5869, 5870, 5877, 5879, 5905, 5950, 6008, 6110, 6134,
6160, 6167, 6234, 6238, 6265, 6344, 6345, 6367, 6384, 6386, 6437,
6495, 6533, 6538, 6569, 6587, 6626, 6634, 6643, 6659, 6661, 6689,
6714, 6725, 6739, 6754, 6757, 6759, 6763, 6781, 6830, 6841, 6843,
6893, 6897, 6913, 6930, 6932, 6936, 6944, 6976, 7003, 7022, 7037,
7052, 7058, 7074, 7103, 7107, 7108, 7113, 7143, 7183, 7185, 7187,
7198, 7200, 7202, 7207, 7222, 7275, 7285, 7388, 7413, 7421, 7425,
7429, 7445, 7487, 7507, 7508, 7528, 7615, 7655, 7696, 7762, 7786,
7787, 7796, 7797, 7801, 7807, 7808, 7809, 7826, 7834, 7835, 7852,
7861, 7874, 7881, 7901, 7902, 7905, 7913, 7918, 7941, 7945, 7990,
7999, 8007, 8009, 8017, 8018, 8019, 8034, 8041, 8057, 8058, 8079,
8080, 8086, 8089, 8092, 8094, 8100, 8105, 8109, 8147, 8149, 8153,
8221, 8264, 8302, 8303, 8331, 8366, 8367, 8370, 8376, 8474, 8501,
8504, 8506, 8507, 8514, 8531, 8532, 8534, 8538, 8563, 8564, 8587,
8604, 8608, 8751, 8771, 8792, 9175, 9204, 9589, 9726, 9841, 10674,
12122, 12305, 12796, 12944, 12947, 12963, 12966, 13098, 13099, 13101,
13103, 13109, 13147, 13157, 13168, 13194, 13236, 13253, 13255, 13410,
13450, 13474, 13477, 13481, 13508, 13630, 13670, 13727, 13741, 13819,
13820, 13908, 13912, 13968, 13979, 14007, 14009, 14010, 14019, 14066,
14067, 14072, 14074, 14148, 14153, 14224, 14230, 14237, 14239, 14241,
14274, 14277, 14290, 14293, 14308, 14309, 14313, 14319, 14668, 14670,
14776, 14918, 14920, 14924, 15135, 15157, 15318, 15319, 15490, 15518,
15531, 15777, 15903, 15913, 16004, 16012, 16013, 16014, 16020, 16215,
16221, 16235, 16240, 16259, 16273, 16279, 16284, 16289, 16316, 16322,
16329, 16336, 16364, 16389, 16706, 16897, 16898, 16903, 16949, 17094,
17101, 17137, 17200, 17222, 17290, 17327, 17336]
from ibeis.dbio import export_subset
export_subset.export_annots(ibs, a, 'PZ_PB_RF_TRAIN')
