def pie_testdb_ibs():
testdb_name = 'manta-test'
try:
ans_ibs = wbia.opendb(testdb_name)
aids = ans_ibs.get_valid_annots()
assert len(aids) > 3
return ans_ibs
except Exception:
print("PIE testdb does not exist; creating it with PIE's example images")
ans_ibs = wbia.opendb(testdb_name, allow_newdir=True)
test_image_folder = os.path.join(_PLUGIN_FOLDER, 'examples/manta-demo/test')
test_images = os.listdir(test_image_folder)
test_images = [fname for fname in test_images if fname.lower().endswith('.png')]
test_images = sorted(test_images)
gpaths = [os.path.join(test_image_folder, fname) for fname in test_images]
names = [fname.split('-')[0] for fname in test_images]
gid_list = ans_ibs.add_images(gpaths)
nid_list = ans_ibs.add_names(names)
species = ['Mobula birostris'] * len(gid_list)
# these images are pre-cropped aka trivial annotations
wh_list = ans_ibs.get_image_sizes(gid_list)
bbox_list = [[0, 0, w, h] for (w, h) in wh_list]
ans_ibs.add_annots(
gid_list, bbox_list=bbox_list, species_list=species, nid_list=nid_list
)
return ans_ibs
def experiment_init_db(tag):
import wbia
if tag in DBDIR_DICT:
dbdir = abspath(expanduser(DBDIR_DICT[tag]))
DB_DICT[tag] = wbia.opendb(dbdir=dbdir, web=False)
return DB_DICT.get(tag, None)
def testdata_nnindexer(dbname='testdb1', with_indexer=True, use_memcache=True):
r"""
Ignore:
>>> # ENABLE_DOCTEST
>>> from wbia.algo.hots.neighbor_index_cache import * # NOQA
>>> nnindexer, qreq_, ibs = testdata_nnindexer('PZ_Master1')
>>> S = np.cov(nnindexer.idx2_vec.T)
>>> import wbia.plottool as pt
>>> pt.ensureqt()
>>> pt.plt.imshow(S)
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.algo.hots.neighbor_index_cache import * # NOQA
>>> nnindexer, qreq_, ibs = testdata_nnindexer()
"""
import wbia
daid_list = [7, 8, 9, 10, 11]
ibs = wbia.opendb(db=dbname)
# use_memcache isn't use here because we aren't lazy loading the indexer
cfgdict = dict(fg_on=False)
qreq_ = ibs.new_query_request(daid_list,
daid_list,
use_memcache=use_memcache,
cfgdict=cfgdict)
if with_indexer:
# we do an explicit creation of an indexer for these tests
nnindexer = request_wbia_nnindexer(qreq_, use_memcache=use_memcache)
else:
nnindexer = None
return nnindexer, qreq_, ibs
def testdata_showchip():
import wbia
ibs = wbia.opendb(defaultdb='PZ_MTEST')
aid_list = ut.get_argval(('--aids', '--aid'), type_=list, default=None)
if aid_list is None:
aid_list = ibs.get_valid_aids()[0:4]
weight_label = ut.get_argval('--weight_label',
type_=str,
default='fg_weights')
annote = not ut.get_argflag('--no-annote')
kwargs = dict(ori=ut.get_argflag('--ori'),
weight_label=weight_label,
annote=annote)
kwargs['notitle'] = ut.get_argflag('--notitle')
kwargs['pts'] = ut.get_argflag('--drawpts')
kwargs['ell'] = True or ut.get_argflag('--drawell')
kwargs['ell_alpha'] = ut.get_argval('--ellalpha', default=0.4)
kwargs['ell_linewidth'] = ut.get_argval('--ell_linewidth', default=2)
kwargs['draw_lbls'] = ut.get_argval('--draw_lbls', default=True)
logger.info('kwargs = ' + ut.repr4(kwargs, nl=True))
default_config = dict(wbia.algo.Config.FeatureWeightConfig().parse_items())
cfgdict = ut.argparse_dict(default_config)
