def sanity_checks(offset_list, Y_list, query_annots, ibs):
nfeat_list = np.diff(offset_list)
for Y, nfeat in ut.ProgIter(zip(Y_list, nfeat_list), 'checking'):
assert nfeat == sum(ut.lmap(len, Y.fxs_list))
if False:
# Visualize queries
# Look at the standard query images here
# http://www.robots.ox.ac.uk:5000/~vgg/publications/2007/Philbin07/philbin07.pdf
from wbia.viz import viz_chip
import wbia.plottool as pt
pt.qt4ensure()
fnum = 1
pnum_ = pt.make_pnum_nextgen(len(query_annots.aids) // 5, 5)
for aid in ut.ProgIter(query_annots.aids):
pnum = pnum_()
viz_chip.show_chip(
ibs,
aid,
in_image=True,
annote=False,
notitle=True,
draw_lbls=False,
fnum=fnum,
pnum=pnum,
)
def update_registry(drive):
print('Updating registered files in %r' % (drive, ))
# Update existing files
fpath_exists_list = list(
map(exists,
ut.ProgIter(drive.fpath_list, 'checkexist fpath', freq=1000)))
dpath_exists_list = list(
map(exists,
ut.ProgIter(drive.dpath_list, 'checkexist dpath', freq=1000)))
if all(fpath_exists_list):
print('No change in file structure')
else:
print('%d/%d files no longer exist' %
(len(drive.fpath_list) - sum(fpath_exists_list),
len(drive.fpath_list)))
removed_fpaths = ut.compress(drive.fpath_list,
ut.not_list(fpath_exists_list))
print('removed_fpaths = %s' % (ut.repr2(removed_fpaths), ))
if all(dpath_exists_list):
print('No change in dpath structure')
else:
print('%d/%d dirs no longer exist' %
(len(drive.dpath_list) - sum(dpath_exists_list),
len(drive.dpath_list)))
removed_dpaths = ut.compress(drive.dpath_list,
ut.not_list(dpath_exists_list))
print('removed_dpaths = %s' % (ut.repr2(removed_dpaths), ))
drive.fpath_list = ut.compress(drive.fpath_list, fpath_exists_list)
drive.dpath_list = ut.compress(drive.dpath_list, dpath_exists_list)
drive.cache.save('fpath_list', drive.fpath_list)
drive.cache.save('dpath_list', drive.dpath_list)
def dpath_similarity(index, dpath1, dpath2):
d1 = index[dpath1]
d2 = index[dpath2]
set1 = {f.hashid for f in ut.ProgIter(d1.files)}
set2 = {f.hashid for f in ut.ProgIter(d2.files)}
# n_isect = len(set1.intersection(set2))
size1, size2 = map(len, (set1, set2))
# minsize = min(size1, size2)
# sim_measures = (n_isect, n_isect / minsize)
return ut.set_overlaps(set1, set2)
def _enriched_pairwise_matches(extr, edges, prog_hook=None):
"""
Adds extra domain specific local and global properties that the match
object (feature corresopndences) doesnt directly provide.
Example:
>>> # xdoctest: +REQUIRES(module:wbia_cnn)
>>> # ENABLE_DOCTEST
>>> from wbia.algo.verif.pairfeat import * # NOQA
>>> import wbia
>>> ibs = wbia.opendb('testdb1')
>>> match_config = {
>>> 'K': 1, 'Knorm': 3, 'affine_invariance': True,
>>> 'augment_orientation': True, 'checks': 20, 'ratio_thresh': 0.8,
>>> 'refine_method': 'homog', 'sv_on': True, 'sver_xy_thresh': 0.01,
>>> 'symmetric': True, 'weight': 'fgweights'
>>> }
>>> global_keys = ['gps', 'qual', 'time']
>>> extr = PairwiseFeatureExtractor(ibs, match_config=match_config,
>>> global_keys=global_keys)
>>> assert extr.global_keys == global_keys
>>> edges = [(1, 2), (2, 3)]
>>> prog_hook = None
>>> match_list = extr._enriched_pairwise_matches(edges)
>>> match1, match2 = match_list
>>> assert match1.annot2 is match2.annot1
>>> assert match1.annot1 is not match2.annot2
>>> print('match1.global_measures = {!r}'.format(match1.global_measures))
>>> assert len(match1.global_measures) == 3, 'global measures'
"""
# logger.info('extr.global_keys = {!r}'.format(extr.global_keys))
if extr.global_keys is None:
raise ValueError('specify global keys')
# global_keys = ['view_int', 'qual', 'gps', 'time']
# global_keys = ['view', 'qual', 'gps', 'time']
matches = extr._exec_pairwise_match(edges, prog_hook=prog_hook)
if extr.need_lnbnn:
extr._enrich_matches_lnbnn(matches, inplace=True)
if extr.verbose:
logger.info('[extr] enriching match attributes')
# Ensure matches know about relavent metadata
for match in matches:
vt.matching.ensure_metadata_normxy(match.annot1)
vt.matching.ensure_metadata_normxy(match.annot2)
for match in ut.ProgIter(matches, label='setup globals'):
match.add_global_measures(extr.global_keys)
