def make_cm_very_old_tuple(qres_copy):
assert ut.listfind(qres_copy.filtkey_list, filtkey) is None
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = special_query.index_partition(
qres_copy.filtkey_list, weight_filters)
aid2_fsv = {}
aid2_fs = {}
aid2_score = {}
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
pass
break
#scorex_vsone = ut.listfind(qres_copy.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = special_query.weighted_average_scoring(
new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
aid2_fsv[daid] = new_fsv_vsone
aid2_fs[daid] = new_fs_vsone
aid2_score[daid] = new_score_vsone
return aid2_fsv, aid2_fs, aid2_score
def make_cm_very_old_tuple(qres_copy):
assert ut.listfind(qres_copy.filtkey_list, filtkey) is None
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = special_query.index_partition(qres_copy.filtkey_list, weight_filters)
aid2_fsv = {}
aid2_fs = {}
aid2_score = {}
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
pass
break
#scorex_vsone = ut.listfind(qres_copy.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = special_query.weighted_average_scoring(new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = special_query.product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
aid2_fsv[daid] = new_fsv_vsone
aid2_fs[daid] = new_fs_vsone
aid2_score[daid] = new_score_vsone
return aid2_fsv, aid2_fs, aid2_score
def get_new_qres_distinctiveness(qres_vsone, qres_vsmany, top_aids, filtkey):
"""
gets the distinctiveness score from vsmany and applies it to vsone
CommandLine:
python -m ibeis.algo.hots.special_query --exec-get_new_qres_distinctiveness
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.special_query import * # NOQA
>>> ibs, valid_aids = testdata_special_query()
>>> qaids = valid_aids[0:1]
>>> daids = valid_aids[1:]
>>> qaid = qaids[0]
>>> filtkey = hstypes.FiltKeys.DISTINCTIVENESS
>>> use_cache = False
>>> # execute function
>>> qaid2_qres_vsmany, qreq_vsmany_ = query_vsmany_initial(ibs, qaids, daids, use_cache)
>>> vsone_query_pairs = build_vsone_shortlist(ibs, qaid2_qres_vsmany)
>>> qaid2_qres_vsone, qreq_vsone_ = query_vsone_pairs(ibs, vsone_query_pairs, use_cache)
>>> qreq_vsone_.load_score_normalizer()
>>> qres_vsone = qaid2_qres_vsone[qaid]
>>> qres_vsmany = qaid2_qres_vsmany[qaid]
>>> top_aids = vsone_query_pairs[0][1]
>>> # verify results
>>> newfsv_list, newscore_aids = get_new_qres_distinctiveness(qres_vsone, qres_vsmany, top_aids, filtkey)
"""
newfsv_list = []
newscore_aids = []
# make sure filter does not already exist
scorex_vsone = ut.listfind(qres_vsone.filtkey_list, filtkey)
# Make new filtkey_list
new_filtkey_list = qres_vsone.filtkey_list[:]
new_filtkey_list.append(filtkey)
newscore_aids = top_aids[:]
for daid in top_aids:
# Distinctiveness is mostly independent of the vsmany database results
if daid not in qres_vsone.aid2_fm: # or daid not in qres_vsmany.aid2_fm):
# no matches to work with
continue
if scorex_vsone is None:
new_fsv_vsone = new_feature_score_dimension(qres_vsone, daid)
assert len(new_filtkey_list) == len(new_fsv_vsone.T), 'filter length is not consistent'
fm_vsone = qres_vsone.aid2_fm[daid]
qfx_vsone = fm_vsone.T[0]
# Use vsmany as the distinctivness
# Get the distinctiveness score from the neighborhood
# around each query point in the vsmany query result
norm_sqared_dist = qres_vsmany.qfx2_dist.T[-1].take(qfx_vsone)
norm_dist = np.sqrt(norm_sqared_dist)
# FIXME: params not used
# but this is probably depricated anyway
dcvs_power, dcvs_max_clip, dcvs_min_clip = 1.0, 1.0, 0.0
dstncvs = distinctiveness_normalizer.compute_distinctiveness_from_dist(norm_dist, dcvs_power, dcvs_max_clip, dcvs_min_clip)
# Copy new scores to the new fsv vector
new_fsv_vsone.T[-1].T[:] = dstncvs #
newfsv_list.append(new_fsv_vsone)
return newfsv_list, newscore_aids
def find_row_from_id(self, _id):
"""
given an id (like an ibeis rowid) find the row of this item
"""
children = self.get_children()
id_list = [child.get_id() for child in children]
row = ut.listfind(id_list, _id)
return row
def find_row_from_id(self, _id):
"""
given an id (like an ibeis rowid) find the row of this item
"""
children = self.get_children()
id_list = [child.get_id() for child in children]
row = ut.listfind(id_list, _id)
return row
def get_prefered_browser(pref_list=[], fallback=True):
r"""
Args:
browser_preferences (list): (default = [])
fallback (bool): uses default if non of preferences work (default = True)
CommandLine:
python -m utool.util_grabdata --test-get_prefered_browser
Ignore:
import webbrowser
webbrowser._tryorder
pref_list = ['chrome', 'firefox', 'google-chrome']
pref_list = ['firefox', 'google-chrome']
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_grabdata import * # NOQA
>>> browser_preferences = ['firefox', 'chrome', 'safari']
>>> fallback = True
>>> browser = get_prefered_browser(browser_preferences, fallback)
>>> result = ('browser = %s' % (str(browser),))
>>> print(result)
>>> ut.quit_if_noshow()
"""
import webbrowser
import utool as ut
pref_list = ut.ensure_iterable(pref_list)
error_list = []
# Hack for finding chrome on win32
if ut.WIN32:
# http://stackoverflow.com/questions/24873302/webbrowser-chrome-exe-does-not-work
win32_chrome_fpath = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe'
win32_chrome_browsername = win32_chrome_fpath + ' %s'
win32_map = {
'chrome': win32_chrome_browsername,
'google-chrome': win32_chrome_browsername,
}
