def collect_ibeis_training_annotations(ibs, nDaids_basis, verbose=True):
# load a dataset
#dbname = 'PZ_MTEST'
#dbname = 'GZ_ALL'
def get_set_groundfalse(ibs, qaids):
# get groundfalse annots relative to the entire set
valid_nids = ibs.get_valid_nids()
qnids = ibs.get_annot_nids(qaids)
nid_list = list(set(valid_nids) - set(qnids))
aids_list = ibs.get_name_aids(nid_list)
return ut.flatten(aids_list)
# determanism
np.random.seed(0)
random.seed(0)
# TODO: USE ANOT FILTERINGS
import ibeis
qaids_all = ibeis.testdata_aids(
a='default:pername=1,mingt=2,is_known=True')
qaids = qaids_all[::2]
print('nQaids = %r' % len(qaids))
def get_annot_groundtruth_sample(ibs,
aid_list,
per_name=1,
isexemplar=True):
r"""
DEPRICATE
"""
all_trues_list = ibs.get_annot_groundtruth(aid_list,
noself=True,
is_exemplar=isexemplar)
def random_choice(aids):
size = min(len(aids), per_name)
return np.random.choice(aids, size, replace=False).tolist()
sample_trues_list = [
random_choice(aids) if len(aids) > 0 else []
for aids in all_trues_list
]
return sample_trues_list
daids_gt_sample = ut.flatten(
ibs.get_annot_groundtruth_sample(ibs, qaids, isexemplar=None))
daids_gf_all = get_set_groundfalse(ibs, qaids)
ut.assert_eq(len(daids_gt_sample), len(qaids), 'missing gt')
daids_list = []
for target_nDaids in ut.ProgressIter(nDaids_basis, lbl='testing dbsize'):
print('---------------------------')
# Sample one match from the groundtruth with padding
daids_gf_sample = ut.random_sample(
daids_gf_all, max(0, target_nDaids - len(daids_gt_sample)))
daids = sorted(daids_gt_sample + daids_gf_sample)
nDaids = len(daids)
if target_nDaids != nDaids:
continue
daids_list.append(daids)
return qaids, daids_list
def collect_ibeis_training_annotations(ibs, nDaids_basis, verbose=True):
# load a dataset
#dbname = 'PZ_MTEST'
#dbname = 'GZ_ALL'
def get_set_groundfalse(ibs, qaids):
# get groundfalse annots relative to the entire set
valid_nids = ibs.get_valid_nids()
qnids = ibs.get_annot_nids(qaids)
nid_list = list(set(valid_nids) - set(qnids))
aids_list = ibs.get_name_aids(nid_list)
return ut.flatten(aids_list)
# determanism
np.random.seed(0)
random.seed(0)
# TODO: USE ANOT FILTERINGS
import ibeis
qaids_all = ibeis.testdata_aids(a='default:pername=1,mingt=2,is_known=True')
qaids = qaids_all[::2]
print('nQaids = %r' % len(qaids))
def get_annot_groundtruth_sample(ibs, aid_list, per_name=1, isexemplar=True):
r"""
DEPRICATE
"""
all_trues_list = ibs.get_annot_groundtruth(aid_list, noself=True, is_exemplar=isexemplar)
def random_choice(aids):
size = min(len(aids), per_name)
return np.random.choice(aids, size, replace=False).tolist()
sample_trues_list = [random_choice(aids) if len(aids) > 0 else [] for aids in all_trues_list]
return sample_trues_list
daids_gt_sample = ut.flatten(ibs.get_annot_groundtruth_sample(ibs, qaids, isexemplar=None))
daids_gf_all = get_set_groundfalse(ibs, qaids)
ut.assert_eq(len(daids_gt_sample), len(qaids), 'missing gt')
daids_list = []
for target_nDaids in ut.ProgressIter(nDaids_basis, lbl='testing dbsize'):
print('---------------------------')
# Sample one match from the groundtruth with padding
daids_gf_sample = ut.random_sample(daids_gf_all, max(0, target_nDaids - len(daids_gt_sample)))
daids = sorted(daids_gt_sample + daids_gf_sample)
nDaids = len(daids)
if target_nDaids != nDaids:
continue
daids_list.append(daids)
return qaids, daids_list
def test_avl_split(verbose=1):
for num in range(0, 20):
for index in range(num):
if verbose:
print('------')
print('num = %r' % (num,))
print('index = %r' % (index,))
try:
tree0 = EulerTourTree(ut.chr_range(num))
tour = list(tree0)
tree0._assert_nodes()
if verbose >= 2:
tree0.print_tree()
if verbose:
print('tree0 = %r' % (tree0,))
node = tree0.get_node(index)
if verbose:
print('node = %s' % (node,))
part1, part2, bnode = avl_split(tree0.root, node)
tree1 = EulerTourTree(root=part1)
tree2 = EulerTourTree(root=part2)
if verbose >= 2:
tree1.print_tree(), tree2.print_tree()
if verbose:
print('tree1 = %r' % (tree1,))
print('tree2 = %r' % (tree2,))
# Should correspond to a split in the tour list
assert bnode.left is None, 'bnode must be split'
assert bnode.right is None, 'bnode must be split'
assert bnode.parent is None, 'bnode must be split'
assert bnode is node, 'node must be same'
ut.assert_eq(list(tree1), tour[:index])
ut.assert_eq(list(tree2), tour[index + 1:])
tree1._assert_nodes(), tree2._assert_nodes()
except Exception:
print('num = %r' % (num,))
print('index = %r' % (index,))
raise
def get_toy_annots(num_annots,
num_names=None,
initial_aids=None,
initial_nids=None,
nid_sequence=None,
seed=None):
r"""
Args:
num_annots (int):
num_names (int): (default = None)
initial_aids (None): (default = None)
initial_nids (None): (default = None)
