def cached_impaint(
bgr_img,
cached_mask_fpath=None,
label_colors=None,
init_mask=None,
aug=False,
refine=False,
):
import vtool as vt
if cached_mask_fpath is None:
cached_mask_fpath = 'image_' + ut.hashstr_arr(bgr_img) + '.png'
if aug:
cached_mask_fpath += '.' + ut.hashstr_arr(bgr_img)
if label_colors is not None:
cached_mask_fpath += ut.hashstr_arr(label_colors)
cached_mask_fpath += '.png'
# cached_mask_fpath = 'tmp_mask.png'
if refine or not ut.checkpath(cached_mask_fpath):
if refine and ut.checkpath(cached_mask_fpath):
if init_mask is None:
init_mask = vt.imread(cached_mask_fpath, grayscale=True)
custom_mask = impaint_mask(bgr_img,
label_colors=label_colors,
init_mask=init_mask)
vt.imwrite(cached_mask_fpath, custom_mask)
else:
custom_mask = vt.imread(cached_mask_fpath, grayscale=True)
return custom_mask
def new_word_index(aid_list=[],
vecs_list=[],
flann_params={},
flann_cachedir=None,
indexer_cfgstr='',
hash_rowids=True,
use_cache=not NOCACHE_WORD,
use_params_hash=True):
print('[windex] building WordIndex object')
_check_input(aid_list, vecs_list)
# Create indexes into the input aids
ax_list = np.arange(len(aid_list))
idx2_vec, idx2_ax, idx2_fx = invert_index(vecs_list, ax_list)
if hash_rowids:
# Fingerprint
aids_hashstr = utool.hashstr_arr(aid_list, '_AIDS')
cfgstr = aids_hashstr + indexer_cfgstr
else:
# Dont hash rowids when given enough info in indexer_cfgstr
cfgstr = indexer_cfgstr
# Build/Load the flann index
flann = nntool.flann_cache(
idx2_vec, **{
'cache_dir': flann_cachedir,
'cfgstr': cfgstr,
'flann_params': flann_params,
'use_cache': use_cache,
'use_params_hash': use_params_hash
})
ax2_aid = np.array(aid_list)
windex = WordIndex(ax2_aid, idx2_vec, idx2_ax, idx2_fx, flann)
return windex
def load_qcx2_res(ibs, qrid_list, nocache=False):
'Prefrosm / loads all queries'
qreq = mc3.quickly_ensure_qreq(ibs, qrids=qrid_list)
# Build query big cache rowid
query_rowid = qreq.get_rowid()
hs_rowid = ibs.get_db_name()
qcxs_rowid = utool.hashstr_arr(qrid_list, lbl='_qcxs')
qres_rowid = hs_rowid + query_rowid + qcxs_rowid
cache_dir = join(ibs.dirs.cache_dir, 'query_results_bigcache')
print('[rr2] load_qcx2_res(): %r' % qres_rowid)
io_kwargs = dict(dpath=cache_dir, fname='query_results', rowid=qres_rowid, ext='.cPkl')
# Return cache if available
if not params.args.nocache_query and (not nocache):
qrid2_qres = io.smart_load(**io_kwargs)
if qrid2_qres is not None:
print('[rr2] * cache hit')
return qrid2_qres
print('[rr2] * cache miss')
else:
print('[rr2] * cache off')
# Individually load / compute queries
if isinstance(qrid_list, list):
qcx_set = set(qrid_list)
else:
qcx_set = set(qrid_list.tolist())
qcx_max = max(qrid_list) + 1
qrid2_qres = [ibs.query(qrid) if qrid in qcx_set else None for qrid in xrange(qcx_max)]
# Save to the cache
print('[rr2] Saving query_results to bigcache: %r' % qres_rowid)
utool.ensuredir(cache_dir)
io.smart_save(qrid2_qres, **io_kwargs)
return qrid2_qres
