def get_test_daids(ibs, default_daids='all', qaid_list=None, return_annot_info=False, aidcfg=None):
""" Gets database annot_rowids based on command line arguments
DEPRICATE
CommandLine:
python dev.py --db PZ_MTEST -t best --exclude-query --qaid 72 -r 0 -c 0 --show --va --vf --dump-extra
Args:
ibs (IBEISController): ibeis controller object
default_daids (str): (default = 'all')
qaid_list (list): list of chosen qaids that may affect daids (default = None)
Returns:
list: available_daids
CommandLine:
python -m ibeis.init.main_helpers --test-get_test_daids
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --verbmhelp
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --exclude-query
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --daid-exclude 2 3 4
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --species=zebra_grevys
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_Master0 --species=zebra_grevys
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_Master0 --controlled --verbmhelp
python -m ibeis.init.main_helpers --exec-get_test_daids --controlled --db PZ_Master0 --exec-mode
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.init.main_helpers import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> default_daids = 'all'
>>> qaid_list = [1]
>>> available_daids = get_test_daids(ibs, default_daids, qaid_list)
>>> ibeis.other.dbinfo.get_dbinfo(ibs, aid_list=available_daids, with_contrib=False, short=True)
>>> result = 'available_daids = ' + ut.obj_str(available_daids, truncate=True, nl=False)
>>> print('len(available_daids) %d' % len(available_daids))
>>> print(result)
available_daids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
"""
daid_request_info = {}
if VERB_MAIN_HELPERS:
print('[get_test_daids] + --- GET_TEST_DAIDS ---')
print('[get_test_daids] * default_daids = %s' % (ut.obj_str(default_daids, truncate=True, nl=False)))
print('[get_test_daids] * qaid_list = %s' % (ut.obj_str(qaid_list, truncate=True, nl=False)))
# ---- INCLUDING STEP
if VERB_MAIN_HELPERS:
print('[get_test_daids] * include step')
available_daids = []
CONTROLLED_CASES = ut.get_argflag('--controlled') or ut.get_argflag('--controlled_daids')
DSHUFFLE = ut.get_argval('--dshuffle')
DINDEX = params.args.dindex
NO_JUNK = not ut.get_argflag('--junk')
EXCLUDE_QUERY = ut.get_argflag('--exclude-query')
#daids_exclude = params.args.daid_exclude
daids_exclude = None
if CONTROLLED_CASES:
print('[get_test_daids] * Including controlled daids')
from ibeis.other import ibsfuncs
controlled_daids = ibsfuncs.get_two_annots_per_name_and_singletons(ibs, onlygt=False)
available_daids.extend(controlled_daids)
daid_request_info['controlled'] = True
else:
daid_request_info['controlled'] = False
# ---- CHECK_DEFAULTS DATA
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
if len(available_daids) == 0:
print('[get_test_daids] * ... defaulting, no available daids on command line.')
if isinstance(default_daids, six.string_types):
if default_daids == 'all':
default_daids = ibs.get_valid_aids()
daid_request_info['default_daids'] = 'all'
elif default_daids == 'gt':
default_daids = ut.flatten(ibs.get_annot_groundtruth(qaid_list))
daid_request_info['default_daids'] = 'gt'
#available_qaids = valid_aids[0:1]
assert not isinstance(available_daids, six.string_types)
available_daids = default_daids
else:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * ... not defaulting')
available_daids = ut.unique_ordered(available_daids)
# ---- EXCLUSION STEP
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
print('[get_test_daids] * exclude step')
species = ut.get_argval('--species', type_=str, default=None)
if NO_JUNK:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Filtering junk')
available_daids = ibs.filter_junk_annotations(available_daids)
if EXCLUDE_QUERY:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Excluding query qaids')
assert qaid_list is not None, 'must specify qaids to exclude'
available_daids = ut.setdiff_ordered(available_daids, qaid_list)
if daids_exclude is not None:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Excluding specified daids')
available_daids = ut.setdiff_ordered(available_daids, daids_exclude)
if species is not None:
if species == 'primary':
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Finiding primary species')
#species = ibs.get_primary_database_species(available_daids)
species = ibs.get_primary_database_species()
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Filtering to species=%r' % (species,))
import numpy as np
isvalid_list = np.array(ibs.get_annot_species(available_daids)) == species
available_daids = ut.compress(available_daids, isvalid_list)
# ---- SUBINDEXING STEP
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
print('[get_test_daids] * subindex step')
#ut.get_argval('--qshuffle')
if DSHUFFLE:
# Determenistic shuffling
available_daids = ut.take(available_daids, ut.random_indexes(len(available_daids), seed=43))
daid_request_info['shuffled'] = True
if DINDEX is not None:
dindexes = ensure_flatlistlike(DINDEX)
_test_daids = [available_daids[dx] for dx in dindexes if dx < len(available_daids)]
print('[get_test_daids] Chose subset of size %d/%d' % (len(_test_daids), len(available_daids)))
available_daids = _test_daids
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
print('[get_test_daids] L ___ GET_TEST_DAIDS ___')
if return_annot_info:
return available_daids, daid_request_info
else:
return available_daids
def test_siamese_performance(model, data, labels, flat_metadata, dataname=''):
r"""
CommandLine:
utprof.py -m ibeis_cnn --tf pz_patchmatch --db liberty --test --weights=liberty:current --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure --weights=new
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --train --weights=new
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 --test # NOQA
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128
"""
import vtool as vt
import plottool as pt
# TODO: save in model.trainind_dpath/diagnostics/figures
ut.colorprint('\n[siam_perf] Testing Siamese Performance', 'white')
#epoch_dpath = model.get_epoch_diagnostic_dpath()
epoch_dpath = model.arch_dpath
ut.vd(epoch_dpath)
dataname += ' ' + model.get_history_hashid() + '\n'
history_text = ut.list_str(model.era_history, newlines=True)
ut.write_to(ut.unixjoin(epoch_dpath, 'era_history.txt'), history_text)
