def setup_incremental_test(ibs_gt, clear_names=True, aid_order='shuffle'):
r"""
CommandLine:
python -m ibeis.algo.hots.automated_helpers --test-setup_incremental_test:0
python dev.py -t custom --cfg codename:vsone_unnorm --db PZ_MTEST --allgt --vf --va
python dev.py -t custom --cfg codename:vsone_unnorm --db PZ_MTEST --allgt --vf --va --index 0 4 8 --verbose
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_helpers import * # NOQA
>>> import ibeis # NOQA
>>> ibs_gt = ibeis.opendb('PZ_MTEST')
>>> ibs2, aid_list1, aid1_to_aid2 = setup_incremental_test(ibs_gt)
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_helpers import * # NOQA
>>> import ibeis # NOQA
>>> ibs_gt = ibeis.opendb('GZ_ALL')
>>> ibs2, aid_list1, aid1_to_aid2 = setup_incremental_test(ibs_gt)
"""
print('\n\n---- SETUP INCREMENTAL TEST ---\n\n')
# Take a known dataase
# Create an empty database to test in
ONLY_GT = True
if ONLY_GT:
# use only annotations that will have matches in test
aid_list1_ = ibs_gt.get_aids_with_groundtruth()
else:
# use every annotation in test
aid_list1_ = ibs_gt.get_valid_aids()
if ut.get_argflag('--gzdev'):
# Use a custom selection of gzall
from ibeis.algo.hots import devcases
assert ibs_gt.get_dbname() == 'GZ_ALL', 'not gzall'
vuuid_list, ignore_vuuids = devcases.get_gzall_small_test()
# TODO; include all names of these annots too
aid_list = ibs_gt.get_annot_aids_from_visual_uuid(vuuid_list)
ignore_aid_list = ibs_gt.get_annot_aids_from_visual_uuid(ignore_vuuids)
ignore_nid_list = ibs_gt.get_annot_nids(ignore_aid_list)
ut.assert_all_not_None(aid_list)
other_aids = ut.flatten(ibs_gt.get_annot_groundtruth(aid_list))
aid_list.extend(other_aids)
aid_list = sorted(set(aid_list))
nid_list = ibs_gt.get_annot_nids(aid_list)
isinvalid_list = [nid in ignore_nid_list for nid in nid_list]
print('Filtering %r annots specified to ignore' %
(sum(isinvalid_list), ))
aid_list = ut.filterfalse_items(aid_list, isinvalid_list)
#ut.embed()
aid_list1_ = aid_list
#ut.embed()
# Add aids in a random order
VALID_ORDERS = ['shuffle', 'stagger', 'same']
#AID_ORDER = 'shuffle'
aid_order = ut.get_argval('--aid-order', default=aid_order)
assert VALID_ORDERS.index(aid_order) > -1
if aid_order == 'shuffle':
aid_list1 = ut.deterministic_shuffle(aid_list1_[:])
elif aid_order == 'stagger':
from six.moves import zip_longest, filter
aid_groups, unique_nid_list = ibs_gt.group_annots_by_name(aid_list1_)
def stagger_group(list_):
return ut.filter_Nones(ut.iflatten(zip_longest(*list_)))
aid_multiton_group = list(
filter(lambda aids: len(aids) > 1, aid_groups))
aid_list1 = stagger_group(aid_multiton_group)
pass
elif aid_order == 'same':
aid_list1 = aid_list1_
# If reset is true the test database is started completely from scratch
reset = ut.get_argflag('--reset')
aid1_to_aid2 = {} # annotation mapping
ibs2 = make_incremental_test_database(ibs_gt, aid_list1, reset)
# Preadd all annotatinos to the test database
aids_chunk1 = aid_list1
aid_list2 = add_annot_chunk(ibs_gt, ibs2, aids_chunk1, aid1_to_aid2)
#ut.embed()
# Assert annotation visual uuids are in agreement
if ut.DEBUG2:
annot_testdb_consistency_checks(ibs_gt, ibs2, aid_list1, aid_list2)
# Remove names and exemplar information from test database
if clear_names:
ensure_testdb_clean_data(ibs_gt, ibs2, aid_list1, aid_list2)
# Preprocess features before testing
ibs2.ensure_annotation_data(aid_list2, featweights=True)