for match in ut.ProgIter(matches, label='setup locals'):
match.add_local_measures()
return matches
def find_pos_redun_candidate_edges(infr, k=None, verbose=False):
r"""
Searches for augmenting edges that would make PCCs k-positive redundant
Doctest:
>>> from wbia.algo.graph.mixin_matching import * # NOQA
>>> from wbia.algo.graph import demo
>>> infr = demo.demodata_infr(ccs=[(1, 2, 3, 4, 5), (7, 8, 9, 10)])
>>> infr.add_feedback((2, 5), 'match')
>>> infr.add_feedback((1, 5), 'notcomp')
>>> infr.params['redun.pos'] = 2
>>> candidate_edges = list(infr.find_pos_redun_candidate_edges())
>>> result = ('candidate_edges = ' + ut.repr2(candidate_edges))
>>> print(result)
candidate_edges = []
"""
# Add random edges between exisiting non-redundant PCCs
if k is None:
k = infr.params['redun.pos']
# infr.find_non_pos_redundant_pccs(k=k, relax=True)
pcc_gen = list(infr.positive_components())
prog = ut.ProgIter(pcc_gen, enabled=verbose, freq=1, adjust=False)
for pcc in prog:
if not infr.is_pos_redundant(pcc, k=k, relax=True, assume_connected=True):
for edge in infr.find_pos_augment_edges(pcc, k=k):
print()
yield nxu.e_(*edge)
def _load_singles(qreq_):
# Find existing cached chip matches
# Try loading as many as possible
fpath_list = qreq_.get_chipmatch_fpaths(qreq_.qaids)
exists_flags = [exists(fpath) for fpath in fpath_list]
qaids_hit = ut.compress(qreq_.qaids, exists_flags)
fpaths_hit = ut.compress(fpath_list, exists_flags)
# First, try a fast reload assuming no errors
fpath_iter = ut.ProgIter(
fpaths_hit,
length=len(fpaths_hit),
enabled=len(fpaths_hit) > 1,
label='loading cache hits',
adjust=True,
freq=1,
)
try:
qaid_to_hit = {
qaid: chip_match.ChipMatch.load_from_fpath(fpath, verbose=False)
for qaid, fpath in zip(qaids_hit, fpath_iter)
}
except chip_match.NeedRecomputeError as ex:
# Fallback to a slow reload
ut.printex(ex, 'Some cached results need to recompute', iswarning=True)
qaid_to_hit = _load_singles_fallback(fpaths_hit)
return qaid_to_hit
def render_vocab(vocab):
"""
Renders the average patch of each word.
This is a quick visualization of the entire vocabulary.
CommandLine:
python -m wbia.algo.smk.vocab_indexer render_vocab --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.smk.vocab_indexer import * # NOQA
>>> vocab = testdata_vocab('PZ_MTEST', num_words=64)
>>> all_words = vocab.render_vocab()
>>> ut.quit_if_noshow()
>>> import wbia.plottool as pt
>>> pt.qt4ensure()
>>> pt.imshow(all_words)
>>> ut.show_if_requested()
"""
import wbia.plottool as pt
wx_list = list(range(len(vocab)))
# wx_list = ut.strided_sample(wx_list, 64)
wx_list = ut.strided_sample(wx_list, 64)
word_patch_list = []
for wx in ut.ProgIter(wx_list, bs=True, lbl='building patches'):
word = vocab.wx_to_word[wx]
word_patch = vt.inverted_sift_patch(word, 64)
word_patch = pt.render_sift_on_patch(word_patch, word)
word_patch_list.append(word_patch)
all_words = vt.stack_square_images(word_patch_list)
return all_words
def find_connecting_edges(infr):
"""
Searches for a small set of edges, which if reviewed as positive would
ensure that each PCC is k-connected. Note that in somes cases this is
not possible
"""
label = 'name_label'
node_to_label = infr.get_node_attrs(label)
label_to_nodes = ut.group_items(node_to_label.keys(), node_to_label.values())
# k = infr.params['redun.pos']
k = 1
new_edges = []
prog = ut.ProgIter(
list(label_to_nodes.keys()),
label='finding connecting edges',
enabled=infr.verbose > 0,
)
for nid in prog:
nodes = set(label_to_nodes[nid])
G = infr.pos_graph.subgraph(nodes, dynamic=False)
impossible = nxu.edges_inside(infr.neg_graph, nodes)
impossible |= nxu.edges_inside(infr.incomp_graph, nodes)
candidates = set(nx.complement(G).edges())
candidates.difference_update(impossible)
aug_edges = nxu.k_edge_augmentation(G, k=k, avail=candidates)
new_edges += aug_edges
prog.ensure_newline()
return new_edges
def get_patches(invassign, wx):
ax_list = invassign.wx2_axs[wx]
fx_list = invassign.wx2_fxs[wx]
config = invassign.fstack.config
ibs = invassign.fstack.ibs
unique_axs, groupxs = vt.group_indices(ax_list)
fxs_groups = vt.apply_grouping(fx_list, groupxs)