for browsername, win32_browsername in win32_map.items():
index = ut.listfind(pref_list, browsername)
if index is not None and True: # ut.checkpath(win32_browsername):
pref_list.insert(index + 1, win32_browsername)
for browsername in pref_list:
try:
browser = webbrowser.get(browsername)
return browser
except webbrowser.Error as ex:
error_list.append(ex)
print(str(browsername) + ' failed. Reason: ' + str(ex))
if fallback:
browser = webbrowser
return browser
else:
raise AssertionError('No browser meets preferences=%r. error_list=%r' %
(pref_list, error_list,))
def apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list,
newscore_aids, filtkey):
r"""
applies the new filter scores vectors to a query result and updates other
scores
Args:
qres_vsone (QueryResult): object of feature correspondences and scores
newfsv_list (list):
newscore_aids (?):
filtkey (?):
CommandLine:
python -m ibeis.algo.hots.special_query --test-apply_new_qres_filter_scores
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.special_query import * # NOQA
>>> ibs, valid_aids = testdata_special_query()
>>> qaids = valid_aids[0:1]
>>> daids = valid_aids[1:]
>>> qaid = qaids[0]
>>> filtkey = hstypes.FiltKeys.DISTINCTIVENESS
>>> use_cache = False
>>> qaid2_qres_vsmany, qreq_vsmany_ = query_vsmany_initial(ibs, qaids, daids, use_cache)
>>> vsone_query_pairs = build_vsone_shortlist(ibs, qaid2_qres_vsmany)
>>> qaid2_qres_vsone, qreq_vsone_ = query_vsone_pairs(ibs, vsone_query_pairs, use_cache)
>>> qreq_vsone_.load_score_normalizer()
>>> qres_vsone = qaid2_qres_vsone[qaid]
>>> qres_vsmany = qaid2_qres_vsmany[qaid]
>>> top_aids = vsone_query_pairs[0][1]
>>> newfsv_list, newscore_aids = get_new_qres_distinctiveness(qres_vsone, qres_vsmany, top_aids, filtkey)
>>> apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey)
Ignore:
qres_vsone.show_top(ibs, name_scoring=True)
print(qres_vsone.get_inspect_str(ibs=ibs, name_scoring=True))
print(qres_vsmany.get_inspect_str(ibs=ibs, name_scoring=True))
"""
assert ut.listfind(qres_vsone.filtkey_list, filtkey) is None
# HACK to update result cfgstr
qres_vsone.filtkey_list.append(filtkey)
qres_vsone.cfgstr = qreq_vsone_.get_cfgstr()
# Find positions of weight filters and score filters
# so we can apply a weighted average
#numer_filters = [hstypes.FiltKeys.LNBNN, hstypes.FiltKeys.RATIO]
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = vt.index_partition(
qres_vsone.filtkey_list, weight_filters)
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
#scorex_vsone = ut.listfind(qres_vsone.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = vt.weighted_average_scoring(new_fsv_vsone,
weight_filtxs,
nonweight_filtxs)
else:
# product scoring
new_fs_vsone = product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
qres_vsone.aid2_fsv[daid] = new_fsv_vsone
qres_vsone.aid2_fs[daid] = new_fs_vsone
qres_vsone.aid2_score[daid] = new_score_vsone
# FIXME: this is not how to compute new probability
#if qres_vsone.aid2_prob is not None:
# qres_vsone.aid2_prob[daid] = qres_vsone.aid2_score[daid]
# This is how to compute new probability
if qreq_vsone_.qparams.score_normalization:
# FIXME: TODO: Have unsupported scores be represented as Nones
# while score normalizer is still being trained.
normalizer = qreq_vsone_.normalizer
daid2_score = qres_vsone.aid2_score
score_list = list(six.itervalues(daid2_score))
daid_list = list(six.iterkeys(daid2_score))
prob_list = normalizer.normalize_score_list(score_list)
daid2_prob = dict(zip(daid_list, prob_list))
qres_vsone.aid2_prob = daid2_prob
def db_to_dbdir(db, allow_newdir=False, extra_workdirs=[]):
"""
Implicitly gets dbdir. Searches for db inside of workdir
"""
if ut.VERBOSE:
logger.info('[sysres] db_to_dbdir: db=%r, allow_newdir=%r' % (db, allow_newdir))
if db is None:
raise ValueError('db is None')
work_dir = get_workdir()
dbalias_dict = get_dbalias_dict()
workdir_list = []
for extra_dir in extra_workdirs:
if exists(extra_dir):
workdir_list.append(extra_dir)
workdir_list.append(work_dir) # TODO: Allow multiple workdirs?
# Check all of your work directories for the database
for _dir in workdir_list:
dbdir = realpath(join(_dir, db))
# Use db aliases
if not exists(dbdir) and db.upper() in dbalias_dict:
dbdir = join(_dir, dbalias_dict[db.upper()])
if exists(dbdir):
break
# Create the database if newdbs are allowed in the workdir
# logger.info('allow_newdir=%r' % allow_newdir)
if allow_newdir:
ut.ensuredir(dbdir, verbose=True)
# Complain if the implicit dbdir does not exist
if not exists(dbdir):
logger.info('!!!')
logger.info('[sysres] WARNING: db=%r not found in work_dir=%r' % (db, work_dir))
fname_list = os.listdir(work_dir)
lower_list = [fname.lower() for fname in fname_list]
index = ut.listfind(lower_list, db.lower())
if index is not None:
logger.info('[sysres] WARNING: db capitalization seems to be off')
if not ut.STRICT:
logger.info('[sysres] attempting to fix it')
db = fname_list[index]
dbdir = join(work_dir, db)
logger.info('[sysres] dbdir=%r' % dbdir)
logger.info('[sysres] db=%r' % db)
if not exists(dbdir):
msg = '[sysres!] ERROR: Database does not exist and allow_newdir=False'
logger.info('<!!!>')
logger.info(msg)
logger.info(
'[sysres!] Here is a list of valid dbs: '
+ ut.indentjoin(sorted(fname_list), '\n * ')
)
logger.info('[sysres!] dbdir=%r' % dbdir)
logger.info('[sysres!] db=%r' % db)
logger.info('[sysres!] work_dir=%r' % work_dir)
logger.info('</!!!>')
raise AssertionError(msg)
logger.info('!!!')