#if True:
# import matplotlib as mpl
# mpl.rcParams['agg.path.chunksize'] = 100000
#data = data[::50]
#labels = labels[::50]
#from ibeis_cnn import utils
#data, labels = utils.random_xy_sample(data, labels, 10000, model.data_per_label_input)
FULL = not ut.get_argflag('--quick')
fnum_gen = pt.make_fnum_nextgen()
ut.colorprint('[siam_perf] Show era history', 'white')
fig = model.show_era_loss(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
# hack
ut.colorprint('[siam_perf] Show weights image', 'white')
fig = model.show_weights_image(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
#model.draw_all_conv_layer_weights(fnum=fnum_gen())
#model.imwrite_weights(1)
#model.imwrite_weights(2)
# Compute each type of score
ut.colorprint('[siam_perf] Building Scores', 'white')
test_outputs = model.predict2(model, data)
network_output = test_outputs['network_output_determ']
# hack converting network output to distances for non-descriptor networks
if len(network_output.shape) == 2 and network_output.shape[1] == 1:
cnn_scores = network_output.T[0]
elif len(network_output.shape) == 1:
cnn_scores = network_output
elif len(network_output.shape) == 2 and network_output.shape[1] > 1:
assert model.data_per_label_output == 2
vecs1 = network_output[0::2]
vecs2 = network_output[1::2]
cnn_scores = vt.L2(vecs1, vecs2)
else:
assert False
cnn_scores = cnn_scores.astype(np.float64)
# Segfaults with the data passed in is large (AND MEMMAPPED apparently)
# Fixed in hesaff implementation
SIFT = FULL
if SIFT:
sift_scores, sift_list = test_sift_patchmatch_scores(data, labels)
sift_scores = sift_scores.astype(np.float64)
ut.colorprint('[siam_perf] Learning Encoders', 'white')
# Learn encoders
encoder_kw = {
#'monotonize': False,
'monotonize': True,
}
cnn_encoder = vt.ScoreNormalizer(**encoder_kw)
cnn_encoder.fit(cnn_scores, labels)
if SIFT:
sift_encoder = vt.ScoreNormalizer(**encoder_kw)
sift_encoder.fit(sift_scores, labels)
# Visualize
ut.colorprint('[siam_perf] Visualize Encoders', 'white')
viz_kw = dict(
with_scores=False,
with_postbayes=False,
with_prebayes=False,
target_tpr=.95,
)
inter_cnn = cnn_encoder.visualize(
figtitle=dataname + ' CNN scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
if SIFT:
inter_sift = sift_encoder.visualize(
figtitle=dataname + ' SIFT scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
# Save
pt.save_figure(fig=inter_cnn.fig, dpath=epoch_dpath)
if SIFT:
pt.save_figure(fig=inter_sift.fig, dpath=epoch_dpath)
# Save out examples of hard errors
#cnn_fp_label_indicies, cnn_fn_label_indicies =
#cnn_encoder.get_error_indicies(cnn_scores, labels)
#sift_fp_label_indicies, sift_fn_label_indicies =
#sift_encoder.get_error_indicies(sift_scores, labels)
with_patch_examples = FULL
if with_patch_examples:
ut.colorprint('[siam_perf] Visualize Confusion Examples', 'white')
cnn_indicies = cnn_encoder.get_confusion_indicies(cnn_scores, labels)
if SIFT:
sift_indicies = sift_encoder.get_confusion_indicies(sift_scores, labels)
warped_patch1_list, warped_patch2_list = list(zip(*ut.ichunks(data, 2)))
samp_args = (warped_patch1_list, warped_patch2_list, labels)
_sample = functools.partial(draw_results.get_patch_sample_img, *samp_args)
cnn_fp_img = _sample({'fs': cnn_scores}, cnn_indicies.fp)[0]
cnn_fn_img = _sample({'fs': cnn_scores}, cnn_indicies.fn)[0]
cnn_tp_img = _sample({'fs': cnn_scores}, cnn_indicies.tp)[0]
cnn_tn_img = _sample({'fs': cnn_scores}, cnn_indicies.tn)[0]
if SIFT:
sift_fp_img = _sample({'fs': sift_scores}, sift_indicies.fp)[0]
sift_fn_img = _sample({'fs': sift_scores}, sift_indicies.fn)[0]
sift_tp_img = _sample({'fs': sift_scores}, sift_indicies.tp)[0]
sift_tn_img = _sample({'fs': sift_scores}, sift_indicies.tn)[0]
#if ut.show_was_requested():
#def rectify(arr):
# return np.flipud(arr)
SINGLE_FIG = False
if SINGLE_FIG:
def dump_img(img_, lbl, fnum):
fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
dump_img(cnn_fp_img, 'cnn_fp_img', fnum_gen())
dump_img(cnn_fn_img, 'cnn_fn_img', fnum_gen())
dump_img(cnn_tp_img, 'cnn_tp_img', fnum_gen())
dump_img(cnn_tn_img, 'cnn_tn_img', fnum_gen())
dump_img(sift_fp_img, 'sift_fp_img', fnum_gen())
dump_img(sift_fn_img, 'sift_fn_img', fnum_gen())
dump_img(sift_tp_img, 'sift_tp_img', fnum_gen())
dump_img(sift_tn_img, 'sift_tn_img', fnum_gen())
#vt.imwrite(dataname + '_' + 'cnn_fp_img.png', (cnn_fp_img))
#vt.imwrite(dataname + '_' + 'cnn_fn_img.png', (cnn_fn_img))
#vt.imwrite(dataname + '_' + 'sift_fp_img.png', (sift_fp_img))
#vt.imwrite(dataname + '_' + 'sift_fn_img.png', (sift_fn_img))
else:
print('Drawing TP FP TN FN')
fnum = fnum_gen()
pnum_gen = pt.make_pnum_nextgen(4, 2)
fig = pt.figure(fnum)
pt.imshow(cnn_fp_img, title='CNN FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fp_img, title='SIFT FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_fn_img, title='CNN FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fn_img, title='SIFT FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tp_img, title='CNN TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tp_img, title='SIFT TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tn_img, title='CNN TN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tn_img, title='SIFT TN', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle(dataname + ' confusions')
pt.adjust_subplots(left=0, right=1.0, bottom=0., wspace=.01, hspace=.05)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
with_patch_desc = FULL
if with_patch_desc:
ut.colorprint('[siam_perf] Visualize Patch Descriptors', 'white')
fnum = fnum_gen()
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1))
num_rows = 7
pnum_gen = pt.make_pnum_nextgen(num_rows, 3)
# Compare actual output descriptors
for index in ut.random_indexes(len(sift_list), num_rows):
vec_sift = sift_list[index]
vec_cnn = network_output[index]
patch = data[index]
pt.imshow(patch, fnum=fnum, pnum=pnum_gen())
pt.plot_descriptor_signature(vec_cnn, 'cnn vec', fnum=fnum, pnum=pnum_gen())
pt.plot_sift_signature(vec_sift, 'sift vec', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle('Patch Descriptors')
pt.adjust_subplots(left=0, right=0.95, bottom=0., wspace=.1, hspace=.15)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
def get_test_qaids(ibs, default_qaids=None, return_annot_info=False, aidcfg=None):
"""
Gets test annot_rowids based on command line arguments
DEPRICATE
Args:
ibs (IBEISController): ibeis controller object