nid_sequence (None): (default = None)
seed (None): (default = None)
Returns:
tuple: (aids, nids, aids1, nids1, all_aids, all_nids)
CommandLine:
python -m ibeis.algo.hots.demobayes --exec-get_toy_annots
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.demobayes import * # NOQA
>>> num_annots = 1
>>> num_names = 5
>>> initial_aids = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64)
>>> initial_nids = np.array([0, 0, 1, 2, 2, 1, 1, 1, 2, 3], dtype=np.int64)
>>> nid_sequence = np.array([0, 0, 1, 2, 2, 1, 1], dtype=np.int64)
>>> seed = 0
>>> (aids, nids, aids1, nids1, all_aids, all_nids) = get_toy_annots(num_annots, num_names, initial_aids, initial_nids, nid_sequence, seed)
>>> result = ('(aids, nids, aids1, nids1, all_aids, all_nids) = %s' % (ut.repr2((aids, nids, aids1, nids1, all_aids, all_nids), nl=1),))
>>> print(result)
"""
import vtool as vt
if num_names is None:
num_names = num_annots
print('Generating toy data with num_annots=%r' % (num_annots, ))
if initial_aids is None:
assert initial_nids is None
first_step = True
initial_aids = []
initial_nids = []
else:
first_step = False
assert initial_nids is not None
aids = np.arange(len(initial_aids), num_annots + len(initial_aids))
rng = vt.ensure_rng(seed)
if nid_sequence is None:
nids = rng.randint(0, num_names, num_annots)
else:
unused_from_sequence = max(len(nid_sequence) - len(initial_aids), 0)
if unused_from_sequence == 0:
nids = rng.randint(0, num_names, num_annots)
elif unused_from_sequence > 0 and unused_from_sequence < num_annots:
num_remain = num_annots - unused_from_sequence
nids = np.append(nid_sequence[-unused_from_sequence:],
rng.randint(0, num_names, num_remain))
else:
nids = nid_sequence[-unused_from_sequence]
nids = np.array(
ut.take(
nid_sequence,
range(len(initial_aids),
len(initial_aids) + num_annots)))
if first_step:
aids1 = aids
nids1 = nids
else:
aids1 = initial_aids
nids1 = initial_nids
all_nids = np.append(initial_nids, nids)
all_aids = np.append(initial_aids, aids)
import utool
with utool.embed_on_exception_context:
ut.assert_eq(len(aids), len(nids), 'len new')
ut.assert_eq(len(aids1), len(nids1), 'len comp')
ut.assert_eq(len(all_aids), len(all_nids), 'len all')
return aids, nids, aids1, nids1, all_aids, all_nids
def get_toy_annots(num_annots, num_names=None, initial_aids=None, initial_nids=None, nid_sequence=None, seed=None):
r"""
Args:
num_annots (int):
num_names (int): (default = None)
initial_aids (None): (default = None)
initial_nids (None): (default = None)
nid_sequence (None): (default = None)
seed (None): (default = None)
Returns:
tuple: (aids, nids, aids1, nids1, all_aids, all_nids)
CommandLine:
python -m ibeis.algo.hots.demobayes --exec-get_toy_annots
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.demobayes import * # NOQA
>>> num_annots = 1
>>> num_names = 5
>>> initial_aids = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64)
>>> initial_nids = np.array([0, 0, 1, 2, 2, 1, 1, 1, 2, 3], dtype=np.int64)
>>> nid_sequence = np.array([0, 0, 1, 2, 2, 1, 1], dtype=np.int64)
>>> seed = 0
>>> (aids, nids, aids1, nids1, all_aids, all_nids) = get_toy_annots(num_annots, num_names, initial_aids, initial_nids, nid_sequence, seed)
>>> result = ('(aids, nids, aids1, nids1, all_aids, all_nids) = %s' % (ut.repr2((aids, nids, aids1, nids1, all_aids, all_nids), nl=1),))
>>> print(result)
"""
import vtool as vt
if num_names is None:
num_names = num_annots
print('Generating toy data with num_annots=%r' % (num_annots,))
if initial_aids is None:
assert initial_nids is None
first_step = True
initial_aids = []
initial_nids = []
else:
first_step = False
assert initial_nids is not None
aids = np.arange(len(initial_aids), num_annots + len(initial_aids))
rng = vt.ensure_rng(seed)
if nid_sequence is None:
nids = rng.randint(0, num_names, num_annots)
else:
unused_from_sequence = max(len(nid_sequence) - len(initial_aids), 0)
if unused_from_sequence == 0:
nids = rng.randint(0, num_names, num_annots)
elif unused_from_sequence > 0 and unused_from_sequence < num_annots:
num_remain = num_annots - unused_from_sequence
nids = np.append(nid_sequence[-unused_from_sequence:], rng.randint(0, num_names, num_remain))
else:
nids = nid_sequence[-unused_from_sequence]
nids = np.array(ut.take(nid_sequence, range(len(initial_aids), len(initial_aids) + num_annots)))
if first_step:
aids1 = aids
nids1 = nids
else:
aids1 = initial_aids
nids1 = initial_nids
all_nids = np.append(initial_nids, nids)
all_aids = np.append(initial_aids, aids)
import utool
with utool.embed_on_exception_context:
ut.assert_eq(len(aids), len(nids), 'len new')
ut.assert_eq(len(aids1), len(nids1), 'len comp')
ut.assert_eq(len(all_aids), len(all_nids), 'len all')
return aids, nids, aids1, nids1, all_aids, all_nids