logger.info('[viz_chip.testdata] cfgdict = %r' % (cfgdict, ))
config2_ = cfgdict
logger.info('[viz_chip.testdata] aid_list = %r' % (aid_list, ))
return ibs, aid_list, kwargs, config2_
def load_oxford_wbia():
import wbia
ibs = wbia.opendb('Oxford')
dim_size = None
_dannots = ibs.annots(ibs.filter_annots_general(has_none='query'),
config=dict(dim_size=dim_size))
_qannots = ibs.annots(ibs.filter_annots_general(has_any='query'),
config=dict(dim_size=dim_size))
with ut.Timer('reading info'):
vecs_list = _dannots.vecs
kpts_list = _dannots.kpts
nfeats_list = np.array(_dannots.num_feats)
with ut.Timer('stacking info'):
all_vecs = np.vstack(vecs_list)
all_kpts = np.vstack(kpts_list)
offset_list = np.hstack(([0], nfeats_list.cumsum())).astype(np.int64)
# data_annots = reorder_annots(_dannots, data_uri_order)
data_uri_order = get_annots_imgid(_dannots)
query_uri_order = get_annots_imgid(_qannots)
data = {
'offset_list': offset_list,
'all_kpts': all_kpts,
'all_vecs': all_vecs,
'data_uri_order': data_uri_order,
'query_uri_order': query_uri_order,
}
return data
def demodata_infr2(defaultdb='PZ_MTEST'):
defaultdb = 'PZ_MTEST'
import wbia
ibs = wbia.opendb(defaultdb=defaultdb)
annots = ibs.annots()
names = list(annots.group_items(annots.nids).values())[0:20]
def dummy_phi(c, n):
x = np.arange(n)
phi = c * x / (c * x + 1)
phi = phi / phi.sum()
phi = np.diff(phi)
return phi
phis = {c: dummy_phi(c, 30) for c in range(1, 4)}
aids = ut.flatten(names)
infr = wbia.AnnotInference(ibs, aids, autoinit=True)
infr.init_termination_criteria(phis)
infr.init_refresh_criteria()
# Partially review
n1, n2, n3, n4 = names[0:4]
for name in names[4:]:
for a, b in ut.itertwo(name.aids):
infr.add_feedback((a, b), POSTV)
for name1, name2 in it.combinations(names[4:], 2):
infr.add_feedback((name1.aids[0], name2.aids[0]), NEGTV)
return infr
def load_ordered_annots(data_uri_order, query_uri_order):
# Open the wbia version of oxford
import wbia
ibs = wbia.opendb('Oxford')
def reorder_annots(_annots, uri_order):
intern_uris = get_annots_imgid(_annots)
lookup = ut.make_index_lookup(intern_uris)
_reordered = _annots.take(ut.take(lookup, uri_order))
return _reordered
# Load database annotations and reorder them to agree with internals
_dannots = ibs.annots(ibs.filter_annots_general(has_none='query'))
data_annots = reorder_annots(_dannots, data_uri_order)
# Load query annototations and reorder to standard order
_qannots = ibs.annots(ibs.filter_annots_general(has_any='query'))
query_annots = reorder_annots(_qannots, query_uri_order)
# Map each query annot to its corresponding data index
dgid_to_dx = ut.make_index_lookup(data_annots.gids)
qx_to_dx = ut.take(dgid_to_dx, query_annots.gids)
return ibs, query_annots, data_annots, qx_to_dx
def testdata_ibs():
r""""""
import wbia
ibs = wbia.opendb('testdb1')
qreq_ = None
return ibs, qreq_
def testdata_ibs():
r""""""
import wbia
ibs = wbia.opendb('testdb1')
config2_ = None
return ibs, config2_
def shark_misc():
import wbia
ibs = wbia.opendb('WS_ALL')
aid_list = ibs.get_valid_aids()
flag_list = ibs.get_annot_been_adjusted(aid_list)
adjusted_aids = ut.compress(aid_list, flag_list)
return adjusted_aids