unique_aids = ut.take(invassign.fstack.ax2_aid, unique_axs)
all_kpts_list = ibs.depc.d.get_feat_kpts(unique_aids, config=config)
sub_kpts_list = vt.ziptake(all_kpts_list, fxs_groups, axis=0)
chip_list = ibs.depc_annot.d.get_chips_img(unique_aids)
# convert to approprate colorspace
#if colorspace is not None:
# chip_list = vt.convert_image_list_colorspace(chip_list, colorspace)
# ut.print_object_size(chip_list, 'chip_list')
patch_size = 64
grouped_patches_list = [
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
for chip, kpts in ut.ProgIter(zip(chip_list, sub_kpts_list),
nTotal=len(unique_aids),
lbl='warping patches')
]
# Make it correspond with original fx_list and ax_list
word_patches = vt.invert_apply_grouping(grouped_patches_list, groupxs)
return word_patches
def preproc_has_tips(depc, aid_list, config=None):
r"""
HACK TO FIND ONLY ANNOTS THAT HAVE TIPS
Args:
depc (DependencyCache):
aid_list (list): list of annotation rowids
config (dict): (default = {})
Yields:
tuple: (np.ndarray, np.ndarray, np.ndarray)
CommandLine:
python -m ibeis_flukematch.plugin --exec-preproc_has_tips --db testdb1
python -m ibeis_flukematch.plugin --exec-preproc_has_tips --dbdir /home/zach/data/IBEIS/humpbacks --no-cnn
python -m ibeis_flukematch.plugin --exec-preproc_has_tips --dbdir /home/zach/data/IBEIS/humpbacks --no-cnn --clear-all-depcache
python -m ibeis_flukematch.plugin --exec-preproc_has_tips --db humpbacks --no-cnn
python -m ibeis_flukematch.plugin --exec-preproc_has_tips --db humpbacks --no-cnn --clear-all-depcache
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_flukematch.plugin import * # NOQA
>>> ibs = ibeis.opendb(defaultdb='humpbacks')
>>> aid_list = ibs.get_valid_aids()
>>> config = {}
>>> propgen = preproc_has_tips(ibs.depc, aid_list, config)
>>> result = list(propgen)
>>> hasnotch_list = ut.take_column(result, 0)
>>> num_with = sum(hasnotch_list)
>>> valid_aids = ut.compress(aid_list, hasnotch_list)
>>> ibs.append_annot_case_tags(valid_aids, ['hasnotch'] * len(valid_aids))
>>> print(ibs.get_annot_info(valid_aids[2], default=True))
>>> print('%r / %r annots have notches' % (num_with, len(aid_list)))
"""
print('Preprocess Has_Notch')
print(config)
config = config.copy()
ibs = depc.controller
fn = join(ibs.get_dbdir(), 'fluke_image_points.pkl')
if not exists(fn):
print('[fluke-module] ERROR: Could not find image points file')
raise NotImplementedError('Could not find image points file')
# this is a dict of img: dict of left/right/notch to the xy-point
img_points_map = ut.load_cPkl(fn)
img_names = ibs.get_annot_image_names(aid_list)
for imgn in ut.ProgIter(img_names, lbl='Checking Has_Notch'):
try:
(
img_points_map[imgn]['notch'],
img_points_map[imgn]['left'],
img_points_map[imgn]['right'],
)
except KeyError:
yield (False, )
else:
yield (True, )
def extract_patches(ibs,
aid_list,
fxs_list=None,
patch_size=None,
colorspace=None):
"""
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.ingest_ibeis import * # NOQA
>>> ut.show_if_requested()
"""
depc = ibs.depc
kpts_list = depc.d.get_feat_kpts(aid_list)
if fxs_list is None:
fxs_list = [slice(None)] * len(kpts_list)
kpts_list_ = ut.ziptake(kpts_list, fxs_list)
chip_list = depc.d.get_chips_img(aid_list)
# convert to approprate colorspace
if colorspace is not None:
chip_list = vt.convert_image_list_colorspace(chip_list, colorspace)
# ut.print_object_size(chip_list, 'chip_list')
patch_size = 64
patches_list = [
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
for chip, kpts in ut.ProgIter(zip(chip_list, kpts_list_),
nTotal=len(aid_list),
lbl='warping patches')
]
return patches_list
def register_files(drive):
print('Loading registered files in %r' % (drive, ))
try:
fpath_list = drive.cache.load('fpath_registry')
dpath_list = drive.cache.load('dpath_registry')
except ut.CacheMissException:
print('Recomputing registry')
fpath_gen_list = []
dpath_gen_list = []
for root, dname_list, fname_list in ut.ProgIter(os.walk(
drive.root_dpath),
'walking',
freq=1000):
# Ignore hidden directories
dname_list[:] = [
d for d in dname_list if not d.startswith('.')