return ibs2, aid_list1, aid1_to_aid2
def setup_incremental_test(ibs_gt, clear_names=True, aid_order='shuffle'):
r"""
CommandLine:
python -m ibeis.algo.hots.automated_helpers --test-setup_incremental_test:0
python dev.py -t custom --cfg codename:vsone_unnorm --db PZ_MTEST --allgt --vf --va
python dev.py -t custom --cfg codename:vsone_unnorm --db PZ_MTEST --allgt --vf --va --index 0 4 8 --verbose
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_helpers import * # NOQA
>>> import ibeis # NOQA
>>> ibs_gt = ibeis.opendb('PZ_MTEST')
>>> ibs2, aid_list1, aid1_to_aid2 = setup_incremental_test(ibs_gt)
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.automated_helpers import * # NOQA
>>> import ibeis # NOQA
>>> ibs_gt = ibeis.opendb('GZ_ALL')
>>> ibs2, aid_list1, aid1_to_aid2 = setup_incremental_test(ibs_gt)
"""
print('\n\n---- SETUP INCREMENTAL TEST ---\n\n')
# Take a known dataase
# Create an empty database to test in
ONLY_GT = True
if ONLY_GT:
# use only annotations that will have matches in test
aid_list1_ = ibs_gt.get_aids_with_groundtruth()
else:
# use every annotation in test
aid_list1_ = ibs_gt.get_valid_aids()
if ut.get_argflag('--gzdev'):
# Use a custom selection of gzall
from ibeis.algo.hots import devcases
assert ibs_gt.get_dbname() == 'GZ_ALL', 'not gzall'
vuuid_list, ignore_vuuids = devcases.get_gzall_small_test()
# TODO; include all names of these annots too
aid_list = ibs_gt.get_annot_aids_from_visual_uuid(vuuid_list)
ignore_aid_list = ibs_gt.get_annot_aids_from_visual_uuid(ignore_vuuids)
ignore_nid_list = ibs_gt.get_annot_nids(ignore_aid_list)
ut.assert_all_not_None(aid_list)
other_aids = ut.flatten(ibs_gt.get_annot_groundtruth(aid_list))
aid_list.extend(other_aids)
aid_list = sorted(set(aid_list))
nid_list = ibs_gt.get_annot_nids(aid_list)
isinvalid_list = [nid in ignore_nid_list for nid in nid_list]
print('Filtering %r annots specified to ignore' % (sum(isinvalid_list),))
aid_list = ut.filterfalse_items(aid_list, isinvalid_list)
#ut.embed()
aid_list1_ = aid_list
#ut.embed()
# Add aids in a random order
VALID_ORDERS = ['shuffle', 'stagger', 'same']
#AID_ORDER = 'shuffle'
aid_order = ut.get_argval('--aid-order', default=aid_order)
assert VALID_ORDERS.index(aid_order) > -1
if aid_order == 'shuffle':
aid_list1 = ut.deterministic_shuffle(aid_list1_[:])
elif aid_order == 'stagger':
from six.moves import zip_longest, filter
aid_groups, unique_nid_list = ibs_gt.group_annots_by_name(aid_list1_)
def stagger_group(list_):
return ut.filter_Nones(ut.iflatten(zip_longest(*list_)))
aid_multiton_group = list(filter(lambda aids: len(aids) > 1, aid_groups))
aid_list1 = stagger_group(aid_multiton_group)
pass
elif aid_order == 'same':
aid_list1 = aid_list1_
# If reset is true the test database is started completely from scratch
reset = ut.get_argflag('--reset')
aid1_to_aid2 = {} # annotation mapping
ibs2 = make_incremental_test_database(ibs_gt, aid_list1, reset)
# Preadd all annotatinos to the test database
aids_chunk1 = aid_list1
aid_list2 = add_annot_chunk(ibs_gt, ibs2, aids_chunk1, aid1_to_aid2)
#ut.embed()
# Assert annotation visual uuids are in agreement
if ut.DEBUG2:
annot_testdb_consistency_checks(ibs_gt, ibs2, aid_list1, aid_list2)
# Remove names and exemplar information from test database
if clear_names:
ensure_testdb_clean_data(ibs_gt, ibs2, aid_list1, aid_list2)