default_qaids (None): if list then used only if no other aids are available (default = [1])
as a string it mimics the command line
Returns:
list: available_qaids
CommandLine:
python -m ibeis.init.main_helpers --test-get_test_qaids
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_Master0
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_Master0 --qaid 1
python -m ibeis.init.main_helpers --test-get_test_qaids --allgt --db PZ_MTEST
python -m ibeis.init.main_helpers --test-get_test_qaids --qaid 4 5 8 --verbmhelp
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_MTEST
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_MTEST --qaid 2 --verbmhelp
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_MTEST --qaid 2
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_Master0 --qindex 0:10 --verbmhelp
python -m ibeis.init.main_helpers --exec-get_test_qaids --controlled --db PZ_Master0 --exec-mode
python -m ibeis.init.main_helpers --exec-get_test_qaids --db testdb1 --allgt --qindex 0:256
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.init.main_helpers import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> default_qaids = None
>>> available_qaids = get_test_qaids(ibs, default_qaids)
>>> ibeis.other.dbinfo.get_dbinfo(ibs, aid_list=available_qaids, with_contrib=False, short=True)
>>> result = 'available_qaids = ' + ut.obj_str(available_qaids, truncate=True, nl=False)
>>> print('len(available_qaids) = %d' % len(available_qaids))
>>> print(result)
available_qaids = [1]
"""
qaid_request_info = {}
if VERB_MAIN_HELPERS:
print('[get_test_qaids] + --- GET_TEST_QAIDS ---')
# Old version of this function
if VERB_MAIN_HELPERS:
print('[get_test_qaids] + --- GET_TEST_QAIDS ---')
print('[get_test_qaids] * default_qaids = %s' % (ut.obj_str(default_qaids, truncate=True, nl=False)))
valid_aids = ibs.get_valid_aids()
if len(valid_aids) == 0:
print('[get_test_qaids] WARNING no annotations available')
# ---- INCLUDING STEP
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * include step')
available_qaids = []
#ut.get_argflag(('--all-cases', '--all'))
#ut.get_argflag(('--all-gt-cases', '--allgt'))
#ut.get_argflag(('--all-hard-cases', '--allhard'))
#ut.get_argflag(('--qaid', '--qaids'))
#ut.get_argflag('--controlled') or ut.get_argflag('--controlled_qaids')
#not ut.get_argflag('--junk')
ALL_CASES = params.args.all_cases or default_qaids == 'all'
GT_CASES = params.args.all_gt_cases or default_qaids == 'gt'
HARD_CASES = params.args.all_hard_cases or ut.get_argflag(('--all-hard-cases', '--allhard', '--hard'))
NO_JUNK = not ut.get_argflag('--junk')
CONTROLLED_CASES = ut.get_argflag('--controlled') or ut.get_argflag('--controlled_qaids')
NO_REVIEWED = ut.get_argflag('--unreviewed')
species = ut.get_argval('--species')
#QAID = params.args.qaid
QAID = ut.get_argval('--qaid', type_='fuzzy_subset', default=None)
QINDEX = params.args.qindex
QSHUFFLE = ut.get_argval('--qshuffle')
if QAID is not None:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including cmdline specified qaids')
try:
args_qaid = ensure_flatlistlike(QAID)
except Exception:
args_qaid = QAID
available_qaids.extend(args_qaid)
qaid_request_info['custom_commandline'] = args_qaid
if ALL_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including all qaids')
available_qaids.extend(valid_aids)
qaid_request_info['all_cases'] = True
if HARD_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including hard qaids')
is_hard_list = ibs.get_annot_is_hard(valid_aids)
hard_aids = ut.compress(valid_aids, is_hard_list)
available_qaids.extend(hard_aids)
qaid_request_info['hard_cases'] = True
if GT_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including groundtruth qaids')
has_gt_list = ibs.get_annot_has_groundtruth(valid_aids)
hasgt_aids = ut.compress(valid_aids, has_gt_list)
print('[get_test_qaids] Adding all %d/%d ground-truthed test cases' % (len(hasgt_aids), len(valid_aids)))
available_qaids.extend(hasgt_aids)
qaid_request_info['gt_cases'] = True
if CONTROLLED_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including controlled qaids')
from ibeis.other import ibsfuncs
# Override all other gts with controlled
controlled_qaids = ibsfuncs.get_two_annots_per_name_and_singletons(ibs, onlygt=True)
available_qaids.extend(controlled_qaids)
qaid_request_info['controlled'] = True
else:
qaid_request_info['controlled'] = False
# ---- CHECK_DEFAULTS QUERY
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
if len(available_qaids) == 0:
print('[get_test_qaids] * ... defaulting, no available qaids on command line.')
if default_qaids is None:
default_qaids = valid_aids[0:1]
qaid_request_info['default_one'] = True
elif isinstance(default_qaids, six.string_types):
if default_qaids == 'gt' or default_qaids == 'allgt':
default_qaids = ibs.get_valid_aids(hasgt=True)
qaid_request_info['default_gt'] = True
available_qaids = default_qaids
else:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * ... not defaulting')
available_qaids = ut.unique_ordered(available_qaids)
# ---- EXCLUSION STEP
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
print('[get_test_qaids] * exclude step')
if NO_JUNK:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Filtering junk')
available_qaids = ibs.filter_junk_annotations(available_qaids)
qaid_request_info['has_junk'] = False
if NO_REVIEWED:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Filtering unreviewed')
isreviewed_list = ibs.get_annot_has_reviewed_matching_aids(available_qaids)
available_qaids = ut.filterfalse_items(available_qaids, isreviewed_list)
qaid_request_info['has_unreviewed'] = False
if species is not None:
if species == 'primary':
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Finiding primary species')
#species = ibs.get_primary_database_species(available_qaids)
species = ibs.get_primary_database_species()
qaid_request_info['primary_species'] = True
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Filtering to species=%r' % (species,))
isvalid_list = np.array(ibs.get_annot_species(available_qaids)) == species
available_qaids = ut.compress(available_qaids, isvalid_list)
qaid_request_info['species_filter'] = species
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
print('[get_test_qaids] * subindex step')
# ---- INDEX SUBSET
#ut.get_argval('--qshuffle')
if QSHUFFLE:
# Determenistic shuffling
available_qaids = ut.take(available_qaids, ut.random_indexes(len(available_qaids), seed=42))
qaid_request_info['shuffled'] = True
# Sample a large pool of chosen query qindexes
if QINDEX is not None:
# FIXME: should use a slice of the list or a sublist
qindexes = ensure_flatlistlike(QINDEX)
_test_qaids = [available_qaids[qx] for qx in qindexes if qx < len(available_qaids)]
print('[get_test_qaids] Chose subset of size %d/%d' % (len(_test_qaids), len(available_qaids)))
available_qaids = _test_qaids
qaid_request_info['subset'] = qindexes
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
print('[get_test_qaids] L ___ GET_TEST_QAIDS ___')
if return_annot_info:
return available_qaids, qaid_request_info
else:
return available_qaids
def split_analysis(ibs):
"""
CommandLine:
python -m ibeis.other.dbinfo split_analysis --show
python -m ibeis split_analysis --show
python -m ibeis split_analysis --show --good
Ignore:
# mount
sshfs -o idmap=user lev:/ ~/lev
# unmount
fusermount -u ~/lev
Example:
>>> # DISABLE_DOCTEST GGR
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> dbdir = '/media/danger/GGR/GGR-IBEIS'
>>> dbdir = dbdir if ut.checkpath(dbdir) else ut.truepath('~/lev/media/danger/GGR/GGR-IBEIS')
>>> ibs = ibeis.opendb(dbdir=dbdir, allow_newdir=False)
>>> import guitool_ibeis as gt
>>> gt.ensure_qtapp()
>>> win = split_analysis(ibs)
>>> ut.quit_if_noshow()
>>> import plottool_ibeis as pt
>>> gt.qtapp_loop(qwin=win)
>>> #ut.show_if_requested()
"""
#nid_list = ibs.get_valid_nids(filter_empty=True)
import datetime
day1 = datetime.date(2016, 1, 30)
day2 = datetime.date(2016, 1, 31)
filter_kw = {
'multiple': None,
#'view': ['right'],
#'minqual': 'good',
'is_known': True,
'min_pername': 1,
}
aids1 = ibs.filter_annots_general(filter_kw=ut.dict_union(
filter_kw, {
'min_unixtime': ut.datetime_to_posixtime(ut.date_to_datetime(day1, 0.0)),
'max_unixtime': ut.datetime_to_posixtime(ut.date_to_datetime(day1, 1.0)),
})
)
aids2 = ibs.filter_annots_general(filter_kw=ut.dict_union(
filter_kw, {
'min_unixtime': ut.datetime_to_posixtime(ut.date_to_datetime(day2, 0.0)),
'max_unixtime': ut.datetime_to_posixtime(ut.date_to_datetime(day2, 1.0)),
})
)
all_aids = aids1 + aids2
all_annots = ibs.annots(all_aids)
print('%d annots on day 1' % (len(aids1)) )
print('%d annots on day 2' % (len(aids2)) )
print('%d annots overall' % (len(all_annots)) )
print('%d names overall' % (len(ut.unique(all_annots.nids))) )
nid_list, annots_list = all_annots.group(all_annots.nids)
REVIEWED_EDGES = True
if REVIEWED_EDGES:
aids_list = [annots.aids for annots in annots_list]
#aid_pairs = [annots.get_am_aidpairs() for annots in annots_list] # Slower
aid_pairs = ibs.get_unflat_am_aidpairs(aids_list) # Faster
else:
# ALL EDGES
aid_pairs = [annots.get_aidpairs() for annots in annots_list]
speeds_list = ibs.unflat_map(ibs.get_annotpair_speeds, aid_pairs)
import vtool_ibeis as vt
max_speeds = np.array([vt.safe_max(s, nans=False) for s in speeds_list])
nan_idx = np.where(np.isnan(max_speeds))[0]
inf_idx = np.where(np.isinf(max_speeds))[0]
bad_idx = sorted(ut.unique(ut.flatten([inf_idx, nan_idx])))
ok_idx = ut.index_complement(bad_idx, len(max_speeds))
print('#nan_idx = %r' % (len(nan_idx),))
print('#inf_idx = %r' % (len(inf_idx),))
print('#ok_idx = %r' % (len(ok_idx),))
ok_speeds = max_speeds[ok_idx]
ok_nids = ut.take(nid_list, ok_idx)
ok_annots = ut.take(annots_list, ok_idx)
sortx = np.argsort(ok_speeds)[::-1]
sorted_speeds = np.array(ut.take(ok_speeds, sortx))
sorted_annots = np.array(ut.take(ok_annots, sortx))
sorted_nids = np.array(ut.take(ok_nids, sortx)) # NOQA
sorted_speeds = np.clip(sorted_speeds, 0, 100)
#idx = vt.find_elbow_point(sorted_speeds)
#EXCESSIVE_SPEED = sorted_speeds[idx]
# http://www.infoplease.com/ipa/A0004737.html
# http://www.speedofanimals.com/animals/zebra
#ZEBRA_SPEED_MAX = 64 # km/h
#ZEBRA_SPEED_RUN = 50 # km/h
ZEBRA_SPEED_SLOW_RUN = 20 # km/h
#ZEBRA_SPEED_FAST_WALK = 10 # km/h
#ZEBRA_SPEED_WALK = 7 # km/h
MAX_SPEED = ZEBRA_SPEED_SLOW_RUN
#MAX_SPEED = ZEBRA_SPEED_WALK
#MAX_SPEED = EXCESSIVE_SPEED
flags = sorted_speeds > MAX_SPEED
flagged_ok_annots = ut.compress(sorted_annots, flags)
inf_annots = ut.take(annots_list, inf_idx)
flagged_annots = inf_annots + flagged_ok_annots
print('MAX_SPEED = %r km/h' % (MAX_SPEED,))
print('%d annots with infinite speed' % (len(inf_annots),))
print('%d annots with large speed' % (len(flagged_ok_annots),))
print('Marking all pairs of annots above the threshold as non-matching')
from ibeis.algo.graph import graph_iden
import networkx as nx
progkw = dict(freq=1, bs=True, est_window=len(flagged_annots))
bad_edges_list = []
good_edges_list = []
for annots in ut.ProgIter(flagged_annots, lbl='flag speeding names', **progkw):
edge_to_speeds = annots.get_speeds()
bad_edges = [edge for edge, speed in edge_to_speeds.items() if speed > MAX_SPEED]
good_edges = [edge for edge, speed in edge_to_speeds.items() if speed <= MAX_SPEED]
bad_edges_list.append(bad_edges)
good_edges_list.append(good_edges)
all_bad_edges = ut.flatten(bad_edges_list)
good_edges_list = ut.flatten(good_edges_list)
print('num_bad_edges = %r' % (len(ut.flatten(bad_edges_list)),))
print('num_bad_edges = %r' % (len(ut.flatten(good_edges_list)),))
if 1:
from ibeis.viz import viz_graph2
import guitool_ibeis as gt
gt.ensure_qtapp()
if ut.get_argflag('--good'):
print('Looking at GOOD (no speed problems) edges')
aid_pairs = good_edges_list
else:
print('Looking at BAD (speed problems) edges')
aid_pairs = all_bad_edges
aids = sorted(list(set(ut.flatten(aid_pairs))))
infr = graph_iden.AnnotInference(ibs, aids, verbose=False)
infr.initialize_graph()
# Use random scores to randomize sort order
rng = np.random.RandomState(0)
scores = (-rng.rand(len(aid_pairs)) * 10).tolist()
infr.graph.add_edges_from(aid_pairs)
if True:
edge_sample_size = 250
pop_nids = ut.unique(ibs.get_annot_nids(ut.unique(ut.flatten(aid_pairs))))
sorted_pairs = ut.sortedby(aid_pairs, scores)[::-1][0:edge_sample_size]
sorted_nids = ibs.get_annot_nids(ut.take_column(sorted_pairs, 0))
sample_size = len(ut.unique(sorted_nids))
am_rowids = ibs.get_annotmatch_rowid_from_undirected_superkey(*zip(*sorted_pairs))
flags = ut.not_list(ut.flag_None_items(am_rowids))
#am_rowids = ut.compress(am_rowids, flags)
positive_tags = ['SplitCase', 'Photobomb']
flags_list = [ut.replace_nones(ibs.get_annotmatch_prop(tag, am_rowids), 0)
for tag in positive_tags]