def new_word_index(aid_list=[], vecs_list=[], flann_params={},
flann_cachedir=None, indexer_cfgstr='', hash_rowids=True,
use_cache=not NOCACHE_WORD, use_params_hash=True):
print('[windex] building WordIndex object')
_check_input(aid_list, vecs_list)
# Create indexes into the input aids
ax_list = np.arange(len(aid_list))
idx2_vec, idx2_ax, idx2_fx = invert_index(vecs_list, ax_list)
if hash_rowids:
# Fingerprint
aids_hashstr = utool.hashstr_arr(aid_list, '_AIDS')
cfgstr = aids_hashstr + indexer_cfgstr
else:
# Dont hash rowids when given enough info in indexer_cfgstr
cfgstr = indexer_cfgstr
# Build/Load the flann index
flann = nntool.flann_cache(idx2_vec, **{
'cache_dir': flann_cachedir,
'cfgstr': cfgstr,
'flann_params': flann_params,
'use_cache': use_cache,
'use_params_hash': use_params_hash})
ax2_aid = np.array(aid_list)
windex = WordIndex(ax2_aid, idx2_vec, idx2_ax, idx2_fx, flann)
return windex
def get_cfgstr(nnindexer, noquery=False):
r""" returns string which uniquely identified configuration and support data
Args:
noquery (bool): if True cfgstr is only relevant to building the
index. No search params are returned (default = False)
Returns:
str: flann_cfgstr
CommandLine:
python -m wbia.algo.hots.neighbor_index --test-get_cfgstr
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.neighbor_index import * # NOQA
>>> import wbia
>>> cfgdict = dict(fg_on=False)
>>> qreq_ = wbia.testdata_qreq_(defaultdb='testdb1', p='default:fg_on=False')
>>> qreq_.load_indexer()
>>> nnindexer = qreq_.indexer
>>> noquery = True
>>> flann_cfgstr = nnindexer.get_cfgstr(noquery)
>>> result = ('flann_cfgstr = %s' % (str(flann_cfgstr),))
>>> print(result)
flann_cfgstr = _FLANN((algo=kdtree,seed=42,t=8,))_VECS((11260,128)gj5nea@ni0%f3aja)
"""
flann_cfgstr_list = []
use_params_hash = True
use_data_hash = True
if use_params_hash:
flann_defaults = vt.get_flann_params(
nnindexer.flann_params['algorithm'])
# flann_params_clean = flann_defaults.copy()
flann_params_clean = ut.sort_dict(flann_defaults)
ut.update_existing(flann_params_clean, nnindexer.flann_params)
if noquery:
ut.delete_dict_keys(flann_params_clean, ['checks'])
shortnames = dict(algorithm='algo',
checks='chks',
random_seed='seed',
trees='t')
short_params = ut.odict([
(shortnames.get(key, key), str(val)[0:7])
for key, val in six.iteritems(flann_params_clean)
])
flann_valsig_ = ut.repr2(short_params,
nl=False,
explicit=True,
strvals=True)
flann_valsig_ = flann_valsig_.lstrip('dict').replace(' ', '')
# flann_valsig_ = str(list(flann_params.values()))
# flann_valsig = ut.remove_chars(flann_valsig_, ', \'[]')
flann_cfgstr_list.append('_FLANN(' + flann_valsig_ + ')')
if use_data_hash:
vecs_hashstr = ut.hashstr_arr(nnindexer.idx2_vec, '_VECS')
flann_cfgstr_list.append(vecs_hashstr)
flann_cfgstr = ''.join(flann_cfgstr_list)
return flann_cfgstr
def make_new_dbpath(ibs, id_label, id_list):
"""
Creates a new database path unique to the exported subset of ids.