# Preprocess features before testing
ibs2.ensure_annotation_data(aid_list2, featweights=True)
return ibs2, aid_list1, aid1_to_aid2
def augment_nnindexer_experiment():
"""
References:
http://answers.opencv.org/question/44592/flann-index-training-fails-with-segfault/
CommandLine:
utprof.py -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_MTEST --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --show
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --show
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --no-api-cache --nocache-uuids
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_MTEST --show
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
# RUNS THE SEGFAULTING CASE
python -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
# Debug it
gdb python
run -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
gdb python
run -m wbia.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots._neighbor_experiment import * # NOQA
>>> # execute function
>>> augment_nnindexer_experiment()
>>> # verify results
>>> ut.show_if_requested()
"""
import wbia
# build test data
# ibs = wbia.opendb('PZ_MTEST')
ibs = wbia.opendb(defaultdb='PZ_Master0')
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
addition_stride = 4
max_ceiling = 100
elif ibs.get_dbname() == 'PZ_Master0':
initial = 128
# addition_stride = 64
# addition_stride = 128
addition_stride = 256
max_ceiling = 10000
# max_ceiling = 4000
# max_ceiling = 2000
# max_ceiling = 600
else:
assert False
all_daids = ibs.get_valid_aids(species='zebra_plains')
qreq_ = ibs.new_query_request(all_daids, all_daids)
max_num = min(max_ceiling, len(all_daids))
# Clear Caches
ibs.delete_flann_cachedir()
neighbor_index_cache.clear_memcache()
neighbor_index_cache.clear_uuid_cache(qreq_)
# Setup
all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
# ensure all features are computed
nnindexer_list = []
addition_lbl = 'Addition'
_addition_iter = list(range(initial + 1, max_num, addition_stride))
addition_iter = iter(
ut.ProgressIter(_addition_iter,
lbl=addition_lbl,
freq=1,
autoadjust=False))
time_list_addition = []
# time_list_reindex = []
addition_count_list = []
tmp_cfgstr_list = []
# for _ in range(80):
# next(addition_iter)
try:
memtrack = ut.MemoryTracker(disable=False)
for count in addition_iter:
aid_list_ = all_randomize_daids_[0:count]
# Request an indexer which could be an augmented version of an existing indexer.
with ut.Timer(verbose=False) as t:
memtrack.report('BEFORE AUGMENT')
nnindexer_ = neighbor_index_cache.request_augmented_wbia_nnindexer(
qreq_, aid_list_)
memtrack.report('AFTER AUGMENT')
nnindexer_list.append(nnindexer_)
addition_count_list.append(count)
time_list_addition.append(t.ellapsed)
tmp_cfgstr_list.append(nnindexer_.cfgstr)
logger.info('===============\n\n')
logger.info(ut.repr2(time_list_addition))
logger.info(ut.repr2(list(map(id, nnindexer_list))))
logger.info(ut.repr2(tmp_cfgstr_list))
logger.info(
ut.repr2(list([nnindxer.cfgstr for nnindxer in nnindexer_list])))
IS_SMALL = False
if IS_SMALL:
nnindexer_list = []
reindex_label = 'Reindex'
# go backwards for reindex
_reindex_iter = list(range(initial + 1, max_num,
addition_stride))[::-1]
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_label)
time_list_reindex = []
# time_list_reindex = []
reindex_count_list = []