]
fpath_gen_list.append((root, fname_list))
dpath_gen_list.append((root, dname_list))
fpath_list = [
join(root, f) for root, fs in fpath_gen_list for f in fs
]
dpath_list = [
join(root, d) for root, ds in dpath_gen_list for d in ds
]
dpath_list = [drive.root_dpath] + dpath_list
print('Regsitering %d files and %d directories' %
(len(fpath_list), len(dpath_list)))
drive.cache.save('fpath_registry', fpath_list)
drive.cache.save('dpath_registry', dpath_list)
print('Loaded %d files and %d directories' %
(len(fpath_list), len(dpath_list)))
drive.fpath_list_ = fpath_list
drive.dpath_list_ = dpath_list
def oracle_review(sim):
queue_params = {
'pos_diameter': None,
'neg_diameter': None,
}
infr = sim.infr
prev = infr.verbose
infr.verbose = 0
# rng = np.random.RandomState(0)
infr = sim.infr
primary_truth = sim.primary_truth
review_edges = infr.generate_reviews(**queue_params)
max_reviews = 1000
for count, (aid1, aid2) in enumerate(ut.ProgIter(review_edges)):
state = primary_truth.loc[(aid1, aid2)].idxmax()
tags = []
infr.add_feedback(aid1,
aid2,
state,
tags,
apply=True,
rectify=False,
user_id='oracle',
confidence='absolutely_sure')
if count > max_reviews:
break
infr.verbose = prev
sim.results['max_reviews'] = max_reviews
n_clusters, n_inconsistent = infr.relabel_using_reviews(rectify=False)
assert n_inconsistent == 0, 'should not create any inconsistencies'
sim.results['n_user_clusters'] = n_clusters
# infr.apply_review_inference()
curr_decisions = infr.edge_attr_df('decision')
curr_truth = primary_truth.loc[curr_decisions.index].idxmax(axis=1)
n_user_mistakes = curr_decisions != curr_truth
sim.results['n_user_mistakes'] = sum(n_user_mistakes)
gt_clusters = ut.group_pairs(infr.gen_node_attrs('orig_name_label'))
curr_clusters = ut.group_pairs(infr.gen_node_attrs('name_label'))
compare_results = compare_groups(list(gt_clusters.values()),
list(curr_clusters.values()))
sim.results.update(ut.map_vals(len, compare_results))
common_per_num = ut.group_items(compare_results['common'],
map(len, compare_results['common']))
sumafter = 3
greater = [i for i in common_per_num.keys() if i > sumafter]
common_per_num['>%s' % sumafter] = ut.flatten(
ut.take(common_per_num, greater))
ut.delete_keys(common_per_num, greater)
for k, v in common_per_num.items():
sim.results['common@' + str(k)] = len(v)
sim.results['n_names_common'] = len(compare_results['common'])
def get_annotmatch_rowids_between_groups(ibs, aids1_list, aids2_list):
ams_list = []
lbl = 'loading between group am rowids'
for aids1, aids2 in ut.ProgIter(list(zip(aids1_list, aids2_list)),
lbl=lbl):
ams = get_annotmatch_rowids_between(ibs, aids1, aids2)
ams_list.append(ams)
return ams_list
def main():
if True:
import pandas as pd
pd.options.display.max_rows = 1000
pd.options.display.width = 1000
basis = {
#'n_clusters': [10, 100, 1000, 2000][::-1],
#'n_features': [4, 32, 128, 512][::-1],
#'per_cluster': [1, 10, 100, 200][::-1],
'n_clusters': [10, 100, 500][::-1],
'n_features': [32, 128][::-1],
'per_cluster': [1, 10, 20][::-1],
'asint': [True, False],
}
vals = []
for kw in ut.ProgIter(ut.all_dict_combinations(basis),
lbl='gridsearch',
bs=False,
adjust=False,
freq=1):
print('kw = ' + ut.repr2(kw))
exec(ut.execstr_dict(kw))
centers1, new_speed = test_kmeans_plus_plus_speed(fix=True, **kw)
centers2, old_speed = test_kmeans_plus_plus_speed(fix=False, **kw)