def assigner_testdb_ibs():
import wbia
from wbia import sysres
dbdir = sysres.ensure_testdb_assigner()
# dbdir = '/data/testdb_assigner'
ibs = wbia.opendb(dbdir=dbdir)
return ibs
def test_lnbnn():
import wbia
ibs = wbia.opendb('PZ_MTEST')
annots = ibs.annots()
qaids = daids = annots.aids
qreq = ibs.new_query_request(qaids, daids)
cm_list = qreq.execute(use_cache=False) # NOQA
def testdata_ibs():
r"""
Auto-docstr for 'testdata_ibs'
"""
import wbia
ibs = wbia.opendb('testdb1')
qreq_ = None
return ibs, qreq_
def testdata_my_exmaples(index):
r"""
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.devcases import * # NOQA
>>> index = 1
"""
import wbia
from uuid import UUID
ibs = wbia.opendb('GZ_ALL')
vsone_pair_examples = [
[
UUID('8415b50f-2c98-0d52-77d6-04002ff4d6f8'),
UUID('308fc664-7990-91ad-0576-d2e8ea3103d0'),
],
[
UUID('490f76bf-7616-54d5-576a-8fbc907e46ae'),
UUID('2046509f-0a9f-1470-2b47-5ea59f803d4b'),
],
[
UUID('5cdf68ab-be49-ee3f-94d8-5483772c8618'),
UUID('879977a7-b841-d223-dd91-761dfa58d486'),
],
]
gf_mapping = {
UUID('8415b50f-2c98-0d52-77d6-04002ff4d6f8'):
[UUID('38211759-8fa7-875b-1f3e-39a630653f66')],
UUID('490f76bf-7616-54d5-576a-8fbc907e46ae'):
[UUID('58920d6e-31ba-307c-2ac8-e56aff2b2b9e')
], # other bad_aid is actually a good partial match
UUID('5cdf68ab-be49-ee3f-94d8-5483772c8618'):
[UUID('5a8c8ad7-873a-e6ed-98df-56a452e0a93e')],
}
# ibs.get_annot_visual_uuids([36, 3])
vuuid_pair = vsone_pair_examples[index]
vuuid1, vuuid2 = vuuid_pair
aid1, aid2 = ibs.get_annot_aids_from_visual_uuid(vuuid_pair)
assert aid1 is not None
assert aid2 is not None
# daids = ibs.get_valid_aids()
tn_vuuid = gf_mapping.get(vuuid1)
if tn_vuuid is None:
qaids = [aid1]
find_close_incorrect_match(ibs, qaids)
logger.info('baste the result in gf_mapping')
return
tn_aids = ibs.get_annot_aids_from_visual_uuid(tn_vuuid)
tn_aid = tn_aids[0]
return ibs, aid1, aid2, tn_aid
def demodata_mtest_infr(state='empty'):
import wbia
ibs = wbia.opendb(db='PZ_MTEST')
annots = ibs.annots()
names = list(annots.group_items(annots.nids).values())
ut.shuffle(names, rng=321)
test_aids = ut.flatten(names[1::2])
infr = wbia.AnnotInference(ibs, test_aids, autoinit=True)
infr.reset(state=state)
return infr
def testdata_newqreq(defaultdb='testdb1'):
"""
Returns:
(wbia.IBEISController, list, list)
"""
import wbia
ibs = wbia.opendb(defaultdb=defaultdb)
qaid_list = [1]
daid_list = [1, 2, 3, 4, 5]
return ibs, qaid_list, daid_list
def slow_merge_test():
r"""
CommandLine:
python -m wbia.dbio.export_subset --test-slow_merge_test
Example:
>>> # SLOW_DOCTEST
>>> from wbia.dbio.export_subset import * # NOQA
>>> result = slow_merge_test()
>>> print(result)
"""
from wbia.dbio import export_subset
import wbia
ibs1 = wbia.opendb('testdb2')
ibs1.fix_invalid_annotmatches()
ibs_dst = wbia.opendb(db='testdb_dst2',
allow_newdir=True,
delete_ibsdir=True)
export_subset.merge_databases(ibs1, ibs_dst)
# ibs_src = ibs1
check_merge(ibs1, ibs_dst)
ibs2 = wbia.opendb('testdb1')
ibs1.print_dbinfo()
ibs2.print_dbinfo()
ibs_dst.print_dbinfo()
ibs_dst.print_dbinfo()
export_subset.merge_databases(ibs2, ibs_dst)
# ibs_src = ibs2
check_merge(ibs2, ibs_dst)