for count in reindex_iter:
logger.info('\n+===PREDONE====================\n')
# check only a single size for memory leaks
# count = max_num // 16 + ((x % 6) * 1)
# x += 1
aid_list_ = all_randomize_daids_[0:count]
# Call the same code, but force rebuilds
memtrack.report('BEFORE REINDEX')
with ut.Timer(verbose=False) as t:
nnindexer_ = neighbor_index_cache.request_augmented_wbia_nnindexer(
qreq_, aid_list_, force_rebuild=True, memtrack=memtrack)
memtrack.report('AFTER REINDEX')
ibs.print_cachestats_str()
logger.info('[nnindex.MEMCACHE] size(NEIGHBOR_CACHE) = %s' %
(ut.get_object_size_str(
neighbor_index_cache.NEIGHBOR_CACHE.items()), ))
logger.info('[nnindex.MEMCACHE] len(NEIGHBOR_CACHE) = %s' %
(len(neighbor_index_cache.NEIGHBOR_CACHE.items()), ))
logger.info('[nnindex.MEMCACHE] size(UUID_MAP_CACHE) = %s' %
(ut.get_object_size_str(
neighbor_index_cache.UUID_MAP_CACHE), ))
logger.info('totalsize(nnindexer) = ' +
ut.get_object_size_str(nnindexer_))
memtrack.report_type(neighbor_index_cache.NeighborIndex)
ut.print_object_size_tree(nnindexer_, lbl='nnindexer_')
if IS_SMALL:
nnindexer_list.append(nnindexer_)
reindex_count_list.append(count)
time_list_reindex.append(t.ellapsed)
# import cv2
# import matplotlib as mpl
# logger.info(mem_top.mem_top(limit=30, width=120,
# #exclude_refs=[cv2.__dict__, mpl.__dict__]
# ))
logger.info('L___________________\n\n\n')
logger.info(ut.repr2(time_list_reindex))
if IS_SMALL:
logger.info(ut.repr2(list(map(id, nnindexer_list))))
logger.info(
ut.repr2(list([nnindxer.cfgstr
for nnindxer in nnindexer_list])))
except KeyboardInterrupt:
logger.info('\n[train] Caught CRTL+C')
resolution = ''
from six.moves import input
while not (resolution.isdigit()):
logger.info('\n[train] What do you want to do?')
logger.info('[train] 0 - Continue')
logger.info('[train] 1 - Embed')
logger.info('[train] ELSE - Stop network training')
resolution = input('[train] Resolution: ')
resolution = int(resolution)
# We have a resolution
if resolution == 0:
logger.info('resuming training...')
elif resolution == 1:
ut.embed()
import wbia.plottool as pt
next_fnum = iter(range(0, 1)).next # python3 PY3
pt.figure(fnum=next_fnum())
if len(addition_count_list) > 0:
pt.plot2(
addition_count_list,
time_list_addition,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=addition_lbl + ' Time',
)
if len(reindex_count_list) > 0:
pt.plot2(
reindex_count_list,
time_list_reindex,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=reindex_label + ' Time',
)
pt.set_figtitle('Augmented indexer experiment')
pt.legend()
def distinct_colors(N, brightness=.878, randomize=True, hue_range=(0.0, 1.0), cmap_seed=None):
r"""
Args:
N (int):
brightness (float):
Returns:
list: RGB_tuples
CommandLine:
python -m plottool.color_funcs --test-distinct_colors --N 2 --show --hue-range=0.05,.95
python -m plottool.color_funcs --test-distinct_colors --N 3 --show --hue-range=0.05,.95
python -m plottool.color_funcs --test-distinct_colors --N 4 --show --hue-range=0.05,.95
python -m plottool.color_funcs --test-distinct_colors --N 3 --show --no-randomize
python -m plottool.color_funcs --test-distinct_colors --N 4 --show --no-randomize
python -m plottool.color_funcs --test-distinct_colors --N 20 --show
References:
http://blog.jianhuashao.com/2011/09/generate-n-distinct-colors.html
CommandLine:
python -m plottool.color_funcs --exec-distinct_colors --show