import utool
with utool.embed_on_exception_context:
assert np.all(centers1 == centers2), 'new code disagrees'
kw['new_speed'] = new_speed
kw['old_speed'] = old_speed
vals.append(kw)
print('---------')
df = pd.DataFrame.from_dict(vals)
df['percent_change'] = 100 * (df['old_speed'] -
df['new_speed']) / df['old_speed']
df = df.reindex_axis(list(basis.keys()) +
['new_speed', 'old_speed', 'percent_change'],
axis=1)
df['absolute_change'] = (df['old_speed'] - df['new_speed'])
print(df.sort('absolute_change', ascending=False))
#print(df)
print(df['percent_change'][df['absolute_change'] > .1].mean())
#print(df.loc[df['percent_change'].argsort()[::-1]])
else:
new_speed = test_kmeans_plus_plus_speed()
try:
profile.dump_stats('out.lprof')
profile.print_stats(stripzeros=True)
except Exception:
pass
print('new_speed = %r' % (new_speed, ))
def random_case_set():
r"""
Returns:
tuple: (labels, pairwise_feats)
CommandLine:
python -m ibeis.algo.hots.testem random_case_set --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.testem import * # NOQA
>>> (labels, pairwise_feats) = random_case_set()
>>> result = ('(labels, pairwise_feats) = %s' % (ut.repr2((labels, pairwise_feats)),))
>>> print(result)
"""
rng = np.random.RandomState(0)
case_params = dict(num_names=5, rng=rng)
num_annots = 600
test_cases = [
random_test_annot(**case_params)
for _ in ut.ProgIter(range(num_annots), bs=1)
]
pairxs = list(ut.product_nonsame(range(num_annots), range(num_annots)))
import utool
utool.embed()
test_pairs = list(ut.unflat_take(test_cases, pairxs))
cases1 = ut.instancelist(ut.take_column(test_pairs, 0), check=False)
cases2 = ut.instancelist(ut.take_column(test_pairs, 1), check=False)
# FIXME
labels = labels1 = make_test_pairwise_labels2(cases1, cases2) # NOQA
#labels = np.array([make_test_pairwise_labels(case1, case2)
# for case1, case2 in ut.ProgIter(test_pairs, bs=1)])
pairwise_feats_ = [
make_test_pairwise_fetaures(case1, case2, label, rng)
for label, (case1,
case2) in ut.ProgIter(list(zip(labels, test_pairs)), bs=1)
]
pairwise_feats = np.vstack(pairwise_feats_)
print(ut.dict_hist(labels))
return labels, pairwise_feats
def preproc_block_curvature(depc, te_rowids, config):
r"""
Args:
depc (DependencyCache):
aid_list (list): list of annotation rowids
config (dict): (default = {'sizes': [5, 10, 15, 20]})
Yields:
list: [np.ndarray]
CommandLine:
python -m ibeis_flukematch.plugin --exec-preproc_block_curvature --dbdir /home/zach/data/IBEIS/humpbacks --no-cnn
python -m ibeis_flukematch.plugin --exec-preproc_block_curvature --db humpbacks --no-cnn
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_flukematch.plugin import * # NOQA
>>> ibs = ibeis.opendb(defaultdb='humpbacks')
>>> all_aids = ibs.get_valid_aids()
>>> isvalid = ibs.depc.get('Has_Notch', all_aids, 'flag', _debug=True)
>>> aid_list = ut.compress(all_aids, isvalid)[0:4]
>>> print('\n!!![test] aid_list = %r' % (aid_list,))
>>> depc = ibs.depc
>>> config = {'sizes': [5, 10, 15, 20]}
>>> te_rowids = depc.get_rowids('Trailing_Edge', aid_list, config)
>>> print('te_rowids = %r' % (te_rowids,))
>>> propgen = preproc_block_curvature(depc, te_rowids, config)
>>> curve_arr_list = list(propgen)
>>> result = ut.depth_profile(curve_arr_list)
>>> print(result)
"""
print('Preprocess Block_Curvature')
print(config)