"""
import ibeis
tag_hash = ut.hashstr_arr(id_list, hashlen=8, alphabet=ut.ALPHABET_27)
base_fmtstr = ibs.get_dbname() + '_' + id_label + 's=' + \
tag_hash.replace('(', '_').replace(')', '_') + '_%d'
dpath = ibeis.get_workdir()
new_dbpath = ut.non_existing_path(base_fmtstr, dpath)
return new_dbpath
def get_cfgstr(nnindexer, noquery=False):
r""" returns string which uniquely identified configuration and support data
Args:
noquery (bool): if True cfgstr is only relevant to building the
index. No search params are returned (default = False)
Returns:
str: flann_cfgstr
CommandLine:
python -m ibeis.algo.hots.neighbor_index --test-get_cfgstr
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.neighbor_index import * # NOQA
>>> import ibeis
>>> cfgdict = dict(fg_on=False)
>>> qreq_ = ibeis.testdata_qreq_(defaultdb='testdb1', p='default:fg_on=False')
>>> qreq_.load_indexer()
>>> nnindexer = qreq_.indexer
>>> noquery = True
>>> flann_cfgstr = nnindexer.get_cfgstr(noquery)
>>> result = ('flann_cfgstr = %s' % (str(flann_cfgstr),))
>>> print(result)
flann_cfgstr = _FLANN((algo=kdtree,seed=42,t=8,))_VECS((11260,128)gj5nea@ni0%f3aja)
"""
flann_cfgstr_list = []
use_params_hash = True
use_data_hash = True
if use_params_hash:
flann_defaults = vt.get_flann_params(nnindexer.flann_params['algorithm'])
#flann_params_clean = flann_defaults.copy()
flann_params_clean = ut.sort_dict(flann_defaults)
ut.updateif_haskey(flann_params_clean, nnindexer.flann_params)
if noquery:
ut.delete_dict_keys(flann_params_clean, ['checks'])
shortnames = dict(algorithm='algo', checks='chks', random_seed='seed', trees='t')
short_params = ut.odict([(shortnames.get(key, key), str(val)[0:7])
for key, val in six.iteritems(flann_params_clean)])
flann_valsig_ = ut.dict_str(
short_params, nl=False, explicit=True, strvals=True)
flann_valsig_ = flann_valsig_.lstrip('dict').replace(' ', '')
#flann_valsig_ = str(list(flann_params.values()))
#flann_valsig = ut.remove_chars(flann_valsig_, ', \'[]')
flann_cfgstr_list.append('_FLANN(' + flann_valsig_ + ')')
if use_data_hash:
vecs_hashstr = ut.hashstr_arr(nnindexer.idx2_vec, '_VECS')
flann_cfgstr_list.append(vecs_hashstr)
flann_cfgstr = ''.join(flann_cfgstr_list)
return flann_cfgstr
def get_indexed_cfgstr(ibs, aid_list):
"""
Creates a config string for the input into the nearest neighbors index
It is based off of the features which were computed for it and the indexes
of the input annotations.
TODO: We should probably use the Annotation UUIDS rather than the ROWIDs
to compute this configstr
"""
feat_cfgstr = ibs.cfg.feat_cfg.get_cfgstr()
# returns something like: _daids((6)qbm6uaegu7gv!ut!)_FEAT(params)
daid_cfgstr = utool.hashstr_arr(aid_list, 'daids') # todo change to uuids
new_cfgstr = '_' + daid_cfgstr + feat_cfgstr
return new_cfgstr
def get_cfgstr_list(vocabtrain_cfg, **kwargs):
if vocabtrain_cfg.override_vocab == 'default':
if isinstance(vocabtrain_cfg.vocab_taids, six.string_types):
taids_cfgstr = 'taids=%s' % vocabtrain_cfg.vocab_taids
else:
taids_cfgstr = ut.hashstr_arr(vocabtrain_cfg.vocab_taids,
'taids', hashlen=8)
vocabtrain_cfg_list = [
'_VocabTrain(',
'nWords=%d' % (vocabtrain_cfg.nWords,),
',init=', str(vocabtrain_cfg.vocab_init_method),
',nIters=%d,' % int(vocabtrain_cfg.vocab_nIters),
taids_cfgstr,
')',
]
else:
vocabtrain_cfg_list = ['_VocabTrain(override=%s)' %
(vocabtrain_cfg.override_vocab,)]
return vocabtrain_cfg_list
def make_incremental_test_database(ibs_gt, aid_list1, reset):
"""
Makes test database. adds image and annotations but does not transfer names.
if reset is true the new database is gaurenteed to be built from a fresh
start.