print('edge_case_hist: ' + ut.repr3(
['%s %s' % (txt, sum(flags_)) for flags_, txt in zip(flags_list, positive_tags)]))
is_positive = ut.or_lists(*flags_list)
num_positive = sum(ut.lmap(any, ut.group_items(is_positive, sorted_nids).values()))
pop = len(pop_nids)
print('A positive is any edge flagged as a %s' % (ut.conj_phrase(positive_tags, 'or'),))
print('--- Sampling wrt edges ---')
print('edge_sample_size = %r' % (edge_sample_size,))
print('edge_population_size = %r' % (len(aid_pairs),))
print('num_positive_edges = %r' % (sum(is_positive)))
print('--- Sampling wrt names ---')
print('name_population_size = %r' % (pop,))
vt.calc_error_bars_from_sample(sample_size, num_positive, pop, conf_level=.95)
nx.set_edge_attributes(infr.graph, name='score', values=dict(zip(aid_pairs, scores)))
win = viz_graph2.AnnotGraphWidget(infr=infr, use_image=False,
init_mode=None)
win.populate_edge_model()
win.show()
return win
# Make review interface for only bad edges
infr_list = []
iter_ = list(zip(flagged_annots, bad_edges_list))
for annots, bad_edges in ut.ProgIter(iter_, lbl='creating inference', **progkw):
aids = annots.aids
nids = [1] * len(aids)
infr = graph_iden.AnnotInference(ibs, aids, nids, verbose=False)
infr.initialize_graph()
infr.reset_feedback()
infr_list.append(infr)
# Check which ones are user defined as incorrect
#num_positive = 0
#for infr in infr_list:
# flag = np.any(infr.get_feedback_probs()[0] == 0)
# num_positive += flag
#print('num_positive = %r' % (num_positive,))
#pop = len(infr_list)
#print('pop = %r' % (pop,))
iter_ = list(zip(infr_list, bad_edges_list))
for infr, bad_edges in ut.ProgIter(iter_, lbl='adding speed edges', **progkw):
flipped_edges = []
for aid1, aid2 in bad_edges:
if infr.graph.has_edge(aid1, aid2):
flipped_edges.append((aid1, aid2))
infr.add_feedback((aid1, aid2), NEGTV)
nx.set_edge_attributes(infr.graph, name='_speed_split', values='orig')
nx.set_edge_attributes(infr.graph, name='_speed_split', values={edge: 'new' for edge in bad_edges})
nx.set_edge_attributes(infr.graph, name='_speed_split', values={edge: 'flip' for edge in flipped_edges})
#for infr in ut.ProgIter(infr_list, lbl='flagging speeding edges', **progkw):
# annots = ibs.annots(infr.aids)
# edge_to_speeds = annots.get_speeds()
# bad_edges = [edge for edge, speed in edge_to_speeds.items() if speed > MAX_SPEED]
def inference_stats(infr_list_):
relabel_stats = []
for infr in infr_list_:
num_ccs, num_inconsistent = infr.relabel_using_reviews()
state_hist = ut.dict_hist(nx.get_edge_attributes(infr.graph, 'decision').values())
if POSTV not in state_hist:
state_hist[POSTV] = 0
hist = ut.dict_hist(nx.get_edge_attributes(infr.graph, '_speed_split').values())
subgraphs = infr.positive_connected_compoments()
subgraph_sizes = [len(g) for g in subgraphs]
info = ut.odict([
('num_nonmatch_edges', state_hist[NEGTV]),
('num_match_edges', state_hist[POSTV]),
('frac_nonmatch_edges', state_hist[NEGTV] / (state_hist[POSTV] + state_hist[NEGTV])),
('num_inconsistent', num_inconsistent),
('num_ccs', num_ccs),
('edges_flipped', hist.get('flip', 0)),
('edges_unchanged', hist.get('orig', 0)),
('bad_unreviewed_edges', hist.get('new', 0)),
('orig_size', len(infr.graph)),
('new_sizes', subgraph_sizes),
])
relabel_stats.append(info)
return relabel_stats
relabel_stats = inference_stats(infr_list)
print('\nAll Split Info:')
lines = []
for key in relabel_stats[0].keys():
data = ut.take_column(relabel_stats, key)
if key == 'new_sizes':
data = ut.flatten(data)
lines.append('stats(%s) = %s' % (key, ut.repr2(ut.get_stats(data, use_median=True), precision=2)))
print('\n'.join(ut.align_lines(lines, '=')))
num_incon_list = np.array(ut.take_column(relabel_stats, 'num_inconsistent'))
can_split_flags = num_incon_list == 0
print('Can trivially split %d / %d' % (sum(can_split_flags), len(can_split_flags)))
splittable_infrs = ut.compress(infr_list, can_split_flags)
relabel_stats = inference_stats(splittable_infrs)
print('\nTrival Split Info:')
lines = []
for key in relabel_stats[0].keys():
if key in ['num_inconsistent']:
continue
data = ut.take_column(relabel_stats, key)
if key == 'new_sizes':
data = ut.flatten(data)
lines.append('stats(%s) = %s' % (
key, ut.repr2(ut.get_stats(data, use_median=True), precision=2)))
print('\n'.join(ut.align_lines(lines, '=')))
num_match_edges = np.array(ut.take_column(relabel_stats, 'num_match_edges'))
num_nonmatch_edges = np.array(ut.take_column(relabel_stats, 'num_nonmatch_edges'))
flags1 = np.logical_and(num_match_edges > num_nonmatch_edges, num_nonmatch_edges < 3)
reasonable_infr = ut.compress(splittable_infrs, flags1)
new_sizes_list = ut.take_column(relabel_stats, 'new_sizes')
flags2 = [len(sizes) == 2 and sum(sizes) > 4 and (min(sizes) / max(sizes)) > .3
for sizes in new_sizes_list]
reasonable_infr = ut.compress(splittable_infrs, flags2)
print('#reasonable_infr = %r' % (len(reasonable_infr),))
for infr in ut.InteractiveIter(reasonable_infr):
annots = ibs.annots(infr.aids)
edge_to_speeds = annots.get_speeds()
print('max_speed = %r' % (max(edge_to_speeds.values())),)
infr.initialize_visual_node_attrs()
infr.show_graph(use_image=True, only_reviewed=True)
rest = ~np.logical_or(flags1, flags2)
nonreasonable_infr = ut.compress(splittable_infrs, rest)
rng = np.random.RandomState(0)
random_idx = ut.random_indexes(len(nonreasonable_infr) - 1, 15, rng=rng)
random_infr = ut.take(nonreasonable_infr, random_idx)
for infr in ut.InteractiveIter(random_infr):
annots = ibs.annots(infr.aids)
edge_to_speeds = annots.get_speeds()
print('max_speed = %r' % (max(edge_to_speeds.values())),)
infr.initialize_visual_node_attrs()
infr.show_graph(use_image=True, only_reviewed=True)
#import scipy.stats as st
#conf_interval = .95
#st.norm.cdf(conf_interval)
# view-source:http://www.surveysystem.com/sscalc.htm
#zval = 1.96 # 95 percent confidence
#zValC = 3.8416 #
#zValC = 6.6564
#import statsmodels.stats.api as sms
#es = sms.proportion_effectsize(0.5, 0.75)
#sms.NormalIndPower().solve_power(es, power=0.9, alpha=0.05, ratio=1)
pop = 279
num_positive = 3
sample_size = 15
conf_level = .95
#conf_level = .99
vt.calc_error_bars_from_sample(sample_size, num_positive, pop, conf_level)
print('---')
vt.calc_error_bars_from_sample(sample_size + 38, num_positive, pop, conf_level)
print('---')
vt.calc_error_bars_from_sample(sample_size + 38 / 3, num_positive, pop, conf_level)
print('---')