python -m plottool.color_funcs --exec-distinct_colors --show --no-randomize --N 50
python -m plottool.color_funcs --exec-distinct_colors --show --cmap_seed=foobar
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.color_funcs import * # NOQA
>>> # build test data
>>> N = ut.get_argval('--N', int, 2)
>>> randomize = not ut.get_argflag('--no-randomize')
>>> brightness = 0.878
>>> # execute function
>>> cmap_seed = ut.get_argval('--cmap_seed', str, default=None)
>>> hue_range = ut.get_argval('--hue-range', list, default=(0.00, 1.0))
>>> RGB_tuples = distinct_colors(N, brightness, randomize, hue_range, cmap_seed=cmap_seed)
>>> # verify results
>>> assert len(RGB_tuples) == N
>>> result = str(RGB_tuples)
>>> print(result)
>>> ut.quit_if_noshow()
>>> color_list = RGB_tuples
>>> testshow_colors(color_list)
>>> ut.show_if_requested()
"""
# TODO: Add sin wave modulation to the sat and value
#import plottool as pt
if True:
import plottool as pt
# HACK for white figures
remove_yellow = not pt.is_default_dark_bg()
#if not pt.is_default_dark_bg():
# brightness = .8
use_jet = False
if use_jet:
import plottool as pt
cmap = pt.plt.cm.jet
RGB_tuples = list(map(tuple, cmap(np.linspace(0, 1, N))))
elif cmap_seed is not None:
# Randomized map based on a seed
#cmap_ = 'Set1'
#cmap_ = 'Dark2'
choices = [
#'Set1', 'Dark2',
'jet',
#'gist_rainbow',
#'rainbow',
#'gnuplot',
#'Accent'
]
cmap_hack = ut.get_argval('--cmap-hack', type_=str, default=None)
ncolor_hack = ut.get_argval('--ncolor-hack', type_=int, default=None)
if cmap_hack is not None:
choices = [cmap_hack]
if ncolor_hack is not None:
N = ncolor_hack
N_ = N
seed = sum(list(map(ord, ut.hashstr27(cmap_seed))))
rng = np.random.RandomState(seed + 48930)
cmap_str = rng.choice(choices, 1)[0]
#print('cmap_str = %r' % (cmap_str,))
cmap = pt.plt.cm.get_cmap(cmap_str)
#ut.hashstr27(cmap_seed)
#cmap_seed = 0
#pass
jitter = (rng.randn(N) / (rng.randn(100).max() / 2)).clip(-1, 1) * ((1 / (N ** 2)))
range_ = np.linspace(0, 1, N, endpoint=False)
#print('range_ = %r' % (range_,))
range_ = range_ + jitter
#print('range_ = %r' % (range_,))
while not (np.all(range_ >= 0) and np.all(range_ <= 1)):
range_[range_ < 0] = np.abs(range_[range_ < 0] )
range_[range_ > 1] = 2 - range_[range_ > 1]
#print('range_ = %r' % (range_,))
shift = rng.rand()
range_ = (range_ + shift) % 1
#print('jitter = %r' % (jitter,))
#print('shift = %r' % (shift,))
#print('range_ = %r' % (range_,))
if ncolor_hack is not None:
range_ = range_[0:N_]
RGB_tuples = list(map(tuple, cmap(range_)))
else:
sat = brightness
val = brightness
hmin, hmax = hue_range
if remove_yellow:
hue_skips = [(.13, .24)]
else:
hue_skips = []
hue_skip_ranges = [_[1] - _[0] for _ in hue_skips]
total_skip = sum(hue_skip_ranges)
hmax_ = hmax - total_skip
hue_list = np.linspace(hmin, hmax_, N, endpoint=False, dtype=np.float)
# Remove colors (like hard to see yellows) in specified ranges
for skip, range_ in zip(hue_skips, hue_skip_ranges):
hue_list = [hue if hue <= skip[0] else hue + range_ for hue in hue_list]
HSV_tuples = [(hue, sat, val) for hue in hue_list]
RGB_tuples = [colorsys.hsv_to_rgb(*x) for x in HSV_tuples]
if randomize:
ut.deterministic_shuffle(RGB_tuples)
return RGB_tuples
def augment_nnindexer_experiment():
"""
References:
http://answers.opencv.org/question/44592/flann-index-training-fails-with-segfault/