ibs3 = wbia.opendb('PZ_MTEST')
export_subset.merge_databases(ibs3, ibs_dst)
# ibs_src = ibs2
check_merge(ibs3, ibs_dst)
ibs_dst.print_dbinfo()
return ibs_dst
def __setstate__(request, state_dict):
import wbia
dbdir = state_dict['dbdir']
del state_dict['dbdir']
params = state_dict['params']
depc = wbia.opendb(dbdir=dbdir, web=False).depc
configclass = depc.configclass_dict[state_dict['tablename']]
config = configclass(**params)
state_dict['depc'] = depc
state_dict['config'] = config
request.__dict__.update(state_dict)
def export_data(ibs, gid_list, aid_list, nid_list, new_dbpath=None):
"""
exports a subset of data and other required info
Args:
ibs (IBEISController): wbia controller object
gid_list (list): list of image rowids
aid_list (list): list of annotation rowids
nid_list (list): list of name rowids
imgsetid_list (list): list of imageset rowids
gsgrid_list (list): list of imageset-image pairs rowids
new_dbpath (None): (default = None)
Returns:
str: new_dbpath
"""
import wbia
imgsetid_list = ut.unique_unordered(
ut.flatten(ibs.get_image_imgsetids(gid_list)))
gsgrid_list = ut.unique_unordered(
ut.flatten(ibs.get_image_gsgrids(gid_list)))
# TODO: write SQL query to do this
am_rowids = ibs._get_all_annotmatch_rowids()
flags1_list = [
aid in set(aid_list) for aid in ibs.get_annotmatch_aid1(am_rowids)
]
flags2_list = [
aid in set(aid_list) for aid in ibs.get_annotmatch_aid2(am_rowids)
]
flag_list = ut.and_lists(flags1_list, flags2_list)
am_rowids = ut.compress(am_rowids, flag_list)
# am_rowids = ibs.get_valid_aids(ibs.get_valid_aids())
rowid_subsets = {
const.ANNOTATION_TABLE: aid_list,
const.NAME_TABLE: nid_list,
const.IMAGE_TABLE: gid_list,
const.ANNOTMATCH_TABLE: am_rowids,
const.GSG_RELATION_TABLE: gsgrid_list,
const.IMAGESET_TABLE: imgsetid_list,
}
ibs_dst = wbia.opendb(dbdir=new_dbpath, allow_newdir=True)
# Main merge driver
merge_databases(ibs, ibs_dst, rowid_subsets=rowid_subsets)
logger.info('Exported to %r' % (new_dbpath, ))
return new_dbpath
def testdata_showname():
import wbia
ibs = wbia.opendb(defaultdb='testdb1')
default = None
if ibs.dbname == 'testdb1':
default = 'easy'
name_text = ut.get_argval('--name', type_=str, default=default)
if name_text is None:
nid = 1
else:
nid = ibs.get_name_rowids_from_text(name_text)
in_image = not ut.get_argflag('--no-inimage')
index_list = ut.get_argval('--index_list', type_=list, default=None)
return ibs, nid, in_image, index_list
def __setstate__(ibs, state):
# Hack to allow for wbia objects to be pickled
import wbia
dbdir = state['dbdir']
machine_name = state.pop('machine_name')
try:
assert (
machine_name == ut.get_computer_name()
), 'wbia objects can only be picked and unpickled on the same machine'
except AssertionError as ex:
iswarning = ut.checkpath(dbdir)
ut.printex(ex, iswarning=iswarning)
if not iswarning:
raise
ibs2 = wbia.opendb(dbdir=dbdir, web=False)
ibs.__dict__.update(**ibs2.__dict__)
def HARDCODE_SHOW_PB_PAIR():
"""
python -m wbia.viz.viz_chip HARDCODE_SHOW_PB_PAIR --show
Example:
>>> # SCRIPT
>>> from wbia.viz.viz_chip import * # NOQA
>>> import wbia.plottool as pt
>>> HARDCODE_SHOW_PB_PAIR()
>>> pt.show_if_requested()
"""
