def index_data_annots(annots_df,
daids,
words,
with_internals=True,
aggregate=False,
alpha=3,
thresh=0,
with_pandas=WITH_PANDAS):
"""
Builds the initial inverted index from a dataframe, daids, and words.
Optionally builds the internals of the inverted structure
>>> from ibeis.model.hots.smk.smk_index import * # NOQA
>>> from ibeis.model.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, words = smk_debug.testdata_words()
>>> with_internals = False
>>> invindex = index_data_annots(annots_df, daids, words, with_internals)
#>>> print(utool.hashstr(repr(list(invindex.__dict__.values()))))
#v8+i5i8+55j0swio
"""
if utool.VERBOSE:
print('[smk_index] index_data_annots')
flann_params = {}
_words = pdh.ensure_values(words)
wordflann = nntool.flann_cache(_words,
flann_params=flann_params,
appname='smk')
_daids = pdh.ensure_values(daids)
_vecs_list = pdh.ensure_2d_values(annots_df['vecs'][_daids])
_idx2_dvec, _idx2_daid, _idx2_dfx = nntool.invertable_stack(
_vecs_list, _daids)
# Pandasify
if with_pandas:
idx_series = pdh.IntIndex(np.arange(len(_idx2_daid)), name='idx')
idx2_dfx = pdh.IntSeries(_idx2_dfx, index=idx_series, name='fx')
idx2_daid = pdh.IntSeries(_idx2_daid, index=idx_series, name='aid')
idx2_dvec = pd.DataFrame(_idx2_dvec,
index=idx_series,
columns=VEC_COLUMNS)
else:
idx2_dfx = _idx2_dfx
idx2_daid = _idx2_daid
idx2_dvec = _idx2_dvec
pass
invindex = InvertedIndex(words, wordflann, idx2_dvec, idx2_daid, idx2_dfx,
daids)
if with_internals:
compute_data_internals_(invindex, aggregate, alpha, thresh) # 99%
return invindex
def index_data_annots(annots_df, daids, words, with_internals=True):
"""
Create inverted index for database annotations
>>> from ibeis.model.hots.smk.smk import * # NOQA
>>> ibs, annots_df, taids, daids, qaids, nWords = testdata()
>>> words = learn_visual_words(annots_df, taids, nWords)
>>> with_internals = True
>>> invindex = index_data_annots(annots_df, daids, words, with_internals)
"""
vecs_list = ensure_values(annots_df['vecs'][daids])
flann_params = {}
cache_dir = utool.get_app_resource_dir('smk')
wordflann = nntool.flann_cache(words, flann_params=flann_params, cache_dir=cache_dir)
_daids = ensure_values(daids)
idx2_dvec, idx2_daid, idx2_dfx = nntool.invertable_stack(vecs_list, _daids)
invindex = InvertedIndex(words, wordflann, idx2_dvec, idx2_daid, idx2_dfx, _daids)
if with_internals:
invindex.compute_internals()
return invindex
def index_data_annots(annots_df, daids, words, with_internals=True,
aggregate=False, alpha=3, thresh=0, with_pandas=WITH_PANDAS):
"""
Builds the initial inverted index from a dataframe, daids, and words.
Optionally builds the internals of the inverted structure
>>> from ibeis.model.hots.smk.smk_index import * # NOQA
>>> from ibeis.model.hots.smk import smk_debug
>>> ibs, annots_df, daids, qaids, words = smk_debug.testdata_words()
>>> with_internals = False
>>> invindex = index_data_annots(annots_df, daids, words, with_internals)
#>>> print(utool.hashstr(repr(list(invindex.__dict__.values()))))
#v8+i5i8+55j0swio
"""
if utool.VERBOSE:
print('[smk_index] index_data_annots')
flann_params = {}
_words = pdh.ensure_values(words)
wordflann = nntool.flann_cache(_words, flann_params=flann_params,
appname='smk')
_daids = pdh.ensure_values(daids)
_vecs_list = pdh.ensure_2d_values(annots_df['vecs'][_daids])
_idx2_dvec, _idx2_daid, _idx2_dfx = nntool.invertable_stack(_vecs_list, _daids)
# Pandasify
if with_pandas:
idx_series = pdh.IntIndex(np.arange(len(_idx2_daid)), name='idx')
idx2_dfx = pdh.IntSeries(_idx2_dfx, index=idx_series, name='fx')
idx2_daid = pdh.IntSeries(_idx2_daid, index=idx_series, name='aid')
idx2_dvec = pd.DataFrame(_idx2_dvec, index=idx_series, columns=VEC_COLUMNS)
else:
idx2_dfx = _idx2_dfx
idx2_daid = _idx2_daid
idx2_dvec = _idx2_dvec
pass
invindex = InvertedIndex(words, wordflann, idx2_dvec, idx2_daid, idx2_dfx, daids)
if with_internals:
compute_data_internals_(invindex, aggregate, alpha, thresh) # 99%
return invindex
def index_data_annots(annots_df, daids, words, with_internals=True):
"""
Create inverted index for database annotations
>>> from ibeis.model.hots.smk.smk import * # NOQA
>>> ibs, annots_df, taids, daids, qaids, nWords = testdata()
>>> words = learn_visual_words(annots_df, taids, nWords)
>>> with_internals = True
>>> invindex = index_data_annots(annots_df, daids, words, with_internals)
"""
vecs_list = ensure_values(annots_df['vecs'][daids])
flann_params = {}
cache_dir = utool.get_app_resource_dir('smk')
wordflann = nntool.flann_cache(words,
flann_params=flann_params,
cache_dir=cache_dir)
_daids = ensure_values(daids)
idx2_dvec, idx2_daid, idx2_dfx = nntool.invertable_stack(vecs_list, _daids)
invindex = InvertedIndex(words, wordflann, idx2_dvec, idx2_daid, idx2_dfx,
_daids)
if with_internals:
invindex.compute_internals()
return invindex