CommandLine:
utprof.py -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_MTEST --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --show
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --show
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6 --nosave-flann --no-api-cache --nocache-uuids
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_MTEST --show
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
# RUNS THE SEGFAULTING CASE
python -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
# Debug it
gdb python
run -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --show
gdb python
run -m ibeis.algo.hots._neighbor_experiment --test-augment_nnindexer_experiment --db PZ_Master0 --diskshow --adjust=.1 --save "augment_experiment_{db}.png" --dpath='.' --dpi=180 --figsize=9,6
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots._neighbor_experiment import * # NOQA
>>> # execute function
>>> augment_nnindexer_experiment()
>>> # verify results
>>> ut.show_if_requested()
"""
import ibeis
# build test data
#ibs = ibeis.opendb('PZ_MTEST')
ibs = ibeis.opendb(defaultdb='PZ_Master0')
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
addition_stride = 4
max_ceiling = 100
elif ibs.get_dbname() == 'PZ_Master0':
initial = 128
#addition_stride = 64
#addition_stride = 128
addition_stride = 256
max_ceiling = 10000
#max_ceiling = 4000
#max_ceiling = 2000
#max_ceiling = 600
else:
assert False
all_daids = ibs.get_valid_aids(species='zebra_plains')
qreq_ = ibs.new_query_request(all_daids, all_daids)
max_num = min(max_ceiling, len(all_daids))
# Clear Caches
ibs.delete_flann_cachedir()
neighbor_index_cache.clear_memcache()
neighbor_index_cache.clear_uuid_cache(qreq_)
# Setup
all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
# ensure all features are computed
#ibs.get_annot_vecs(all_randomize_daids_, ensure=True)
#ibs.get_annot_fgweights(all_randomize_daids_, ensure=True)
nnindexer_list = []
addition_lbl = 'Addition'
_addition_iter = list(range(initial + 1, max_num, addition_stride))
addition_iter = iter(ut.ProgressIter(_addition_iter, lbl=addition_lbl,
freq=1, autoadjust=False))
time_list_addition = []
#time_list_reindex = []
addition_count_list = []
tmp_cfgstr_list = []
#for _ in range(80):
# next(addition_iter)
try:
memtrack = ut.MemoryTracker(disable=False)
for count in addition_iter:
aid_list_ = all_randomize_daids_[0:count]
# Request an indexer which could be an augmented version of an existing indexer.
with ut.Timer(verbose=False) as t:
memtrack.report('BEFORE AUGMENT')
nnindexer_ = neighbor_index_cache.request_augmented_ibeis_nnindexer(qreq_, aid_list_)
memtrack.report('AFTER AUGMENT')
nnindexer_list.append(nnindexer_)
addition_count_list.append(count)
time_list_addition.append(t.ellapsed)
tmp_cfgstr_list.append(nnindexer_.cfgstr)
print('===============\n\n')
print(ut.list_str(time_list_addition))
print(ut.list_str(list(map(id, nnindexer_list))))
print(ut.list_str(tmp_cfgstr_list))
print(ut.list_str(list([nnindxer.cfgstr for nnindxer in nnindexer_list])))
IS_SMALL = False
if IS_SMALL:
nnindexer_list = []
reindex_label = 'Reindex'
# go backwards for reindex
_reindex_iter = list(range(initial + 1, max_num, addition_stride))[::-1]
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_label)
time_list_reindex = []
#time_list_reindex = []
reindex_count_list = []