ibs = depc.controller
# NOTE: Need to use get_native_property because the take the type
# of the parent (trailing ege) ids, not the root (annot) ids.
# get the trailing edges
# NOTE: Can specify a single column, so unpacking is done automatically
tedges = ibs.depc.get_native_property('Trailing_Edge', te_rowids, 'edge')
# FIXME: CONFIG
sizes = list(
range(config['csize_min'], config['csize_max'] + 1,
config['csize_step']))
sizes = map(lambda x: float(x) / 100, sizes)
# call flukematch.block_integral_curvatures_cpp
progiter = ut.ProgIter(tedges, lbl='compute Block_Curvature')
for tedge in progiter:
if tedge is None:
yield None
else:
curve_arr = block_integral_curvatures_cpp(sizes, tedge)
yield (curve_arr, )
def get_annotmatch_rowids_in_cliques(ibs, aids_list):
# Equivalent call:
#ibs.get_annotmatch_rowids_between_groups(ibs, aids_list, aids_list)
import itertools
ams_list = [
ibs.get_annotmatch_rowid_from_undirected_superkey(*zip(
*itertools.combinations(aids, 2)))
for aids in ut.ProgIter(aids_list, lbl='loading clique am rowids')
]
ams_list = [[] if ams is None else ut.filter_Nones(ams)
for ams in ams_list]
return ams_list
def _ratio_thresh(y_true, match_list):
# Try and find optional ratio threshold
auc_list = []
for cfgdict in ut.ProgIter(grid, lbl='gridsearch'):
y_score = [
match.fs.compress(match.ratio_test_flags(cfgdict)).sum()
for match in match_list
]
auc = sklearn.metrics.roc_auc_score(y_true, y_score)
auc_list.append(auc)
auc_list = np.array(auc_list)
return auc_list
def find_duplicates(index):
# fpaths = list(index.files.keys())
files = list(index.files.values())
print('Grouping {} files'.format(len(files)))
grouped = ut.group_items(files, [f.nbytes for f in files])
print('Found {} groups'.format(len(grouped)))
potential_dups = {k: v for k, v in grouped.items() if len(v) > 1}
print('Found {} potential dups by nbytes'.format(len(potential_dups)))
GB = 2**30 # NOQA
MB = 2**20 # NOQA
max_bytes = 10 * MB
min_bytes = 64 * MB
duplicates = []
for k, fs in ut.ProgIter(potential_dups.items(), freq=1):
names = [f.n for f in fs]
if ut.allsame(names):
# Don't do big files yet
if k < max_bytes and k > min_bytes:
if ut.allsame([f.hashid for f in fs]):
duplicates.extend(fs)
for f1, f2 in ut.combinations(fs, 2):
f1.duplicates.add(f2)
f2.duplicates.add(f1)
def dpath_similarity(index, dpath1, dpath2):
d1 = index[dpath1]
d2 = index[dpath2]
set1 = {f.hashid for f in ut.ProgIter(d1.files)}
set2 = {f.hashid for f in ut.ProgIter(d2.files)}
# n_isect = len(set1.intersection(set2))
size1, size2 = map(len, (set1, set2))
# minsize = min(size1, size2)
# sim_measures = (n_isect, n_isect / minsize)
return ut.set_overlaps(set1, set2)
# return sim_measures
similarities = {}
r_to_dup = ut.group_items(duplicates, [p.r for p in duplicates])
for dpath, dups in r_to_dup.items():
# Check to see if the duplicates all point to the same dir
f = dups[0] # NOQA
common_dpath = set.intersection(*[{_.r
for _ in f.duplicates}
for f in dups])
for other in common_dpath:
sim_measures = dpath_similarity(index, dpath, other)
similarities[(dpath, other)] = sim_measures
print(ut.repr4(similarities, si=True, nl=2))
def generate_class_images(dream, target_labels):
"""
import plottool as pt
fnum = None
kw = dict(init='gauss', niters=500, update_rate=.05, weight_decay=1e-4)
target_labels = list(range(model.output_dims))
dream = draw_net.Dream(model, **kw)
target_labels = 8
images = list(dream.generate_class_images(target_labels))
vid = vt.make_video(images, 'dynimg.pimj', fps=1, is_color=False, format='PIM1')
vid = vt.make_video2(images, 'dynimg')
import matplotlib.pyplot as plt
ims = []
for img in imgs:
im = plt.imshow(img[:, :, 0], interpolation='nearest', cmap='gray')
ims.append([im])
import matplotlib.animation as animation
fig = plt.figure()
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True,
repeat_delay=1000)
ani.save('dynamic_images.mp4')
ut.startfile('dynamic_images.mp4')
plt.show()
"""
import ibeis_cnn.__THEANO__ as theano
from ibeis_cnn.__THEANO__ import tensor as T # NOQA
import utool as ut
input_shape = dream.model.input_shape
b, c, w, h = input_shape
was_scalar = not ut.isiterable(target_labels)
target_labels = ut.ensure_iterable(target_labels)
assert len(target_labels) <= b, 'batch size too small'
initial_state = dream._make_init_state()
shared_images = theano.shared(initial_state.astype(np.float32))
step_fn = dream._make_objective(shared_images, target_labels)
out = dream._postprocess_class_image(shared_images, target_labels,
was_scalar)
yield out
for _ in ut.ProgIter(range(dream.niters), lbl='class dream',
bs=True):
step_fn()
# objective = step_fn()
# print('objective = %r' % (objective,))
out = dream._postprocess_class_image(shared_images, target_labels,
was_scalar)
yield out
def batch_knn(indexer, vecs, K, chunksize=4096, label='batch knn'):
"""
Works like `indexer.knn` but the input is split into batches and
progress is reported to give an esimated time remaining.