return dbdir
def get_new_qres_distinctiveness(qres_vsone, qres_vsmany, top_aids, filtkey):
"""
gets the distinctiveness score from vsmany and applies it to vsone
CommandLine:
python -m ibeis.algo.hots.special_query --exec-get_new_qres_distinctiveness
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.special_query import * # NOQA
>>> ibs, valid_aids = testdata_special_query()
>>> qaids = valid_aids[0:1]
>>> daids = valid_aids[1:]
>>> qaid = qaids[0]
>>> filtkey = hstypes.FiltKeys.DISTINCTIVENESS
>>> use_cache = False
>>> # execute function
>>> qaid2_qres_vsmany, qreq_vsmany_ = query_vsmany_initial(ibs, qaids, daids, use_cache)
>>> vsone_query_pairs = build_vsone_shortlist(ibs, qaid2_qres_vsmany)
>>> qaid2_qres_vsone, qreq_vsone_ = query_vsone_pairs(ibs, vsone_query_pairs, use_cache)
>>> qreq_vsone_.load_score_normalizer()
>>> qres_vsone = qaid2_qres_vsone[qaid]
>>> qres_vsmany = qaid2_qres_vsmany[qaid]
>>> top_aids = vsone_query_pairs[0][1]
>>> # verify results
>>> newfsv_list, newscore_aids = get_new_qres_distinctiveness(qres_vsone, qres_vsmany, top_aids, filtkey)
"""
newfsv_list = []
newscore_aids = []
# make sure filter does not already exist
scorex_vsone = ut.listfind(qres_vsone.filtkey_list, filtkey)
# Make new filtkey_list
new_filtkey_list = qres_vsone.filtkey_list[:]
new_filtkey_list.append(filtkey)
newscore_aids = top_aids[:]
for daid in top_aids:
# Distinctiveness is mostly independent of the vsmany database results
if daid not in qres_vsone.aid2_fm: # or daid not in qres_vsmany.aid2_fm):
# no matches to work with
continue
if scorex_vsone is None:
new_fsv_vsone = new_feature_score_dimension(qres_vsone, daid)
assert len(new_filtkey_list) == len(
new_fsv_vsone.T), 'filter length is not consistent'
fm_vsone = qres_vsone.aid2_fm[daid]
qfx_vsone = fm_vsone.T[0]
# Use vsmany as the distinctivness
# Get the distinctiveness score from the neighborhood
# around each query point in the vsmany query result
norm_sqared_dist = qres_vsmany.qfx2_dist.T[-1].take(qfx_vsone)
norm_dist = np.sqrt(norm_sqared_dist)
# FIXME: params not used
# but this is probably depricated anyway
dcvs_power, dcvs_max_clip, dcvs_min_clip = 1.0, 1.0, 0.0
dstncvs = distinctiveness_normalizer.compute_distinctiveness_from_dist(
norm_dist, dcvs_power, dcvs_max_clip, dcvs_min_clip)
# Copy new scores to the new fsv vector
new_fsv_vsone.T[-1].T[:] = dstncvs #
newfsv_list.append(new_fsv_vsone)
return newfsv_list, newscore_aids
def get_extern_distinctiveness(qreq_, cm, **kwargs):
r"""
Uses distinctivness normalizer class (which uses predownloaded models)
to normalize the distinctivness of a keypoint for query points.
IDEA:
because we have database points as well we can use the distance between
normalizer of the query point and the normalizer of the database point.
They should have a similar normalizer if they are a correct match AND
nondistinctive.
Args:
qreq_ (QueryRequest): query request object with hyper-parameters
cm (QueryResult): object of feature correspondences and scores
Returns:
tuple: (new_fsv_list, daid_list)
CommandLine:
python -m ibeis.algo.hots.special_query --test-get_extern_distinctiveness
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.special_query import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> daids = ibs.get_valid_aids(species=ibeis.const.TEST_SPECIES.ZEB_PLAIN)
>>> qaids = daids[0:1]
>>> cfgdict = dict(codename='vsone_unnorm_dist_ratio_extern_distinctiveness')
>>> qreq_ = ibs.new_query_request(qaids, daids, cfgdict=cfgdict)
>>> #qreq_.lazy_load()
>>> cm = ibs.query_chips(qreq_=qreq_, use_cache=False, save_qcache=False)[0]
>>> # execute function
>>> (new_fsv_list, daid_list) = get_extern_distinctiveness(qreq_, cm)
>>> # verify results
>>> assert all([fsv.shape[1] == 1 + len(cm.filtkey_list) for fsv in new_fsv_list])
>>> assert all([np.all(fsv.T[-1] >= 0) for fsv in new_fsv_list])
>>> assert all([np.all(fsv.T[-1] <= 1) for fsv in new_fsv_list])
"""
dstcnvs_normer = qreq_.dstcnvs_normer
assert dstcnvs_normer is not None, 'must have loaded normalizer'
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
# make sure filter does not already exist
scorex_vsone = ut.listfind(cm.filtkey_list, filtkey)
assert scorex_vsone is None, 'already applied distinctivness'
daid_list = list(six.iterkeys(cm.aid2_fsv))
# Find subset of features to get distinctivness of
qfxs_list = [cm.aid2_fm[daid].T[0] for daid in daid_list]
query_vecs = qreq_.ibs.get_annot_vecs(cm.qaid, config2_=qreq_.qparams)
# there might be duplicate feature indexes in the list of feature index
# lists. We can use to perform neighbor lookup more efficiently by only
# performing a single query per feature index. Utool does the mapping for us
def rowid_distinctivness(unique_flat_qfx_list,
dstcnvs_normer=None,
query_vecs=None,
**kwargs):
# Take only the unique vectors
unique_flat_subvecs = query_vecs.take(unique_flat_qfx_list, axis=0)
unique_flat_dstcvns = dstcnvs_normer.get_distinctiveness(
unique_flat_subvecs, **kwargs)
return unique_flat_dstcvns[:, None]
aug_fsv_list = ut.unflat_unique_rowid_map(rowid_distinctivness,
qfxs_list,
dstcnvs_normer=dstcnvs_normer,
query_vecs=query_vecs,
**kwargs)
if False:
with ut.Timer('time1'):
aug_fsv_list = ut.unflat_unique_rowid_map(
rowid_distinctivness,
qfxs_list,
dstcnvs_normer=dstcnvs_normer,
query_vecs=query_vecs)
with ut.Timer('time2'):
# Less efficient way to do this
_vecs_list = [query_vecs.take(qfxs, axis=0) for qfxs in qfxs_list]
_aug_fsv_list = [
dstcnvs_normer.get_distinctiveness(_vecs)[:, None]
for _vecs in _vecs_list
]
isequal_list = [
np.all(np.equal(*tup)) for tup in zip(aug_fsv_list, _aug_fsv_list)
]
assert all(isequal_list), 'utool is broken'
# Compute the distinctiveness as the augmenting score
# ensure the shape is (X, 1)
# Stack the new and augmenting scores
old_fsv_list = [cm.aid2_fsv[daid] for daid in daid_list]
new_fsv_list = list(map(np.hstack, zip(old_fsv_list, aug_fsv_list)))