vt.calc_error_bars_from_sample(15 + 38, num_positive=3, pop=675, conf_level=.95)
vt.calc_error_bars_from_sample(15, num_positive=3, pop=675, conf_level=.95)
pop = 279
#err_frac = .05 # 5%
err_frac = .10 # 10%
conf_level = .95
vt.calc_sample_from_error_bars(err_frac, pop, conf_level)
pop = 675
vt.calc_sample_from_error_bars(err_frac, pop, conf_level)
vt.calc_sample_from_error_bars(.05, pop, conf_level=.95, prior=.1)
vt.calc_sample_from_error_bars(.05, pop, conf_level=.68, prior=.2)
vt.calc_sample_from_error_bars(.10, pop, conf_level=.68)
vt.calc_error_bars_from_sample(100, num_positive=5, pop=675, conf_level=.95)
vt.calc_error_bars_from_sample(100, num_positive=5, pop=675, conf_level=.68)
def get_test_daids(ibs, default_daids='all', qaid_list=None, return_annot_info=False, aidcfg=None):
""" Gets database annot_rowids based on command line arguments
DEPRICATE
CommandLine:
python dev.py --db PZ_MTEST -t best --exclude-query --qaid 72 -r 0 -c 0 --show --va --vf --dump-extra
Args:
ibs (IBEISController): ibeis controller object
default_daids (str): (default = 'all')
qaid_list (list): list of chosen qaids that may affect daids (default = None)
Returns:
list: available_daids
CommandLine:
python -m ibeis.init.main_helpers --test-get_test_daids
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --verbmhelp
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --exclude-query
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --daid-exclude 2 3 4
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_MTEST --species=zebra_grevys
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_Master0 --species=zebra_grevys
python -m ibeis.init.main_helpers --test-get_test_daids --db PZ_Master0 --controlled --verbmhelp
python -m ibeis.init.main_helpers --exec-get_test_daids --controlled --db PZ_Master0 --exec-mode
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.init.main_helpers import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> default_daids = 'all'
>>> qaid_list = [1]
>>> available_daids = get_test_daids(ibs, default_daids, qaid_list)
>>> ibeis.other.dbinfo.get_dbinfo(ibs, aid_list=available_daids, with_contrib=False, short=True)
>>> result = 'available_daids = ' + ut.obj_str(available_daids, truncate=True, nl=False)
>>> print('len(available_daids) %d' % len(available_daids))
>>> print(result)
available_daids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
"""
daid_request_info = {}
if VERB_MAIN_HELPERS:
print('[get_test_daids] + --- GET_TEST_DAIDS ---')
print('[get_test_daids] * default_daids = %s' % (ut.obj_str(default_daids, truncate=True, nl=False)))
print('[get_test_daids] * qaid_list = %s' % (ut.obj_str(qaid_list, truncate=True, nl=False)))
# ---- INCLUDING STEP
if VERB_MAIN_HELPERS:
print('[get_test_daids] * include step')
available_daids = []
CONTROLLED_CASES = ut.get_argflag('--controlled') or ut.get_argflag('--controlled_daids')
DSHUFFLE = ut.get_argval('--dshuffle')
DINDEX = params.args.dindex
NO_JUNK = not ut.get_argflag('--junk')
EXCLUDE_QUERY = ut.get_argflag('--exclude-query')
#daids_exclude = params.args.daid_exclude
daids_exclude = None
if CONTROLLED_CASES:
print('[get_test_daids] * Including controlled daids')
from ibeis import ibsfuncs
controlled_daids = ibsfuncs.get_two_annots_per_name_and_singletons(ibs, onlygt=False)
available_daids.extend(controlled_daids)
daid_request_info['controlled'] = True
else:
daid_request_info['controlled'] = False
# ---- CHECK_DEFAULTS DATA
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
if len(available_daids) == 0:
print('[get_test_daids] * ... defaulting, no available daids on command line.')
if isinstance(default_daids, six.string_types):
if default_daids == 'all':
default_daids = ibs.get_valid_aids()
daid_request_info['default_daids'] = 'all'
elif default_daids == 'gt':
default_daids = ut.flatten(ibs.get_annot_groundtruth(qaid_list))
daid_request_info['default_daids'] = 'gt'
#available_qaids = valid_aids[0:1]
assert not isinstance(available_daids, six.string_types)
available_daids = default_daids
else:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * ... not defaulting')
available_daids = ut.unique_keep_order(available_daids)
# ---- EXCLUSION STEP
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
print('[get_test_daids] * exclude step')
species = ut.get_argval('--species', type_=str, default=None)
if NO_JUNK:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Filtering junk')
available_daids = ibs.filter_junk_annotations(available_daids)
if EXCLUDE_QUERY:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Excluding query qaids')
assert qaid_list is not None, 'must specify qaids to exclude'
available_daids = ut.setdiff_ordered(available_daids, qaid_list)
if daids_exclude is not None:
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Excluding specified daids')
available_daids = ut.setdiff_ordered(available_daids, daids_exclude)
if species is not None:
if species == 'primary':
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Finiding primary species')
#species = ibs.get_primary_database_species(available_daids)
species = ibs.get_primary_database_species()
if VERB_MAIN_HELPERS:
print('[get_test_daids] * Filtering to species=%r' % (species,))
import numpy as np
isvalid_list = np.array(ibs.get_annot_species(available_daids)) == species
available_daids = ut.compress(available_daids, isvalid_list)
# ---- SUBINDEXING STEP
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
print('[get_test_daids] * subindex step')
#ut.get_argval('--qshuffle')
if DSHUFFLE:
# Determenistic shuffling
available_daids = ut.take(available_daids, ut.random_indexes(len(available_daids), seed=43))
daid_request_info['shuffled'] = True
if DINDEX is not None:
dindexes = ensure_flatlistlike(DINDEX)
_test_daids = [available_daids[dx] for dx in dindexes if dx < len(available_daids)]
print('[get_test_daids] Chose subset of size %d/%d' % (len(_test_daids), len(available_daids)))
available_daids = _test_daids
if VERB_MAIN_HELPERS:
print('[get_test_daids] * len(available_daids) = %r' % (len(available_daids)))
print('[get_test_daids] L ___ GET_TEST_DAIDS ___')
if return_annot_info:
return available_daids, daid_request_info
else:
return available_daids
def get_test_qaids(ibs, default_qaids=None, return_annot_info=False, aidcfg=None):
"""
Gets test annot_rowids based on command line arguments
DEPRICATE
Args:
ibs (IBEISController): ibeis controller object
default_qaids (None): if list then used only if no other aids are available (default = [1])
as a string it mimics the command line
Returns:
list: available_qaids
CommandLine:
python -m ibeis.init.main_helpers --test-get_test_qaids
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_Master0
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_Master0 --qaid 1
python -m ibeis.init.main_helpers --test-get_test_qaids --allgt --db PZ_MTEST
python -m ibeis.init.main_helpers --test-get_test_qaids --qaid 4 5 8 --verbmhelp
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_MTEST
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_MTEST --qaid 2 --verbmhelp
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_MTEST --qaid 2
python -m ibeis.init.main_helpers --test-get_test_qaids --controlled --db PZ_Master0 --qindex 0:10 --verbmhelp
python -m ibeis.init.main_helpers --exec-get_test_qaids --controlled --db PZ_Master0 --exec-mode
python -m ibeis.init.main_helpers --exec-get_test_qaids --db testdb1 --allgt --qindex 0:256
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.init.main_helpers import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> default_qaids = None
>>> available_qaids = get_test_qaids(ibs, default_qaids)
>>> ibeis.other.dbinfo.get_dbinfo(ibs, aid_list=available_qaids, with_contrib=False, short=True)
>>> result = 'available_qaids = ' + ut.obj_str(available_qaids, truncate=True, nl=False)
>>> print('len(available_qaids) = %d' % len(available_qaids))
>>> print(result)
available_qaids = [1]
"""
qaid_request_info = {}
if VERB_MAIN_HELPERS:
print('[get_test_qaids] + --- GET_TEST_QAIDS ---')
# Old version of this function
if VERB_MAIN_HELPERS:
print('[get_test_qaids] + --- GET_TEST_QAIDS ---')
print('[get_test_qaids] * default_qaids = %s' % (ut.obj_str(default_qaids, truncate=True, nl=False)))
valid_aids = ibs.get_valid_aids()
if len(valid_aids) == 0:
print('[get_test_qaids] WARNING no annotations available')
# ---- INCLUDING STEP
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * include step')
available_qaids = []
#ut.get_argflag(('--all-cases', '--all'))
#ut.get_argflag(('--all-gt-cases', '--allgt'))
#ut.get_argflag(('--all-hard-cases', '--allhard'))
#ut.get_argflag(('--qaid', '--qaids'))
#ut.get_argflag('--controlled') or ut.get_argflag('--controlled_qaids')
#not ut.get_argflag('--junk')
ALL_CASES = params.args.all_cases or default_qaids == 'all'
GT_CASES = params.args.all_gt_cases or default_qaids == 'gt'
HARD_CASES = params.args.all_hard_cases or ut.get_argflag(('--all-hard-cases', '--allhard', '--hard'))
NO_JUNK = not ut.get_argflag('--junk')
CONTROLLED_CASES = ut.get_argflag('--controlled') or ut.get_argflag('--controlled_qaids')
NO_REVIEWED = ut.get_argflag('--unreviewed')
species = ut.get_argval('--species')
#QAID = params.args.qaid
QAID = ut.get_argval('--qaid', type_='fuzzy_subset', default=None)
QINDEX = params.args.qindex
QSHUFFLE = ut.get_argval('--qshuffle')
if QAID is not None:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including cmdline specified qaids')
try:
args_qaid = ensure_flatlistlike(QAID)
except Exception:
args_qaid = QAID
available_qaids.extend(args_qaid)
qaid_request_info['custom_commandline'] = args_qaid
if ALL_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including all qaids')
available_qaids.extend(valid_aids)
qaid_request_info['all_cases'] = True
if HARD_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including hard qaids')
is_hard_list = ibs.get_annot_is_hard(valid_aids)
hard_aids = ut.compress(valid_aids, is_hard_list)
available_qaids.extend(hard_aids)
qaid_request_info['hard_cases'] = True
if GT_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including groundtruth qaids')
has_gt_list = ibs.get_annot_has_groundtruth(valid_aids)
hasgt_aids = ut.compress(valid_aids, has_gt_list)
print('[get_test_qaids] Adding all %d/%d ground-truthed test cases' % (len(hasgt_aids), len(valid_aids)))
available_qaids.extend(hasgt_aids)
qaid_request_info['gt_cases'] = True
if CONTROLLED_CASES:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Including controlled qaids')
from ibeis import ibsfuncs
# Override all other gts with controlled
controlled_qaids = ibsfuncs.get_two_annots_per_name_and_singletons(ibs, onlygt=True)
available_qaids.extend(controlled_qaids)
qaid_request_info['controlled'] = True
else:
qaid_request_info['controlled'] = False
# ---- CHECK_DEFAULTS QUERY
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
if len(available_qaids) == 0:
print('[get_test_qaids] * ... defaulting, no available qaids on command line.')
if default_qaids is None:
default_qaids = valid_aids[0:1]
qaid_request_info['default_one'] = True
elif isinstance(default_qaids, six.string_types):
if default_qaids == 'gt' or default_qaids == 'allgt':
default_qaids = ibs.get_valid_aids(hasgt=True)
qaid_request_info['default_gt'] = True
available_qaids = default_qaids
else:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * ... not defaulting')
available_qaids = ut.unique_keep_order(available_qaids)
# ---- EXCLUSION STEP
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
print('[get_test_qaids] * exclude step')
if NO_JUNK:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Filtering junk')
available_qaids = ibs.filter_junk_annotations(available_qaids)
qaid_request_info['has_junk'] = False
if NO_REVIEWED:
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Filtering unreviewed')
isreviewed_list = ibs.get_annot_has_reviewed_matching_aids(available_qaids)
available_qaids = ut.filterfalse_items(available_qaids, isreviewed_list)
qaid_request_info['has_unreviewed'] = False
if species is not None:
if species == 'primary':
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Finiding primary species')
#species = ibs.get_primary_database_species(available_qaids)
species = ibs.get_primary_database_species()
qaid_request_info['primary_species'] = True
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * Filtering to species=%r' % (species,))
isvalid_list = np.array(ibs.get_annot_species(available_qaids)) == species
available_qaids = ut.compress(available_qaids, isvalid_list)
qaid_request_info['species_filter'] = species
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
print('[get_test_qaids] * subindex step')
# ---- INDEX SUBSET
#ut.get_argval('--qshuffle')
if QSHUFFLE:
# Determenistic shuffling
available_qaids = ut.take(available_qaids, ut.random_indexes(len(available_qaids), seed=42))
qaid_request_info['shuffled'] = True
# Sample a large pool of chosen query qindexes
if QINDEX is not None:
# FIXME: should use a slice of the list or a sublist
qindexes = ensure_flatlistlike(QINDEX)
_test_qaids = [available_qaids[qx] for qx in qindexes if qx < len(available_qaids)]
print('[get_test_qaids] Chose subset of size %d/%d' % (len(_test_qaids), len(available_qaids)))
available_qaids = _test_qaids
qaid_request_info['subset'] = qindexes
if VERB_MAIN_HELPERS:
print('[get_test_qaids] * len(available_qaids) = %r' % (len(available_qaids)))
print('[get_test_qaids] L ___ GET_TEST_QAIDS ___')
if return_annot_info:
return available_qaids, qaid_request_info
else:
return available_qaids
def ggr_random_name_splits():
"""
CommandLine:
python -m wbia.viz.viz_graph2 ggr_random_name_splits --show
Ignore:
sshfs -o idmap=user lev:/ ~/lev
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.viz.viz_graph2 import * # NOQA
>>> ggr_random_name_splits()
"""
import wbia.guitool as gt
gt.ensure_qtapp()
# nid_list = ibs.get_valid_nids(filter_empty=True)
import wbia
dbdir = '/media/danger/GGR/GGR-IBEIS'
dbdir = (dbdir if ut.checkpath(dbdir) else
ut.truepath('~/lev/media/danger/GGR/GGR-IBEIS'))
ibs = wbia.opendb(dbdir=dbdir, allow_newdir=False)
import datetime
day1 = datetime.date(2016, 1, 30)
day2 = datetime.date(2016, 1, 31)
orig_filter_kw = {
'multiple': None,
# 'view': ['right'],
# 'minqual': 'good',
'is_known': True,
'min_pername': 2,
}
orig_aids = ibs.filter_annots_general(filter_kw=ut.dict_union(
orig_filter_kw,
{
'min_unixtime':
ut.datetime_to_posixtime(ut.date_to_datetime(day1, 0.0)),
'max_unixtime':
ut.datetime_to_posixtime(ut.date_to_datetime(day2, 1.0)),
},
))
orig_all_annots = ibs.annots(orig_aids)
orig_unique_nids, orig_grouped_annots_ = orig_all_annots.group(
orig_all_annots.nids)
# Ensure we get everything
orig_grouped_annots = [
ibs.annots(aids_) for aids_ in ibs.get_name_aids(orig_unique_nids)
]
# pip install quantumrandom
if False:
import quantumrandom
data = quantumrandom.uint16()
seed = data.sum()
print('seed = %r' % (seed, ))
# import Crypto.Random
# from Crypto import Random
# quantumrandom.get_data()
# StrongRandom = Crypto.Random.random.StrongRandom
# aes.reseed(3340258)
# chars = [str(chr(x)) for x in data.view(np.uint8)]
# aes_seed = str('').join(chars)
# aes = Crypto.Random.Fortuna.FortunaGenerator.AESGenerator()
# aes.reseed(aes_seed)
# aes.pseudo_random_data(10)
orig_rand_idxs = ut.random_indexes(len(orig_grouped_annots), seed=3340258)
orig_sample_size = 75
random_annot_groups = ut.take(orig_grouped_annots, orig_rand_idxs)
orig_annot_sample = random_annot_groups[:orig_sample_size]
# OOOPS MADE ERROR REDO ----
filter_kw = {
'multiple': None,
'view': ['right'],
'minqual': 'good',
'is_known': True,
'min_pername': 2,
}
filter_kw_ = ut.dict_union(
filter_kw,
{
'min_unixtime':
ut.datetime_to_posixtime(ut.date_to_datetime(day1, 0.0)),
'max_unixtime':
ut.datetime_to_posixtime(ut.date_to_datetime(day2, 1.0)),
},
)
refiltered_sample = [
ibs.filter_annots_general(annot.aids, filter_kw=filter_kw_)
for annot in orig_annot_sample
]
is_ok = np.array(ut.lmap(len, refiltered_sample)) >= 2
ok_part_orig_sample = ut.compress(orig_annot_sample, is_ok)
ok_part_orig_nids = [x.nids[0] for x in ok_part_orig_sample]
# Now compute real sample
aids = ibs.filter_annots_general(filter_kw=filter_kw_)
all_annots = ibs.annots(aids)
unique_nids, grouped_annots_ = all_annots.group(all_annots.nids)
grouped_annots = grouped_annots_
# Ensure we get everything
# grouped_annots = [ibs.annots(aids_) for aids_ in ibs.get_name_aids(unique_nids)]
pop = len(grouped_annots)
pername_list = ut.lmap(len, grouped_annots)
groups = wbia.annots.AnnotGroups(grouped_annots, ibs)
match_tags = [ut.unique(ut.flatten(t)) for t in groups.match_tags]
tag_case_hist = ut.dict_hist(ut.flatten(match_tags))
print('name_pop = %r' % (pop, ))
print('Annots per Multiton Name' +
ut.repr3(ut.get_stats(pername_list, use_median=True)))
print('Name Tag Hist ' + ut.repr3(tag_case_hist))
print('Percent Photobomb: %.2f%%' %
(tag_case_hist['photobomb'] / pop * 100))
print('Percent Split: %.2f%%' % (tag_case_hist['splitcase'] / pop * 100))
# Remove the ok part from this sample
remain_unique_nids = ut.setdiff(unique_nids, ok_part_orig_nids)
remain_grouped_annots = [
ibs.annots(aids_) for aids_ in ibs.get_name_aids(remain_unique_nids)
]
sample_size = 75
import vtool as vt
vt.calc_sample_from_error_bars(0.05, pop, conf_level=0.95, prior=0.05)
remain_rand_idxs = ut.random_indexes(len(remain_grouped_annots),
seed=3340258)
remain_sample_size = sample_size - len(ok_part_orig_nids)
remain_random_annot_groups = ut.take(remain_grouped_annots,
remain_rand_idxs)
remain_annot_sample = remain_random_annot_groups[:remain_sample_size]
annot_sample_nofilter = ok_part_orig_sample + remain_annot_sample
# Filter out all bad parts
annot_sample_filter = [
ibs.annots(ibs.filter_annots_general(annot.aids, filter_kw=filter_kw_))
for annot in annot_sample_nofilter
]
annot_sample = annot_sample_filter
win = None
from wbia.viz import viz_graph2
for annots in ut.InteractiveIter(annot_sample):
if win is not None:
win.close()
win = viz_graph2.make_qt_graph_interface(ibs,
aids=annots.aids,
init_mode='rereview')
print(win)
sample_groups = wbia.annots.AnnotGroups(annot_sample, ibs)
flat_tags = [ut.unique(ut.flatten(t)) for t in sample_groups.match_tags]
print('Using Split and Photobomb')
is_positive = ['photobomb' in t or 'splitcase' in t for t in flat_tags]
num_positive = sum(is_positive)
vt.calc_error_bars_from_sample(sample_size,
num_positive,
pop,
conf_level=0.95)
print('Only Photobomb')
is_positive = ['photobomb' in t for t in flat_tags]
num_positive = sum(is_positive)
vt.calc_error_bars_from_sample(sample_size,
num_positive,
pop,
conf_level=0.95)
print('Only SplitCase')
is_positive = ['splitcase' in t for t in flat_tags]
num_positive = sum(is_positive)
vt.calc_error_bars_from_sample(sample_size,
num_positive,
pop,
conf_level=0.95)
def random_xy_sample(X, y, size_, data_per_label, seed=0):
label_indicies = ut.random_indexes(len(y), seed=seed)[0:size_]
data_indicies = expand_data_indicies(label_indicies, data_per_label)
X_subset = X.take(data_indicies, axis=0)
y_sbuset = y.take(label_indicies, axis=0)
return X_subset, y_sbuset