def load_internal_data():
r"""
wbia TestResult --db Oxford \
-p smk:nWords=[64000],nAssign=[1],SV=[False],can_match_sameimg=True,dim_size=None \
-a oxford \
--dev-mode
wbia TestResult --db GZ_Master1 \
-p smk:nWords=[64000],nAssign=[1],SV=[False],fg_on=False \
-a ctrl:qmingt=2 \
--dev-mode
"""
# from wbia.algo.smk.smk_pipeline import * # NOQA
import wbia
qreq_ = wbia.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)
logger.info('mAP = %.3f' % (mAP, ))
cm = cm_list[-1]
return qreq_, cm
def testdata_match_interact(**kwargs):
"""
CommandLine:
python -m wbia.viz.interact.interact_matches --test-testdata_match_interact --show --db PZ_MTEST --qaid 3
Example:
>>> # VIZ_DOCTEST
>>> from wbia.viz.interact.interact_matches import * # NOQA
>>> import wbia.plottool as pt
>>> kwargs = {}
>>> mx = ut.get_argval('--mx', type_=int, default=None)
>>> self = testdata_match_interact(mx=mx, **kwargs)
>>> pt.show_if_requested()
"""
import wbia
qreq_ = wbia.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)
self.start()
return self
def testdata_post_sver(
defaultdb='PZ_MTEST',
qaid_list=None,
daid_list=None,
codename='vsmany',
cfgdict=None,
):
"""
>>> from wbia.algo.hots._pipeline_helpers import * # NOQA
"""
# TODO: testdata_pre('end')
# from wbia.algo import Config
if cfgdict is None:
cfgdict = dict(codename=codename)
import wbia
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = wbia.testdata_qreq_(defaultdb=defaultdb,
default_qaids=qaid_list,
default_daids=daid_list,
p=p)
ibs = qreq_.ibs
locals_ = testrun_pipeline_upto(qreq_, 'end')
cm_list = locals_['cm_list_SVER']
# nnfilts_list = locals_['nnfilts_list']
return ibs, qreq_, cm_list
def benchmark_knn():
r"""
CommandLine:
python ~/code/wbia/wbia/algo/hots/tests/bench.py benchmark_knn --profile
Example:
>>> # DISABLE_DOCTEST
>>> from bench import * # NOQA
>>> result = benchmark_knn()
>>> print(result)
"""
from wbia.algo.hots import _pipeline_helpers as plh
from wbia.algo.hots.pipeline import nearest_neighbors
import wbia
verbose = True
qreq_ = wbia.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( # NOQA
qreq_, Kpad_list, impossible_daids_list, verbose=verbose)
def train_featscore_normalizer():
r"""
CommandLine:
python -m wbia --tf train_featscore_normalizer --show
# Write Encoder
python -m wbia --tf train_featscore_normalizer --db PZ_MTEST -t best -a default --fsvx=0 --threshx=1 --show
# Visualize encoder score adjustment
python -m wbia --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 wbia --tf draw_rank_cmc --db PZ_MTEST -a timectrl -t best:lnbnn_normer=[None,wulu] --show
python -m wbia --tf draw_rank_cmc --db PZ_MTEST -a default -t best:lnbnn_normer=[None,wulu] --show
# Compare in ipynb
python -m wbia --tf autogen_ipynb --ipynb --db PZ_MTEST -a default -t best:lnbnn_normer=[None,lnbnn_fg_0.9__featscore]
# Big Test
python -m wbia --tf draw_rank_cmc --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,lovb],lnbnn_norm_thresh=.5 --show
python -m wbia --tf draw_rank_cmc --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,jypz],lnbnn_norm_thresh=.1 --show
python -m wbia --tf draw_rank_cmc --db PZ_Master1 -a timectrl -t best:lnbnn_normer=[None,jypz],lnbnn_norm_thresh=0 --show
# Big Train
python -m wbia --tf learn_featscore_normalizer --db PZ_Master1 -a timectrl -t best:K=1 --fsvx=0 --threshx=1 --show
python -m wbia --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=1 --fsvx=0 --threshx=1 --show --ainfo
python -m wbia --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=1 --fsvx=0 --threshx=1 --show
python -m wbia --tf train_featscore_normalizer --db PZ_Master1 -a timectrl:has_none=photobomb -t best:K=3 --fsvx=0 --threshx=1 --show
Example:
>>> # SCRIPT
>>> from wbia.unstable.scorenorm import * # NOQA
>>> encoder = train_featscore_normalizer()
>>> encoder.visualize(figtitle=encoder.get_cfgstr())
>>> ut.show_if_requested()
"""
import wbia
# TODO: training / loading / general external models
qreq_ = wbia.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(
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 wbia.algo.hots._pipeline_helpers --exec-testdata_pre --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.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 wbia
from wbia.algo.hots import pipeline
qreq_ = wbia.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_pre_baselinefilter(defaultdb='testdb1',
qaid_list=None,
daid_list=None,
codename='vsmany'):
cfgdict = dict(codename=codename)
import wbia
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = wbia.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 testdata_inva():
"""
from wbia.algo.smk.inverted_index import * # NOQA
"""
import wbia
qreq_ = wbia.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 compare_data(Y_list_):
import wbia
qreq_ = wbia.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 = []
logger.info(len(Y_list_))
logger.info(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]
logger.info(Y.nid)
# logger.info(Y.qual)
# ibs = qreq_.ibs
# z = ibs.annots([a.aid for a in bady])
import wbia.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_pre_sver(defaultdb='PZ_MTEST', qaid_list=None, daid_list=None):
"""
>>> from wbia.algo.hots._pipeline_helpers import * # NOQA
"""
# TODO: testdata_pre('sver')
# from wbia.algo import Config
cfgdict = dict()
import wbia
p = 'default' + ut.get_cfg_lbl(cfgdict)
qreq_ = wbia.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 photobombing_subset():
"""
CommandLine:
python -m wbia.scripts.script_vsone photobombing_subset
"""
import wbia
# 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_ = wbia.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 = wbia.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 wbia.dbio import export_subset
export_subset.export_annots(ibs, a, 'PZ_PB_RF_TRAIN')