# FURTHER HACKS TO SCORING
#if 'fg_power' in kwargs:
for filtkey in hstypes.WEIGHT_FILTERS:
key = filtkey + '_power'
if key in kwargs:
_power = kwargs[key]
_index = ut.listfind(cm.filtkey_list, filtkey)
for fsv in new_fsv_list:
fsv.T[_index] **= _power
#new_aid2_fsv = dict(zip(daid_list, new_fsv_list))
return new_fsv_list, daid_list
def total_purge_developed_repo(repodir):
r"""
Outputs commands to help purge a repo
Args:
repodir (str): path to developed repository
CommandLine:
python -m utool.util_sysreq total_purge_installed_repo --show
Ignore:
repodir = ut.truepath('~/code/Lasagne')
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_sysreq import * # NOQA
>>> import utool as ut
>>> repodir = ut.get_argval('--repodir', default=None)
>>> result = total_purge_installed_repo(repodir)
"""
assert repodir is not None
import utool as ut
import os
repo = ut.util_git.Repo(dpath=repodir)
user = os.environ['USER']
fmtdict = dict(
user=user,
modname=repo.modname,
reponame=repo.reponame,
dpath=repo.dpath,
global_site_pkgs=ut.get_global_dist_packages_dir(),
local_site_pkgs=ut.get_local_dist_packages_dir(),
venv_site_pkgs=ut.get_site_packages_dir(),
)
commands = [_.format(**fmtdict) for _ in [
'pip uninstall {modname}',
'sudo -H pip uninstall {modname}',
'sudo pip uninstall {modname}',
'easy_install -m {modname}',
'cd {dpath} && python setup.py develop --uninstall',
# If they still exist try chowning to current user
'sudo chown -R {user}:{user} {dpath}',
]]
print('Normal uninstall commands')
print('\n'.join(commands))
possible_link_paths = [_.format(**fmtdict) for _ in [
'{dpath}/{modname}.egg-info',
'{dpath}/build',
'{venv_site_pkgs}/{reponame}.egg-info',
'{local_site_pkgs}/{reponame}.egg-info',
'{venv_site_pkgs}/{reponame}.egg-info',
]]
from os.path import exists, basename
existing_link_paths = [path for path in possible_link_paths]
print('# Delete paths and eggs')
for path in existing_link_paths:
if exists(path):
if ut.get_file_info(path)['owner'] != user:
print('sudo /bin/rm -rf {path}'.format(path=path))
else:
print('/bin/rm -rf {path}'.format(path=path))
#ut.delete(path)
print('# Make sure nothing is in the easy install paths')
easyinstall_paths = [_.format(**fmtdict) for _ in [
'{venv_site_pkgs}/easy-install.pth',
'{local_site_pkgs}/easy-install.pth',
'{venv_site_pkgs}/easy-install.pth',
]]
for path in easyinstall_paths:
if exists(path):
easy_install_list = ut.readfrom(path, verbose=False).strip().split('\n')
easy_install_list_ = [basename(p) for p in easy_install_list]
index1 = ut.listfind(easy_install_list_, repo.reponame)
index2 = ut.listfind(easy_install_list_, repo.modname)
if index1 is not None or index2 is not None:
print('Found at index1=%r, index=%r' % (index1, index2))
if ut.get_file_info(path)['owner'] != user:
print('sudo gvim {path}'.format(path=path))
else:
print('gvim {path}'.format(path=path))
checkcmds = [_.format(**fmtdict) for _ in [
'python -c "import {modname}; print({modname}.__file__)"'
]]
import sys
assert repo.modname not in sys.modules
print("# CHECK STATUS")
for cmd in checkcmds:
print(cmd)
aids_tup = (
all_daids_t,
top_aids_vsone_t,
top_aids_vsmany_t,
(qaid_t, ),
)
nids_tup = ibs_gt.unflat_map(ibs_gt.get_annot_nids, aids_tup)
(
all_nids_t,
top_nids_vsone_t,
top_nids_vsmany_t,
(qnid_t, ),
) = nids_tup
vsmany_rank = ut.listfind(top_nids_vsmany_t, qnid_t)
vsone_rank = ut.listfind(top_nids_vsone_t, qnid_t)
impossible_to_match = ut.listfind(all_nids_t, qnid_t) is None
# Sort the test case into a category
testtup = TestTup(qaid_t, qaid, vsmany_rank, vsone_rank)
if vsmany_rank is None and vsone_rank is None and impossible_to_match:
append_case(SINGLETON, testtup)
elif vsmany_rank is not None and vsone_rank is None:
if vsmany_rank < 5:
append_case(VSMANY_DOMINATES, testtup)
else:
append_case(VSMANY_OUTPERFORMED, testtup)
elif vsmany_rank is None:
append_case(BOTH_FAIL, testtup)
elif vsone_rank > vsmany_rank:
def update_normalizer(ibs, cm, qreq_, chosen_names):
r"""
adds new support data to the current normalizer
FIXME: broken
Args:
ibs (IBEISController): ibeis controller object
qreq_ (QueryRequest): query request object with hyper-parameters
choicetup (?):
name (?):
Returns:
tuple: (tp_rawscore, tn_rawscore)
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-update_normalizer
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_matcher import * # NOQA
>>> ibs, qaid_chunk = testdata_automatch()
>>> exemplar_aids = ibs.get_valid_aids(is_exemplar=True)
>>> incinfo = {}
>>> gen = generate_subquery_steps(ibs, qaid_chunk, incinfo)
>>> item = six.next(gen)
>>> ibs, cm, qreq_, incinfo = item
>>> qreq_.load_score_normalizer()
>>> # verify results
>>> chosen_names = ['easy']
>>> update_normalizer(ibs, cm, qreq_, chosen_names)
"""
# Fixme: duplicate call to get_qres_name_choices
if qreq_.normalizer is None:
print(
'[update_normalizer] NOT UPDATING. qreq_ has not loaded a score normalizer'
)
return
if len(chosen_names) != 1:
print(
'[update_normalizer] NOT UPDATING. only updates using simple matches'
)
return
qaid = cm.qaid
choicetup = automatch_suggestor.get_qres_name_choices(ibs, cm)
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids,
sorted_ascores) = choicetup
# Get new True Negative support data for score normalization
name = chosen_names[0]
rank = ut.listfind(ibs.get_name_texts(sorted_nids), name)
if rank is None:
return
nid = sorted_nids[rank]
tp_rawscore = sorted_rawscore[rank]
valid_falseranks = set(range(len(sorted_rawscore))) - set([rank])
if len(valid_falseranks) > 0:
tn_rank = min(valid_falseranks)
tn_rawscore = sorted_rawscore[tn_rank][0]
else:
tn_rawscore = None
#return tp_rawscore, tn_rawscore
canupdate = tp_rawscore is not None and tn_rawscore is not None
if canupdate:
# TODO: UPDATE SCORE NORMALIZER HERE
print('UPDATING! NORMALIZER')
tp_labels = [ut.deterministic_uuid((qaid, nid))]
tn_labels = [ut.deterministic_uuid((qaid, nid))]
print(