for count in reindex_iter:
print('\n+===PREDONE====================\n')
# check only a single size for memory leaks
#count = max_num // 16 + ((x % 6) * 1)
#x += 1
aid_list_ = all_randomize_daids_[0:count]
# Call the same code, but force rebuilds
memtrack.report('BEFORE REINDEX')
with ut.Timer(verbose=False) as t:
nnindexer_ = neighbor_index_cache.request_augmented_ibeis_nnindexer(
qreq_, aid_list_, force_rebuild=True, memtrack=memtrack)
memtrack.report('AFTER REINDEX')
ibs.print_cachestats_str()
print('[nnindex.MEMCACHE] size(NEIGHBOR_CACHE) = %s' % (
ut.get_object_size_str(neighbor_index_cache.NEIGHBOR_CACHE.items()),))
print('[nnindex.MEMCACHE] len(NEIGHBOR_CACHE) = %s' % (
len(neighbor_index_cache.NEIGHBOR_CACHE.items()),))
print('[nnindex.MEMCACHE] size(UUID_MAP_CACHE) = %s' % (
ut.get_object_size_str(neighbor_index_cache.UUID_MAP_CACHE),))
print('totalsize(nnindexer) = ' + ut.get_object_size_str(nnindexer_))
memtrack.report_type(neighbor_index_cache.NeighborIndex)
ut.print_object_size_tree(nnindexer_, lbl='nnindexer_')
if IS_SMALL:
nnindexer_list.append(nnindexer_)
reindex_count_list.append(count)
time_list_reindex.append(t.ellapsed)
#import cv2
#import matplotlib as mpl
#print(mem_top.mem_top(limit=30, width=120,
# #exclude_refs=[cv2.__dict__, mpl.__dict__]
# ))
print('L___________________\n\n\n')
print(ut.list_str(time_list_reindex))
if IS_SMALL:
print(ut.list_str(list(map(id, nnindexer_list))))
print(ut.list_str(list([nnindxer.cfgstr for nnindxer in nnindexer_list])))
except KeyboardInterrupt:
print('\n[train] Caught CRTL+C')
resolution = ''
from six.moves import input
while not (resolution.isdigit()):
print('\n[train] What do you want to do?')
print('[train] 0 - Continue')
print('[train] 1 - Embed')
print('[train] ELSE - Stop network training')
resolution = input('[train] Resolution: ')
resolution = int(resolution)
# We have a resolution
if resolution == 0:
print('resuming training...')
elif resolution == 1:
ut.embed()
import plottool as pt
next_fnum = iter(range(0, 1)).next # python3 PY3
pt.figure(fnum=next_fnum())
if len(addition_count_list) > 0:
pt.plot2(addition_count_list, time_list_addition, marker='-o', equal_aspect=False,
x_label='num_annotations', label=addition_lbl + ' Time')
if len(reindex_count_list) > 0:
pt.plot2(reindex_count_list, time_list_reindex, marker='-o', equal_aspect=False,
x_label='num_annotations', label=reindex_label + ' Time')
pt.set_figtitle('Augmented indexer experiment')
pt.legend()
def distinct_colors(N,
brightness=0.878,
randomize=True,
hue_range=(0.0, 1.0),
cmap_seed=None):
r"""
Args:
N (int):
brightness (float):
Returns:
list: RGB_tuples
CommandLine:
python -m wbia.plottool.color_funcs --test-distinct_colors --N 2 --show --hue-range=0.05,.95
python -m wbia.plottool.color_funcs --test-distinct_colors --N 3 --show --hue-range=0.05,.95
python -m wbia.plottool.color_funcs --test-distinct_colors --N 4 --show --hue-range=0.05,.95
python -m wbia.plottool.color_funcs --test-distinct_colors --N 3 --show --no-randomize
python -m wbia.plottool.color_funcs --test-distinct_colors --N 4 --show --no-randomize
python -m wbia.plottool.color_funcs --test-distinct_colors --N 6 --show --no-randomize
python -m wbia.plottool.color_funcs --test-distinct_colors --N 20 --show
References:
http://blog.jianhuashao.com/2011/09/generate-n-distinct-colors.html
CommandLine:
python -m wbia.plottool.color_funcs --exec-distinct_colors --show
python -m wbia.plottool.color_funcs --exec-distinct_colors --show --no-randomize --N 50
python -m wbia.plottool.color_funcs --exec-distinct_colors --show --cmap_seed=foobar
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.plottool.color_funcs import * # NOQA
>>> # build test data
>>> N = ut.get_argval('--N', int, 2)
>>> randomize = not ut.get_argflag('--no-randomize')
>>> brightness = 0.878
>>> # execute function
>>> cmap_seed = ut.get_argval('--cmap_seed', str, default=None)
>>> hue_range = ut.get_argval('--hue-range', list, default=(0.00, 1.0))
>>> RGB_tuples = distinct_colors(N, brightness, randomize, hue_range, cmap_seed=cmap_seed)
>>> # verify results
>>> assert len(RGB_tuples) == N
>>> result = str(RGB_tuples)
>>> print(result)
>>> ut.quit_if_noshow()
>>> color_list = RGB_tuples
>>> testshow_colors(color_list)
>>> import wbia.plottool as pt
>>> pt.show_if_requested()
"""
# TODO: Add sin wave modulation to the sat and value
# import wbia.plottool as pt
if True:
import wbia.plottool as pt
# HACK for white figures
remove_yellow = not pt.is_default_dark_bg()
# if not pt.is_default_dark_bg():
# brightness = .8
use_jet = False
if use_jet:
import wbia.plottool as pt
cmap = pt.plt.cm.jet
RGB_tuples = list(map(tuple, cmap(np.linspace(0, 1, N))))
elif cmap_seed is not None:
# Randomized map based on a seed
# cmap_ = 'Set1'
# cmap_ = 'Dark2'
choices = [
# 'Set1', 'Dark2',
'jet',
# 'gist_rainbow',
# 'rainbow',
# 'gnuplot',
# 'Accent'
]
cmap_hack = ut.get_argval('--cmap-hack', type_=str, default=None)
ncolor_hack = ut.get_argval('--ncolor-hack', type_=int, default=None)
if cmap_hack is not None:
choices = [cmap_hack]
if ncolor_hack is not None:
N = ncolor_hack
N_ = N
seed = sum(list(map(ord, ut.hashstr27(cmap_seed))))
rng = np.random.RandomState(seed + 48930)
cmap_str = rng.choice(choices, 1)[0]
# print('cmap_str = %r' % (cmap_str,))
cmap = pt.plt.cm.get_cmap(cmap_str)
# ut.hashstr27(cmap_seed)
# cmap_seed = 0
# pass
jitter = (rng.randn(N) /
(rng.randn(100).max() / 2)).clip(-1, 1) * ((1 / (N**2)))
range_ = np.linspace(0, 1, N, endpoint=False)
# print('range_ = %r' % (range_,))
range_ = range_ + jitter
# print('range_ = %r' % (range_,))
while not (np.all(range_ >= 0) and np.all(range_ <= 1)):
range_[range_ < 0] = np.abs(range_[range_ < 0])
range_[range_ > 1] = 2 - range_[range_ > 1]
# print('range_ = %r' % (range_,))
shift = rng.rand()
range_ = (range_ + shift) % 1
# print('jitter = %r' % (jitter,))
# print('shift = %r' % (shift,))
# print('range_ = %r' % (range_,))
if ncolor_hack is not None:
range_ = range_[0:N_]
RGB_tuples = list(map(tuple, cmap(range_)))
else:
sat = brightness
val = brightness
hmin, hmax = hue_range
if remove_yellow:
hue_skips = [(0.13, 0.24)]
else:
hue_skips = []
hue_skip_ranges = [_[1] - _[0] for _ in hue_skips]
total_skip = sum(hue_skip_ranges)
hmax_ = hmax - total_skip
hue_list = np.linspace(hmin, hmax_, N, endpoint=False, dtype=np.float)
# Remove colors (like hard to see yellows) in specified ranges
for skip, range_ in zip(hue_skips, hue_skip_ranges):
hue_list = [
hue if hue <= skip[0] else hue + range_ for hue in hue_list
]
HSV_tuples = [(hue, sat, val) for hue in hue_list]
RGB_tuples = [colorsys.hsv_to_rgb(*x) for x in HSV_tuples]
if randomize:
ut.deterministic_shuffle(RGB_tuples)
return RGB_tuples