# TODO: generalize into testdata_annotmatches which filters ams propertly
# Then a function to show these ams
import wbia
import wbia.viz
has_any = ut.get_argval('--has_any', default=['photobomb'])
index = ut.get_argval('--index', default=0)
ibs = wbia.opendb(defaultdb='PZ_Master1')
ams = ibs._get_all_annotmatch_rowids()
tags = ibs.get_annotmatch_case_tags(ams)
flags = ut.filterflags_general_tags(tags, has_any=has_any)
selected_ams = ut.compress(ams, flags)
aid_pairs = ibs.get_annotmatch_aids(selected_ams)
aid1, aid2 = aid_pairs[index]
import wbia.plottool as pt
fnum = 1
if ut.get_argflag('--match'):
request = ibs.depc_annot.new_request('vsone', [aid1], [aid2])
res_list2 = request.execute()
match = res_list2[0]
match.show_single_annotmatch(
qreq_=request,
vert=False,
colorbar_=False,
notitle=True,
draw_lbl=False,
draw_border=False,
)
else:
chip1, chip2 = ibs.get_annot_chips([aid1, aid2])
pt.imshow(chip1, pnum=(1, 2, 1), fnum=fnum)
pt.imshow(chip2, pnum=(1, 2, 2), fnum=fnum)
def testdata_multichips():
import wbia
ibs = wbia.opendb(defaultdb='testdb1')
nid = ut.get_argval('--nid', type_=int, default=None)
tags = ut.get_argval('--tags', type_=list, default=None)
if nid is not None:
aid_list = ibs.get_name_aids(nid)
elif tags is not None:
index = ut.get_argval('--index', default=0)
aid_list = ibs.filter_aidpairs_by_tags(any_tags=tags)[index]
else:
# aid_list = ut.get_argval('--aids', type_=list, default=[1, 2, 3])
aid_list = wbia.testdata_aids(default_aids=[1, 2, 3], ibs=ibs)
in_image = not ut.get_argflag('--no-inimage')
return ibs, aid_list, in_image
def start(actor, dbdir, aids='all', config={}, **kwargs):
import wbia
assert dbdir is not None, 'must specify dbdir'
assert actor.infr is None, 'AnnotInference already running'
ibs = wbia.opendb(dbdir=dbdir,
use_cache=False,
web=False,
force_serial=True)
# Create the AnnotInference
log.info('starting via actor with ibs = %r' % (ibs, ))
actor.infr = wbia.AnnotInference(ibs=ibs, aids=aids, autoinit=True)
actor.infr.print('started via actor')
actor.infr.print('config = {}'.format(ut.repr3(config)))
# Configure query_annot_infr
for key in config:
actor.infr.params[key] = config[key]
# Initialize
# TODO: Initialize state from staging reviews after annotmatch
# timestamps (in case of crash)
actor.infr.print('Initializing infr tables')
table = kwargs.get('init', 'staging')
actor.infr.reset_feedback(table, apply=True)
actor.infr.ensure_mst()
actor.infr.apply_nondynamic_update()
actor.infr.print('infr.status() = {}'.format(
ut.repr4(actor.infr.status())))
# Load random forests (TODO: should this be config specifiable?)
actor.infr.print('loading published models')
try:
actor.infr.load_published()
except Exception:
pass
# Start actor.infr Main Loop
actor.infr.print('start id review')
actor.infr.start_id_review()
return 'initialized'
def train_part_detector():
"""
Problem:
healthy sharks usually have a mostly whole body shot
injured sharks usually have a close up shot.
This distribution of images is likely what the injur-shark net is picking up on.
The goal is to train a detector that looks for things that look
like the distribution of injured sharks.