'new normalization example: tp_rawscore={}, tn_rawscore={}'.format(
tp_rawscore, tn_rawscore))
print('new normalization example: tp_labels={}, tn_labels={}'.format(
tp_labels, tn_labels))
tp_scores = [tp_rawscore]
tn_scores = [tn_rawscore]
qreq_.normalizer.add_support(tp_scores, tn_scores, tp_labels,
tn_labels)
qreq_.normalizer.retrain()
species_text = '_'.join(qreq_.get_unique_species()) # HACK
# TODO: figure out where to store normalizer
qreq_.normalizer.save(
ibs.get_local_species_scorenorm_cachedir(species_text))
else:
print('NOUPDATE! cannot update score normalization')
def process_batch(X_train, y_train, batch_size, theano_fn, **kwargs):
"""
compute the loss over all training batches
Jon, if you get to this before I do, please fix. -J
CommandLine:
python -m ibeis_cnn.batch_processing --test-process_batch
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.batch_processing import * # NOQA
>>> from ibeis_cnn import models
>>> model = models.DummyModel(autoinit=True)
>>> X_train, y_train = model.make_random_testdata()
>>> theano_fn = create_unbuffered_iter_funcs_train2(model)
>>> kwargs = {'X_is_cv2_native': False}
>>> batch_size = model.batch_size
>>> (loss, accu, prob_list, albl_list, pred_list, conf_list) = process_batch(X_train, y_train, batch_size, theano_fn)
>>> result = str((loss, accu, prob_list, albl_list, pred_list, conf_list))
>>> print(result)
Ignore:
Xb, yb = batch_iter.next()
assert Xb.shape == (8, 1, 4, 4)
yb.shape == (8,)
"""
batch_output_list = [] # NOQA
output_names = [op.variable.name for op in theano_fn.outputs] # NOQA
albl_list = [] # [a]ugmented [l]a[b]e[l] list
show = False
batch_iter = batch_iterator(X_train, y_train, batch_size, **kwargs)
for Xb, yb in batch_iter:
# Runs a batch through the network and updates the weights. Just returns what it did
batch_output = theano_fn(Xb, yb)
albl_list.append(yb)
batch_output_list.append(batch_output)
if show:
# Print the network output for the first batch
print('--------------')
print(ut.list_str(zip(output_names, batch_output)))
print('Correct: ', yb)
print('--------------')
show = False
# Convert to numpy array
# get outputs of each type
def concatenate_hack(sequence, axis=0):
# Hack to fix numpy bug. concatenate should do hstacks on 0-dim arrays
if len(_output_unstacked) > 0 and len(_output_unstacked[1].shape) == 0:
res = np.hstack(_output_unstacked)
else:
res = np.concatenate(_output_unstacked, axis=axis)
return res
unstacked_output_gen = ([bop[count] for bop in batch_output_list] for count, name in enumerate(output_names))
stacked_output_list = [concatenate_hack(_output_unstacked, axis=-1) for _output_unstacked in unstacked_output_gen]
albl_list = np.hstack(albl_list)
# Calculate performance
loss_index = ut.listfind(output_names, 'loss_train')
if loss_index is not None:
loss_list = stacked_output_list[loss_index]
loss = np.mean(loss_list)
pred_index = ut.listfind(output_names, 'prediction')
if pred_index is not None:
pred_list = stacked_output_list[pred_index]
accu = np.mean(np.equal(albl_list, pred_list))
# Return
return loss, accu, prob_list, albl_list, pred_list, conf_list
def update_normalizer(ibs, cm, qreq_, chosen_names):
r"""
adds new support data to the current normalizer
FIXME: broken
Args:
ibs (IBEISController): ibeis controller object
qreq_ (QueryRequest): query request object with hyper-parameters
choicetup (?):
name (?):
Returns:
tuple: (tp_rawscore, tn_rawscore)
CommandLine:
python -m ibeis.algo.hots.automated_matcher --test-update_normalizer
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_matcher import * # NOQA
>>> ibs, qaid_chunk = testdata_automatch()
>>> exemplar_aids = ibs.get_valid_aids(is_exemplar=True)
>>> incinfo = {}
>>> gen = generate_subquery_steps(ibs, qaid_chunk, incinfo)
>>> item = six.next(gen)
>>> ibs, cm, qreq_, incinfo = item
>>> qreq_.load_score_normalizer()
>>> # verify results
>>> chosen_names = ['easy']
>>> update_normalizer(ibs, cm, qreq_, chosen_names)
"""
# Fixme: duplicate call to get_qres_name_choices
if qreq_.normalizer is None:
print('[update_normalizer] NOT UPDATING. qreq_ has not loaded a score normalizer')
return
if len(chosen_names) != 1:
print('[update_normalizer] NOT UPDATING. only updates using simple matches')
return
qaid = cm.qaid
choicetup = automatch_suggestor.get_qres_name_choices(ibs, cm)
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids, sorted_ascores) = choicetup
# Get new True Negative support data for score normalization
name = chosen_names[0]
rank = ut.listfind(ibs.get_name_texts(sorted_nids), name)
if rank is None:
return
nid = sorted_nids[rank]
tp_rawscore = sorted_rawscore[rank]
valid_falseranks = set(range(len(sorted_rawscore))) - set([rank])
if len(valid_falseranks) > 0:
tn_rank = min(valid_falseranks)
tn_rawscore = sorted_rawscore[tn_rank][0]
else:
tn_rawscore = None
#return tp_rawscore, tn_rawscore
canupdate = tp_rawscore is not None and tn_rawscore is not None
if canupdate:
# TODO: UPDATE SCORE NORMALIZER HERE
print('UPDATING! NORMALIZER')
tp_labels = [ut.deterministic_uuid((qaid, nid))]
tn_labels = [ut.deterministic_uuid((qaid, nid))]
print('new normalization example: tp_rawscore={}, tn_rawscore={}'.format(tp_rawscore, tn_rawscore))
print('new normalization example: tp_labels={}, tn_labels={}'.format(tp_labels, tn_labels))
tp_scores = [tp_rawscore]
tn_scores = [tn_rawscore]
qreq_.normalizer.add_support(tp_scores, tn_scores, tp_labels, tn_labels)
qreq_.normalizer.retrain()
species_text = '_'.join(qreq_.get_unique_species()) # HACK
# TODO: figure out where to store normalizer
qreq_.normalizer.save(ibs.get_local_species_scorenorm_cachedir(species_text))
else:
print('NOUPDATE! cannot update score normalization')
def total_purge_developed_repo(repodir):
r"""
Outputs commands to help purge a repo
Args:
repodir (str): path to developed repository
CommandLine:
python -m utool.util_sysreq total_purge_installed_repo --show
Ignore:
repodir = ut.truepath('~/code/Lasagne')
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_sysreq import * # NOQA
>>> import utool as ut
>>> repodir = ut.get_argval('--repodir', default=None)
>>> result = total_purge_installed_repo(repodir)
"""
assert repodir is not None
import utool as ut
import os
repo = ut.util_git.Repo(dpath=repodir)
user = os.environ['USER']
fmtdict = dict(
user=user,
modname=repo.modname,
reponame=repo.reponame,
dpath=repo.dpath,
global_site_pkgs=ut.get_global_dist_packages_dir(),
local_site_pkgs=ut.get_local_dist_packages_dir(),
venv_site_pkgs=ut.get_site_packages_dir(),
)
commands = [
_.format(**fmtdict) for _ in [
'pip uninstall {modname}',
'sudo -H pip uninstall {modname}',
'sudo pip uninstall {modname}',
'easy_install -m {modname}',
'cd {dpath} && python setup.py develop --uninstall',
# If they still exist try chowning to current user
'sudo chown -R {user}:{user} {dpath}',
]
]
print('Normal uninstall commands')
print('\n'.join(commands))
possible_link_paths = [
_.format(**fmtdict) for _ in [
'{dpath}/{modname}.egg-info',
'{dpath}/build',
'{venv_site_pkgs}/{reponame}.egg-info',
'{local_site_pkgs}/{reponame}.egg-info',
'{venv_site_pkgs}/{reponame}.egg-info',
]
]
from os.path import exists, basename
existing_link_paths = [path for path in possible_link_paths]
print('# Delete paths and eggs')
for path in existing_link_paths:
if exists(path):
if ut.get_file_info(path)['owner'] != user:
print('sudo /bin/rm -rf {path}'.format(path=path))
else:
print('/bin/rm -rf {path}'.format(path=path))
#ut.delete(path)
print('# Make sure nothing is in the easy install paths')
easyinstall_paths = [
_.format(**fmtdict) for _ in [
'{venv_site_pkgs}/easy-install.pth',
'{local_site_pkgs}/easy-install.pth',
'{venv_site_pkgs}/easy-install.pth',
]
]
for path in easyinstall_paths:
if exists(path):
easy_install_list = ut.readfrom(path,
verbose=False).strip().split('\n')
easy_install_list_ = [basename(p) for p in easy_install_list]
index1 = ut.listfind(easy_install_list_, repo.reponame)
index2 = ut.listfind(easy_install_list_, repo.modname)
if index1 is not None or index2 is not None:
print('Found at index1=%r, index=%r' % (index1, index2))
if ut.get_file_info(path)['owner'] != user:
print('sudo gvim {path}'.format(path=path))
else:
print('gvim {path}'.format(path=path))
checkcmds = [
_.format(**fmtdict)
for _ in ['python -c "import {modname}; print({modname}.__file__)"']
]
import sys
assert repo.modname not in sys.modules
print("# CHECK STATUS")
for cmd in checkcmds:
print(cmd)
def apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey):
r"""
applies the new filter scores vectors to a query result and updates other
scores
Args:
qres_vsone (QueryResult): object of feature correspondences and scores
newfsv_list (list):
newscore_aids (?):
filtkey (?):
CommandLine:
python -m ibeis.algo.hots.special_query --test-apply_new_qres_filter_scores
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.special_query import * # NOQA
>>> ibs, valid_aids = testdata_special_query()
>>> qaids = valid_aids[0:1]
>>> daids = valid_aids[1:]
>>> qaid = qaids[0]
>>> filtkey = hstypes.FiltKeys.DISTINCTIVENESS
>>> use_cache = False
>>> qaid2_qres_vsmany, qreq_vsmany_ = query_vsmany_initial(ibs, qaids, daids, use_cache)
>>> vsone_query_pairs = build_vsone_shortlist(ibs, qaid2_qres_vsmany)
>>> qaid2_qres_vsone, qreq_vsone_ = query_vsone_pairs(ibs, vsone_query_pairs, use_cache)
>>> qreq_vsone_.load_score_normalizer()
>>> qres_vsone = qaid2_qres_vsone[qaid]
>>> qres_vsmany = qaid2_qres_vsmany[qaid]
>>> top_aids = vsone_query_pairs[0][1]
>>> newfsv_list, newscore_aids = get_new_qres_distinctiveness(qres_vsone, qres_vsmany, top_aids, filtkey)
>>> apply_new_qres_filter_scores(qreq_vsone_, qres_vsone, newfsv_list, newscore_aids, filtkey)
Ignore:
qres_vsone.show_top(ibs, name_scoring=True)
print(qres_vsone.get_inspect_str(ibs=ibs, name_scoring=True))
print(qres_vsmany.get_inspect_str(ibs=ibs, name_scoring=True))
"""
assert ut.listfind(qres_vsone.filtkey_list, filtkey) is None
# HACK to update result cfgstr
qres_vsone.filtkey_list.append(filtkey)
qres_vsone.cfgstr = qreq_vsone_.get_cfgstr()
# Find positions of weight filters and score filters
# so we can apply a weighted average
#numer_filters = [hstypes.FiltKeys.LNBNN, hstypes.FiltKeys.RATIO]
weight_filters = hstypes.WEIGHT_FILTERS
weight_filtxs, nonweight_filtxs = vt.index_partition(qres_vsone.filtkey_list, weight_filters)
for new_fsv_vsone, daid in zip(newfsv_list, newscore_aids):
#scorex_vsone = ut.listfind(qres_vsone.filtkey_list, filtkey)
#if scorex_vsone is None:
# TODO: add spatial verification as a filter score
# augment the vsone scores
# TODO: paramaterize
weighted_ave_score = True
if weighted_ave_score:
# weighted average scoring
new_fs_vsone = vt.weighted_average_scoring(new_fsv_vsone, weight_filtxs, nonweight_filtxs)
else:
# product scoring
new_fs_vsone = product_scoring(new_fsv_vsone)
new_score_vsone = new_fs_vsone.sum()
qres_vsone.aid2_fsv[daid] = new_fsv_vsone
qres_vsone.aid2_fs[daid] = new_fs_vsone
qres_vsone.aid2_score[daid] = new_score_vsone
# FIXME: this is not how to compute new probability
#if qres_vsone.aid2_prob is not None:
# qres_vsone.aid2_prob[daid] = qres_vsone.aid2_score[daid]
# This is how to compute new probability
if qreq_vsone_.qparams.score_normalization:
# FIXME: TODO: Have unsupported scores be represented as Nones
# while score normalizer is still being trained.
normalizer = qreq_vsone_.normalizer
daid2_score = qres_vsone.aid2_score
score_list = list(six.itervalues(daid2_score))
daid_list = list(six.iterkeys(daid2_score))
prob_list = normalizer.normalize_score_list(score_list)
daid2_prob = dict(zip(daid_list, prob_list))
qres_vsone.aid2_prob = daid2_prob
def get_extern_distinctiveness(qreq_, cm, **kwargs):
r"""
Uses distinctivness normalizer class (which uses predownloaded models)
to normalize the distinctivness of a keypoint for query points.