"""
# Preallocate output
idxs = np.empty((vecs.shape[0], K), dtype=np.int32)
dists = np.empty((vecs.shape[0], K), dtype=np.float32)
# Generate chunk slices
num_chunks = ut.get_num_chunks(vecs.shape[0], chunksize)
iter_ = ut.ichunk_slices(vecs.shape[0], chunksize)
prog = ut.ProgIter(iter_, length=num_chunks, label=label)
for sl_ in prog:
idxs[sl_], dists[sl_] = indexer.knn(vecs[sl_], K=K)
return idxs, dists
def sight_resight_prob(N_range, nvisit1, nvisit2, resight):
"""
https://en.wikipedia.org/wiki/Talk:Mark_and_recapture#Statistical_treatment
http://stackoverflow.com/questions/31439875/infinite-summation-in-python/31442749
"""
k, K, n = resight, nvisit1, nvisit2
from scipy.special import comb
N_range = np.array(N_range)
def integers(start, blk_size=10000, pos=True, neg=False):
x = np.arange(start, start + blk_size)
while True:
if pos:
yield x
if neg:
yield -x - 1
x += blk_size
def converge_inf_sum(func, x_strm, eps=1e-5, axis=0):
# Can still be very slow
total = np.sum(func(x_strm.next()), axis=axis)
# for x_blk in ut.ProgIter(x_strm, lbl='converging'):
for x_blk in x_strm:
diff = np.sum(func(x_blk), axis=axis)
total += diff
# error = abs(np.linalg.norm(diff))
# logger.info('error = %r' % (error,))
if np.sqrt(diff.ravel().dot(diff.ravel())) <= eps:
# Converged
break
return total
numers = comb(N_range - K, n - k) / comb(N_range, n)
@ut.memoize
def func(N_):
return comb(N_ - K, n - k) / comb(N_, n)
denoms = []
for N in ut.ProgIter(N_range, lbl='denoms'):
x_strm = integers(start=(N + n - k), blk_size=100)
denom = converge_inf_sum(func, x_strm, eps=1e-3)
denoms.append(denom)
# denom = sum([func(N_) for N_ in range(N_start, N_start * 2)])
probs = numers / np.array(denoms)
return probs
def build_dpath_to_fidx(fpath_list, fidx_list, root_dpath):
dpath_to_fidx = ut.ddict(list)
nTotal = len(fpath_list)
_iter = zip(fidx_list, fpath_list)
dpath_to_fidx = ut.ddict(list)
for fidx, fpath in ut.ProgIter(_iter,
'making dpath fidx map',
freq=50000,
nTotal=nTotal):
current_path = fpath
while True:
current_path = dirname(current_path)
dpath_to_fidx[current_path].append(fidx)
if current_path == root_dpath:
break
return dpath_to_fidx
def find_opt_ratio(pblm):
"""
script to help find the correct value for the ratio threshold
>>> from wbia.algo.verif.vsone import * # NOQA
>>> pblm = OneVsOneProblem.from_empty('PZ_PB_RF_TRAIN')
>>> pblm = OneVsOneProblem.from_empty('GZ_Master1')
"""
# Find best ratio threshold
pblm.load_samples()
infr = pblm.infr
edges = ut.emap(tuple, pblm.samples.aid_pairs.tolist())
task = pblm.samples['match_state']
pos_idx = task.class_names.tolist().index(POSTV)
config = {'ratio_thresh': 1.0, 'sv_on': False}
matches = infr._exec_pairwise_match(edges, config)
import wbia.plottool as pt
import sklearn.metrics
pt.qtensure()
thresholds = np.linspace(0, 1.0, 100)
pos_truth = task.y_bin.T[pos_idx]
ratio_fs = [m.local_measures['ratio'] for m in matches]
aucs = []