We will run this on healthy sharks to find the parts of
"""
import wbia
ibs = wbia.opendb('WS_ALL')
imgset = ibs.imagesets(text='Injured Sharks')
injured_annots = imgset.annots[0] # NOQA
# config = {
# 'dim_size': (224, 224),
# 'resize_dim': 'wh'
# }
from pydarknet import Darknet_YOLO_Detector
data_path = ibs.export_to_xml()
output_path = join(ibs.get_cachedir(), 'training', 'localizer')
ut.ensuredir(output_path)
dark = Darknet_YOLO_Detector()
results = dark.train(data_path, output_path)
del dark
localizer_weight_path, localizer_config_path, localizer_class_path = results
classifier_model_path = ibs.classifier_train()
labeler_model_path = ibs.labeler_train()
output_path = join(ibs.get_cachedir(), 'training', 'detector')
ut.ensuredir(output_path)
ut.copy(localizer_weight_path, join(output_path, 'localizer.weights'))
ut.copy(localizer_config_path, join(output_path, 'localizer.config'))
ut.copy(localizer_class_path, join(output_path, 'localizer.classes'))
ut.copy(classifier_model_path, join(output_path, 'classifier.npy'))
ut.copy(labeler_model_path, join(output_path, 'labeler.npy'))
def cfg_deepcopy_test():
"""
TESTING FUNCTION
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.algo.hots.query_request import * # NOQA
>>> result = cfg_deepcopy_test()
>>> print(result)
"""
import wbia
ibs = wbia.opendb('testdb1')
cfg1 = ibs.cfg.query_cfg
cfg2 = cfg1.deepcopy()
cfg3 = cfg2
assert cfg1.get_cfgstr() == cfg2.get_cfgstr()
assert cfg2.sv_cfg is not cfg1.sv_cfg
assert cfg3.sv_cfg is cfg2.sv_cfg
cfg2.update_query_cfg(sv_on=False)
assert cfg1.get_cfgstr() != cfg2.get_cfgstr()
assert cfg2.get_cfgstr() == cfg3.get_cfgstr()
def subindexer_time_experiment():
"""
builds plot of number of annotations vs indexer build time.
TODO: time experiment
"""
import wbia
import utool as ut
from vtool._pyflann_backend import pyflann as pyflann
import wbia.plottool as pt
ibs = wbia.opendb(db='PZ_Master0')
daid_list = ibs.get_valid_aids()
count_list = []
time_list = []
flann_params = vt.get_flann_params()
for count in ut.ProgressIter(range(1, 301)):
daids_ = daid_list[:]
np.random.shuffle(daids_)
daids = daids_[0:count]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann = pyflann.FLANN()
flann.build_index(vecs, **flann_params)
count_list.append(count)
time_list.append(t.ellapsed)
count_arr = np.array(count_list)
time_arr = np.array(time_list)
pt.plot2(
count_arr,
time_arr,
marker='-',
equal_aspect=False,
x_label='num_annotations',
y_label='FLANN build time',
)
def testsdata_match_verification(defaultdb='testdb1', aid1=1, aid2=2):
r"""
CommandLine:
main.py --imgsetid 2
main.py --imgsetid 13 --db PZ_MUGU_19
CommandLine:
python -m wbia.viz.interact.interact_name --test-testsdata_match_verification --show
python -m wbia.viz.interact.interact_name --test-testsdata_match_verification --aid1 2 --aid2 3 --show
# Merge case
python -m wbia.viz.interact.interact_name --test-testsdata_match_verification --show --db PZ_MTEST --aid1 1 --aid2 30
# Split case
python -m wbia.viz.interact.interact_name --test-testsdata_match_verification --show --db PZ_MTEST --aid1 30 --aid2 32
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.viz.interact.interact_name import * # NOQA
>>> self = testsdata_match_verification()
>>> # verify results
>>> ut.quit_if_noshow()
>>> self.show_page()
>>> ut.show_if_requested()
"""
# from wbia.viz.interact.interact_name import * # NOQA
import wbia
# ibs = wbia.opendb(defaultdb='PZ_Master0')
ibs = wbia.opendb(defaultdb=defaultdb)
# aid1 = ut.get_argval('--aid1', int, 14)
# aid2 = ut.get_argval('--aid2', int, 5545)
aid1 = ut.get_argval('--aid1', int, aid1)