IDEA:
because we have database points as well we can use the distance between
normalizer of the query point and the normalizer of the database point.
They should have a similar normalizer if they are a correct match AND
nondistinctive.
Args:
qreq_ (QueryRequest): query request object with hyper-parameters
cm (QueryResult): object of feature correspondences and scores
Returns:
tuple: (new_fsv_list, daid_list)
CommandLine:
python -m ibeis.algo.hots.special_query --test-get_extern_distinctiveness
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.hots.special_query import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> daids = ibs.get_valid_aids(species=ibeis.const.TEST_SPECIES.ZEB_PLAIN)
>>> qaids = daids[0:1]
>>> cfgdict = dict(codename='vsone_unnorm_dist_ratio_extern_distinctiveness')
>>> qreq_ = ibs.new_query_request(qaids, daids, cfgdict=cfgdict)
>>> #qreq_.lazy_load()
>>> cm = ibs.query_chips(qreq_=qreq_, use_cache=False, save_qcache=False)[0]
>>> # execute function
>>> (new_fsv_list, daid_list) = get_extern_distinctiveness(qreq_, cm)
>>> # verify results
>>> assert all([fsv.shape[1] == 1 + len(cm.filtkey_list) for fsv in new_fsv_list])
>>> assert all([np.all(fsv.T[-1] >= 0) for fsv in new_fsv_list])
>>> assert all([np.all(fsv.T[-1] <= 1) for fsv in new_fsv_list])
"""
dstcnvs_normer = qreq_.dstcnvs_normer
assert dstcnvs_normer is not None, 'must have loaded normalizer'
filtkey = hstypes.FiltKeys.DISTINCTIVENESS
# make sure filter does not already exist
scorex_vsone = ut.listfind(cm.filtkey_list, filtkey)
assert scorex_vsone is None, 'already applied distinctivness'
daid_list = list(six.iterkeys(cm.aid2_fsv))
# Find subset of features to get distinctivness of
qfxs_list = [cm.aid2_fm[daid].T[0] for daid in daid_list]
query_vecs = qreq_.ibs.get_annot_vecs(cm.qaid, config2_=qreq_.qparams)
# there might be duplicate feature indexes in the list of feature index
# lists. We can use to perform neighbor lookup more efficiently by only
# performing a single query per feature index. Utool does the mapping for us
def rowid_distinctivness(unique_flat_qfx_list, dstcnvs_normer=None, query_vecs=None, **kwargs):
# Take only the unique vectors
unique_flat_subvecs = query_vecs.take(unique_flat_qfx_list, axis=0)
unique_flat_dstcvns = dstcnvs_normer.get_distinctiveness(unique_flat_subvecs, **kwargs)
return unique_flat_dstcvns[:, None]
aug_fsv_list = ut.unflat_unique_rowid_map(
rowid_distinctivness, qfxs_list,
dstcnvs_normer=dstcnvs_normer, query_vecs=query_vecs, **kwargs)
if False:
with ut.Timer('time1'):
aug_fsv_list = ut.unflat_unique_rowid_map(
rowid_distinctivness, qfxs_list, dstcnvs_normer=dstcnvs_normer,
query_vecs=query_vecs)
with ut.Timer('time2'):
# Less efficient way to do this
_vecs_list = [query_vecs.take(qfxs, axis=0) for qfxs in qfxs_list]
_aug_fsv_list = [dstcnvs_normer.get_distinctiveness(_vecs)[:, None] for _vecs in _vecs_list]
isequal_list = [np.all(np.equal(*tup)) for tup in zip(aug_fsv_list, _aug_fsv_list)]
assert all(isequal_list), 'utool is broken'
# Compute the distinctiveness as the augmenting score
# ensure the shape is (X, 1)
# Stack the new and augmenting scores
old_fsv_list = [cm.aid2_fsv[daid] for daid in daid_list]
new_fsv_list = list(map(np.hstack, zip(old_fsv_list, aug_fsv_list)))
# FURTHER HACKS TO SCORING
#if 'fg_power' in kwargs:
for filtkey in hstypes.WEIGHT_FILTERS:
key = filtkey + '_power'
if key in kwargs:
_power = kwargs[key]
_index = ut.listfind(cm.filtkey_list, filtkey)
for fsv in new_fsv_list:
fsv.T[_index] **= _power
#new_aid2_fsv = dict(zip(daid_list, new_fsv_list))
return new_fsv_list, daid_list
def test_vsone_errors(ibs, daids, qaid2_qres_vsmany, qaid2_qres_vsone, incinfo):
"""
ibs1 = ibs_gt
ibs2 = ibs (the current test database, sorry for the backwardness)
aid1_to_aid2 - maps annots from ibs1 to ibs2
"""
WASH = 'wash'
BOTH_FAIL = 'both_fail'
SINGLETON = 'singleton'
VSMANY_OUTPERFORMED = 'vsmany_outperformed'
VSMANY_DOMINATES = 'vsmany_dominates'
VSMANY_WINS = 'vsmany_wins'
VSONE_WINS = 'vsone_wins'
if 'testcases' not in incinfo:
testcases = {}
for case in [WASH, BOTH_FAIL, SINGLETON, VSMANY_OUTPERFORMED,
VSMANY_DOMINATES, VSMANY_WINS, VSONE_WINS]:
testcases[case] = []
incinfo['testcases'] = testcases
testcases = incinfo['testcases']
def append_case(case, testtup):
print('APPENDED NEW TESTCASE: case=%r' % (case,))
print('* testup = %r' % (testtup,))
print('* vuuid = %r' % (ibs_gt.get_annot_visual_uuids(testtup.qaid_t),))
if ut.get_argflag('--interupt-case') and case in [VSMANY_WINS, VSMANY_DOMINATES]:
incinfo['interactive'] = True
incinfo['use_oracle'] = False
incinfo['STOP'] = True