# Given the current correspondences: Find the optimal
# correspondence threshold.
for thresh in ut.ProgIter(thresholds, 'computing thresh'):
scores = np.array([fs[fs < thresh].sum() for fs in ratio_fs])
roc = sklearn.metrics.roc_auc_score(pos_truth, scores)
aucs.append(roc)
aucs = np.array(aucs)
opt_auc = aucs.max()
opt_thresh = thresholds[aucs.argmax()]
if True:
pt.plt.plot(thresholds, aucs, 'r-', label='')
pt.plt.plot(opt_thresh,
opt_auc,
'ro',
label='L opt=%r' % (opt_thresh, ))
pt.set_ylabel('auc')
pt.set_xlabel('ratio threshold')
pt.legend()
def build_fpath_hashes(drive):
try:
fpath_hashX_list = drive.cache.load('fpath_hashX_list')
except ut.CacheMissException:
fpath_hashX_list = [None] * len(drive.fpath_list)
assert len(drive.fpath_bytes_list) == len(drive.fpath_list)
tier_windows = drive.get_tier_windows()
tier_flags = drive.get_tier_flags()
tier_fpaths = [
ut.compress(drive.fpath_list, flags) for flags in tier_flags
]
#for tier, fpaths in enumerate(tier_fpaths):
#chosen_tiers = [6, 5, 4, 3, 2, 1, 0]
chosen_tiers = list(range(len(tier_windows)))[::-1]
for tier in chosen_tiers:
window = np.array(tier_windows[tier])
minbytes = window[np.isfinite(window)].min()
#stride = max(1, minbytes // (2 ** 20))
stride = max(1, minbytes // (2**20))
print('%s tier %d stride = %r' % (
drive.root_dpath,
tier,
stride,
))
fpaths = tier_fpaths[tier]
print('# fpaths = %r' % (len(fpaths), ))
tier_hashes = [
tryhash(fpath, stride) for fpath in ut.ProgIter(
fpaths, 'tier=%r hashes' % (tier, ), freq=100)
]
#import register_files
#tier_hashes = list(ut.buffered_generator((
# register_files.tryhash(fpath, stride) for fpath in
# ut.ProgIter(fpaths, 'tier=%r hashes' % (tier,), freq=100)
#)))
tier_idxs = np.where(tier_flags[tier])[0]
for idx, hash_ in zip(tier_idxs, tier_hashes):
fpath_hashX_list[idx] = hash_
drive.cache.save('fpath_hashX_list', fpath_hashX_list)
return fpath_hashX_list
def _load_singles_fallback(fpaths_hit):
fpath_iter = ut.ProgIter(fpaths_hit,
enabled=len(fpaths_hit) > 1,
label='checking chipmatch cache',
adjust=True,
freq=1)
# Recompute those that fail loading
qaid_to_hit = {}
for fpath in fpath_iter:
try:
cm = chip_match.ChipMatch.load_from_fpath(fpath, verbose=False)
except chip_match.NeedRecomputeError:
pass
else:
qaid_to_hit[cm.qaid] = cm
print('%d / %d cached matches need to be recomputed' %
(len(fpaths_hit) - len(qaid_to_hit), len(fpaths_hit)))
return qaid_to_hit
def make_tree_structure(valid_fpaths):
root = {}
def dict_getitem_default(dict_, key, type_):
try:
val = dict_[key]
except KeyError:
val = type_()
dict_[key] = val
return val
for fpath in ut.ProgIter(valid_fpaths, 'building tree', freq=30000):
path_components = ut.dirsplit(fpath)
current = root
for comp in path_components[:-1]:
current = dict_getitem_default(current, comp, dict)
contents = dict_getitem_default(current, '.', list)
contents.append(path_components[-1])
return root
def get_fpath_bytes_list(drive):
print('Building fpath bytes for %r' % (drive, ))
try:
fpath_bytes_list = drive.cache.load('fpath_bytes_list')
assert len(fpath_bytes_list) == len(drive.fpath_list), 'bad length'
except ut.CacheMissException:
def tryread_nbytes(fpath):
try:
return ut.file_bytes(fpath)
except SystemErrors:
return np.nan
fpath_bytes_list = [
tryread_nbytes(fpath) for fpath in ut.ProgIter(
drive.fpath_list, 'reading size', freq=1000)
]
assert len(drive.fpath_list) == len(drive.fpath_list)
drive.cache.save('fpath_bytes_list', fpath_bytes_list)
return fpath_bytes_list
def dump_vectors(qreq_):
"""
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.smk.smk_pipeline import * # NOQA
>>> import wbia
>>> ibs, aid_list = wbia.testdata_aids(defaultdb='PZ_MTEST', a='default:mingt=2,pername=2')
>>> qaids = aid_list[0:2]
>>> daids = aid_list[:]
>>> config = {'nAssign': 1, 'num_words': 8000,
>>> 'sv_on': True}
>>> qreq_ = SMKRequest(ibs, qaids, daids, config)
>>> qreq_.ensure_data()
"""
inva = qreq_.dinva
X = qreq_.dinva.get_annot(qreq_.daids[0])
n_words = inva.wx_list[-1] + 1
n_dims = X.agg_rvecs.shape[1]
n_annots = len(qreq_.daids)
X.agg_rvecs.dtype
vlads = np.zeros((n_annots, n_words, n_dims), dtype=np.float32)
ids_ = list(zip(qreq_.dnids, qreq_.daids))
for count, (nid, aid) in enumerate(ut.ProgIter(ids_, label='vlad')):
# X.rrr()
X = qreq_.dinva.get_annot(aid)
out = vlads[count]
out[X.wx_list] = X.agg_rvecs
# X.to_dense(out=out)
# Flatten out
vlads.shape = (n_annots, n_words * n_dims)
ut.print_object_size(vlads)
fname = 'vlad_%d_d%d_%s' % (n_annots, n_words * n_dims,
qreq_.ibs.get_dbname())
fpath = ut.truepath('~/' + fname + '.mat')
import scipy.io
mdict = {
'vlads': vlads,
'nids': qreq_.dnids,
}
scipy.io.savemat(fpath, mdict)