aid2 = ut.get_argval('--aid2', int, aid2)
self = MatchVerificationInteraction(ibs, aid1, aid2, dodraw=False)
return self
def testdata_distinctiveness():
"""
Example:
>>> # SLOW_DOCTEST
>>> from wbia.algo.hots.distinctiveness_normalizer import * # NOQA
>>> dstcnvs_normer, qreq_ = testdata_distinctiveness()
"""
import wbia
# build test data
db = ut.get_argval('--db', str, 'testdb1')
species = ut.get_argval('--species', str, None)
aid = ut.get_argval('--aid', int, None)
ibs = wbia.opendb(db)
if aid is not None:
species = ibs.get_annot_species_texts(aid)
if species is None:
if db == 'testdb1':
species = wbia.const.TEST_SPECIES.ZEB_PLAIN
daids = ibs.get_valid_aids(species=species)
qaids = [aid] if aid is not None else daids
qreq_ = ibs.new_query_request(qaids, daids)
dstcnvs_normer = request_wbia_distinctiveness_normalizer(qreq_)
return dstcnvs_normer, qreq_
def dev_train_distinctiveness(species=None):
r"""
Args:
ibs (IBEISController): wbia controller object
species (None):
CommandLine:
python -m wbia.algo.hots.distinctiveness_normalizer --test-dev_train_distinctiveness
alias dev_train_distinctiveness='python -m wbia.algo.hots.distinctiveness_normalizer --test-dev_train_distinctiveness'
# Publishing (uses cached normalizers if available)
dev_train_distinctiveness --species GZ --publish
dev_train_distinctiveness --species PZ --publish
dev_train_distinctiveness --species PZ --retrain
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.distinctiveness_normalizer import * # NOQA
>>> import wbia
>>> species = ut.get_argval('--species', str, 'zebra_grevys')
>>> dev_train_distinctiveness(species)
"""
import wbia
# if 'species' not in vars() or species is None:
# species = 'zebra_grevys'
if species == 'zebra_grevys':
dbname = 'GZ_ALL'
elif species == 'zebra_plains':
dbname = 'PZ_Master0'
ibs = wbia.opendb(dbname)
global_distinctdir = ibs.get_global_distinctiveness_modeldir()
cachedir = global_distinctdir
dstcnvs_normer = DistinctivnessNormalizer(species, cachedir=cachedir)
try:
if ut.get_argflag('--retrain'):
raise IOError('force cache miss')
with ut.Timer('loading distinctiveness'):
dstcnvs_normer.load(cachedir)
# Cache hit
logger.info('distinctivness model cache hit')
except IOError:
logger.info('distinctivness model cache miss')
with ut.Timer('training distinctiveness'):
# Need to train
# Add one example from each name
# TODO: add one exemplar per viewpoint for each name
# max_vecs = 1E6
# max_annots = 975
max_annots = 975
# ibs.fix_and_clean_database()
nid_list = ibs.get_valid_nids()
aids_list = ibs.get_name_aids(nid_list)
# remove junk
aids_list = ibs.unflat_map(ibs.filter_junk_annotations, aids_list)
# remove empty
aids_list = [aids for aids in aids_list if len(aids) > 0]
num_annots_list = list(map(len, aids_list))
aids_list = ut.sortedby(aids_list, num_annots_list, reverse=True)
# take only one annot per name
aid_list = ut.get_list_column(aids_list, 0)
# Keep only a certain number of annots for distinctiveness mapping
aid_list_ = ut.listclip(aid_list, max_annots)
logger.info('total num named annots = %r' % (sum(num_annots_list)))
logger.info(
'training distinctiveness using %d/%d singleton annots' %
(len(aid_list_), len(aid_list)))
# vec
# FIXME: qreq_ params for config rowid
vecs_list = ibs.get_annot_vecs(aid_list_)
num_vecs = sum(list(map(len, vecs_list)))
logger.info('num_vecs = %r' % (num_vecs, ))
vecs = np.vstack(vecs_list)
logger.info('vecs size = %r' % (ut.get_object_size_str(vecs), ))
dstcnvs_normer.init_support(vecs)
dstcnvs_normer.save(global_distinctdir)
if ut.get_argflag('--publish'):
dstcnvs_normer.publish()