if ut.is_developer():
import plottool as pt # NOQA
IPYTHON_COMMANDS = """
>>> %pylab qt4
>>> from ibeis.viz.interact import interact_matches # NOQA
>>> #qres_vsmany = ut.search_stack_for_localvar('qres_vsmany')
>>> ibs = ut.search_stack_for_localvar('ibs')
>>> daids = ut.search_stack_for_localvar('daids')
>>> qnid_t = ut.search_stack_for_localvar('qnid_t')
>>> qres_vsone = ut.search_stack_for_localvar('qres_vsone')
>>> all_nids_t = ut.search_stack_for_localvar('all_nids_t')
>>> # Find index in daids of correct matches
>>> cm = qres_vsone
>>> correct_indices = np.where(np.array(all_nids_t) == qnid_t)[0]
>>> correct_aids2 = ut.take(daids, correct_indices)
>>> qaid = cm.qaid
>>> aid = correct_aids2[0]
>>> # Report visual uuid for inclusion or exclusion in script
>>> print(ibs.get_annot_visual_uuids([qaid, aid]))
>>> # Feature match things
>>> print('cm.filtkey_list = %r' % (cm.filtkey_list,))
>>> fm = cm.aid2_fm[aid]
>>> fs = cm.aid2_fs[aid]
>>> fsv = cm.aid2_fsv[aid]
>>> mx = 2
>>> qfx, dfx = fm[mx]
>>> fsv_single = fsv[mx]
>>> fs_single = fs[mx]
>>> # check featweights
>>> data_featweights = ibs.get_annot_fgweights([aid])[0]
>>> data_featweights[dfx]
>>> fnum = pt.next_fnum()
>>> bad_aid = cm.get_top_aids()[0]
>>> #match_interaction_good = interact_matches.MatchInteraction(ibs, cm, aid, annot_mode=1)
>>> #match_interaction_bad = interact_matches.MatchInteraction(ibs, cm, bad_aid)
>>> match_interaction_good = cm.ishow_matches(ibs, aid, annot_mode=1, fnum=1)
>>> match_interaction_bad = cm.ishow_matches(ibs, bad_aid, annot_mode=1, fnum=2)
>>> match_interaction = match_interaction_good
>>> self = match_interaction
>>> self.select_ith_match(mx)
>>> #impossible_to_match = len(correct_indices) > 0
"""
y = """
>>> from os.path import exists
>>> import vtool as vt
>>> import vtool.patch as vtpatch
>>> import vtool.image as vtimage # NOQA
>>> chip_list = ibs.get_annot_chips([aid])
>>> kpts_list = ibs.get_annot_kpts([aid])
>>> probchip_fpath_list = ibs.get_probchip_fpath(aid)
>>> probchip_list = [vt.imread(fpath, grayscale=True) if exists(fpath) else None for fpath in probchip_fpath_list]
>>> kpts = kpts_list[0]
>>> probchip = probchip_list[0]
>>> kp = kpts[dfx]
>>> patch = vt.get_warped_patch(probchip, kp)[0].astype(np.float32) / 255.0
>>> fnum2 = pt.next_fnum()
>>> pt.figure(fnum2, pnum=(1, 2, 1), doclf=True, docla=True)
>>> pt.imshow(probchip)
>>> pt.draw_kpts2([kp])
>>> pt.figure(fnum2, pnum=(1, 2, 2))
>>> pt.imshow(patch * 255)
>>> pt.update()
>>> vt.gaussian_average_patch(patch)
>>> cm.ishow_top(ibs, annot_mode=1)
"""
y
ut.set_clipboard(IPYTHON_COMMANDS)
#ut.spawn_delayed_ipython_paste()
ut.embed(remove_pyqt_hook=False)
IPYTHON_COMMANDS
testcases[case].append(testtup)
for qaid in six.iterkeys(qaid2_qres_vsmany):
qres_vsmany = qaid2_qres_vsmany[qaid]
qres_vsone = qaid2_qres_vsone[qaid]
nscoretup_vsone = qres_vsone.get_nscoretup()
nscoretup_vsmany = qres_vsmany.get_nscoretup()
metatup = incinfo['metatup']
ibs_gt, aid1_to_aid2 = metatup
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
top_aids_vsone = ut.get_list_column(nscoretup_vsone.sorted_aids, 0)
top_aids_vsmany = ut.get_list_column(nscoretup_vsmany.sorted_aids, 0)
# tranform to groundtruth database coordinates
all_daids_t = ut.dict_take_list(aid2_to_aid1, daids)
top_aids_vsone_t = ut.dict_take_list(aid2_to_aid1, top_aids_vsone)
top_aids_vsmany_t = ut.dict_take_list(aid2_to_aid1, top_aids_vsmany)
qaid_t = aid2_to_aid1[qaid]
aids_tup = (all_daids_t, top_aids_vsone_t, top_aids_vsmany_t, (qaid_t,),)
nids_tup = ibs_gt.unflat_map(ibs_gt.get_annot_nids, aids_tup)
(all_nids_t, top_nids_vsone_t, top_nids_vsmany_t, (qnid_t,),) = nids_tup
vsmany_rank = ut.listfind(top_nids_vsmany_t, qnid_t)
vsone_rank = ut.listfind(top_nids_vsone_t, qnid_t)
impossible_to_match = ut.listfind(all_nids_t, qnid_t) is None
# Sort the test case into a category
testtup = TestTup(qaid_t, qaid, vsmany_rank, vsone_rank)
if vsmany_rank is None and vsone_rank is None and impossible_to_match:
append_case(SINGLETON, testtup)
elif vsmany_rank is not None and vsone_rank is None:
if vsmany_rank < 5:
append_case(VSMANY_DOMINATES, testtup)
else:
append_case(VSMANY_OUTPERFORMED, testtup)
elif vsmany_rank is None:
append_case(BOTH_FAIL, testtup)
elif vsone_rank > vsmany_rank:
append_case(VSMANY_WINS, testtup)
elif vsone_rank < vsmany_rank:
append_case(VSONE_WINS, testtup)
elif vsone_rank == vsmany_rank:
append_case(WASH, testtup)
else:
raise AssertionError('unenumerated case')
count_dict = ut.count_dict_vals(testcases)
print('+--')
#print(ut.dict_str(testcases))
print('---')
print(ut.dict_str(count_dict))
print('L__')