Args:
ibs_gt (IBEISController):
aid_list1 (list):
reset (bool): if True the test database is completely rebuilt
Returns:
IBEISController: ibs2
"""
import ibeis
print('make_incremental_test_database. reset=%r' % (reset, ))
aids1_hashid = ut.hashstr_arr(aid_list1)
prefix = '_INCTEST_' + aids1_hashid + '_'
dbname2 = prefix + ibs_gt.get_dbname()
ibs2 = ibeis.opendb(dbname2,
allow_newdir=True,
delete_ibsdir=reset,
use_cache=False)
# reset if flag specified or no data in ibs2
if reset or len(ibs2.get_valid_gids()) == 0:
assert len(ibs2.get_valid_aids()) == 0
assert len(ibs2.get_valid_gids()) == 0
assert len(ibs2.get_valid_nids()) == 0
# Get annotations and their images from database 1
gid_list1 = ibs_gt.get_annot_gids(aid_list1)
gpath_list1 = ibs_gt.get_image_paths(gid_list1)
# Add all images from database 1 to database 2
gid_list2 = ibs2.add_images(gpath_list1, auto_localize=False)
# Image UUIDS should be consistent between databases
image_uuid_list1 = ibs_gt.get_image_uuids(gid_list1)
image_uuid_list2 = ibs2.get_image_uuids(gid_list2)
assert image_uuid_list1 == image_uuid_list2
ut.assert_lists_eq(image_uuid_list1, image_uuid_list2)
return ibs2
def load_qcx2_res(ibs, qrid_list, nocache=False):
'Prefrosm / loads all queries'
qreq = mc3.quickly_ensure_qreq(ibs, qrids=qrid_list)
# Build query big cache rowid
query_rowid = qreq.get_rowid()
hs_rowid = ibs.get_db_name()
qcxs_rowid = utool.hashstr_arr(qrid_list, lbl='_qcxs')
qres_rowid = hs_rowid + query_rowid + qcxs_rowid
cache_dir = join(ibs.dirs.cache_dir, 'query_results_bigcache')
print('[rr2] load_qcx2_res(): %r' % qres_rowid)
io_kwargs = dict(dpath=cache_dir,
fname='query_results',
rowid=qres_rowid,
ext='.cPkl')
# Return cache if available
if not params.args.nocache_query and (not nocache):
qrid2_qres = io.smart_load(**io_kwargs)
if qrid2_qres is not None:
print('[rr2] * cache hit')
return qrid2_qres
print('[rr2] * cache miss')
else:
print('[rr2] * cache off')
# Individually load / compute queries
if isinstance(qrid_list, list):
qcx_set = set(qrid_list)
else:
qcx_set = set(qrid_list.tolist())
qcx_max = max(qrid_list) + 1
qrid2_qres = [
ibs.query(qrid) if qrid in qcx_set else None
for qrid in xrange(qcx_max)
]
# Save to the cache
print('[rr2] Saving query_results to bigcache: %r' % qres_rowid)
utool.ensuredir(cache_dir)
io.smart_save(qrid2_qres, **io_kwargs)
return qrid2_qres
def make_incremental_test_database(ibs_gt, aid_list1, reset):
"""
Makes test database. adds image and annotations but does not transfer names.
if reset is true the new database is gaurenteed to be built from a fresh
start.
Args:
ibs_gt (IBEISController):
aid_list1 (list):
reset (bool): if True the test database is completely rebuilt
Returns:
IBEISController: ibs2
"""
import ibeis
print('make_incremental_test_database. reset=%r' % (reset,))
aids1_hashid = ut.hashstr_arr(aid_list1)
prefix = '_INCTEST_' + aids1_hashid + '_'
dbname2 = prefix + ibs_gt.get_dbname()
ibs2 = ibeis.opendb(dbname2, allow_newdir=True, delete_ibsdir=reset, use_cache=False)
# reset if flag specified or no data in ibs2
if reset or len(ibs2.get_valid_gids()) == 0:
assert len(ibs2.get_valid_aids()) == 0
assert len(ibs2.get_valid_gids()) == 0
assert len(ibs2.get_valid_nids()) == 0
# Get annotations and their images from database 1
gid_list1 = ibs_gt.get_annot_gids(aid_list1)
gpath_list1 = ibs_gt.get_image_paths(gid_list1)
# Add all images from database 1 to database 2
gid_list2 = ibs2.add_images(gpath_list1, auto_localize=False)
# Image UUIDS should be consistent between databases
image_uuid_list1 = ibs_gt.get_image_uuids(gid_list1)
image_uuid_list2 = ibs2.get_image_uuids(gid_list2)
assert image_uuid_list1 == image_uuid_list2
ut.assert_lists_eq(image_uuid_list1, image_uuid_list2)
return ibs2
def get_qaids_hashid(qreq):
assert len(qreq.qaids) > 0, 'QueryRequest not populated. len(qaids)=0'
qaids_hashid = utool.hashstr_arr(qreq.qaids, '_qaids')
return qaids_hashid
def get_qaids_hashid(qreq):
assert len(qreq.qaids) > 0, 'QueryRequest not populated. len(qaids)=0'
qaids_hashid = utool.hashstr_arr(qreq.qaids, '_qaids')
return qaids_hashid
def get_flann_cfgstr(ibs, aid_list):
""" </CYTHE> """
feat_cfgstr = ibs.cfg.feat_cfg.get_cfgstr()
sample_cfgstr = utool.hashstr_arr(aid_list, 'daids')
cfgstr = '_' + sample_cfgstr + feat_cfgstr
return cfgstr
