def show_each_dstncvs_chip(self, dodraw=True):
"""
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-show_each_dstncvs_chip --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact(mx=1)
>>> self.show_each_dstncvs_chip(dodraw=False)
>>> pt.show_if_requested()
"""
dstncvs1, dstncvs2 = scoring.get_kpts_distinctiveness(self.ibs,
[self.qaid,
self.daid])
print('dstncvs1_stats = ' + ut.get_stats_str(dstncvs1))
print('dstncvs2_stats = ' + ut.get_stats_str(dstncvs2))
weight_label = 'dstncvs'
showkw = dict(weight_label=weight_label, ell=False, pts=True)
viz_chip.show_chip(self.ibs, self.qaid, weights=dstncvs1,
fnum=pt.next_fnum(), **showkw)
viz_chip.show_chip(self.ibs, self.daid, weights=dstncvs2,
fnum=pt.next_fnum(), **showkw)
if dodraw:
viz.draw()
def show_each_dstncvs_chip(self, dodraw=True):
"""
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-show_each_dstncvs_chip --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact(mx=1)
>>> self.show_each_dstncvs_chip(dodraw=False)
>>> pt.show_if_requested()
"""
dstncvs1, dstncvs2 = scoring.get_kpts_distinctiveness(
self.ibs, [self.qaid, self.daid])
print('dstncvs1_stats = ' + ut.get_stats_str(dstncvs1))
print('dstncvs2_stats = ' + ut.get_stats_str(dstncvs2))
weight_label = 'dstncvs'
showkw = dict(weight_label=weight_label, ell=False, pts=True)
viz_chip.show_chip(self.ibs,
self.qaid,
weights=dstncvs1,
fnum=pt.next_fnum(),
**showkw)
viz_chip.show_chip(self.ibs,
self.daid,
weights=dstncvs2,
fnum=pt.next_fnum(),
**showkw)
if dodraw:
viz.draw()
def jagged_stats_info(arr_, lbl, col_lbls):
arr = ut.recursive_replace(arr_, np.inf, np.nan)
# Treat infinite as nan
stat_dict = ut.get_jagged_stats(arr, use_nan=True, use_sum=True)
sel_stat_dict, sel_indices = ut.find_interesting_stats(stat_dict, col_lbls)
sel_col_lbls = ut.take(col_lbls, sel_indices)
statstr_kw = dict(precision=3, newlines=True, lbl=lbl, align=True)
stat_str = ut.get_stats_str(stat_dict=stat_dict, **statstr_kw)
sel_stat_str = ut.get_stats_str(stat_dict=sel_stat_dict, **statstr_kw)
sel_stat_str = 'sel_col_lbls = %s' % (ut.list_str(sel_col_lbls),) + '\n' + sel_stat_str
return stat_str, sel_stat_str
def jagged_stats_info(arr_, lbl, col_lbls):
arr = ut.recursive_replace(arr_, np.inf, np.nan)
# Treat infinite as nan
stat_dict = ut.get_jagged_stats(arr, use_nan=True, use_sum=True)
sel_stat_dict, sel_indices = ut.find_interesting_stats(
stat_dict, col_lbls)
sel_col_lbls = ut.take(col_lbls, sel_indices)
statstr_kw = dict(precision=3, newlines=True, lbl=lbl, align=True)
stat_str = ut.get_stats_str(stat_dict=stat_dict, **statstr_kw)
sel_stat_str = ut.get_stats_str(stat_dict=sel_stat_dict, **statstr_kw)
sel_stat_str = 'sel_col_lbls = %s' % (
ut.list_str(sel_col_lbls), ) + '\n' + sel_stat_str
return stat_str, sel_stat_str
def check_daid2_sccw(daid2_sccw, verbose=True):
daid2_sccw_values = daid2_sccw
assert not np.any(np.isnan(daid2_sccw_values)), 'sccws are nan'
if verbose:
print('database sccws are not nan')
print('database sccw stats:')
print(ut.get_stats_str(daid2_sccw_values, newlines=True))
def print_annotationmatch_scores_map(orgres2_scores):
print('+-----------------------------')
print('| CHIPMATCH SCORES:')
for orgtype, scores in six.iteritems(orgres2_scores):
print('| orgtype(%r)' % (orgtype, ))
print('| scores: %s' % (ut.get_stats_str(scores)))
print('L-----------------------------')
def print_annotationmatch_scores_map(orgres2_scores):
print('+-----------------------------')
print('| CHIPMATCH SCORES:')
for orgtype, scores in six.iteritems(orgres2_scores):
print('| orgtype(%r)' % (orgtype,))
print('| scores: %s' % (ut.get_stats_str(scores)))
print('L-----------------------------')
def check_daid2_sccw(daid2_sccw, verbose=True):
daid2_sccw_values = daid2_sccw
assert not np.any(np.isnan(daid2_sccw_values)), 'sccws are nan'
if verbose:
print('database sccws are not nan')
print('database sccw stats:')
print(ut.get_stats_str(daid2_sccw_values, newlines=True))
def display_info(ibs, invindex, annots_df):
from vtool import clustering2 as clustertool
################
from ibeis.other import dbinfo
print(ibs.get_infostr())
dbinfo.get_dbinfo(ibs, verbose=True)
################
print('Inverted Index Stats: vectors per word')
print(ut.get_stats_str(map(len, invindex.wx2_idxs.values())))
################
#qfx2_vec = annots_df['vecs'][1]
centroids = invindex.words
num_pca_dims = 2 # 3
whiten = False
kwd = dict(
num_pca_dims=num_pca_dims,
whiten=whiten,
)
#clustertool.rrr()
def makeplot_(fnum, prefix, data, labels='centroids', centroids=centroids):
return clustertool.plot_centroids(data,
centroids,
labels=labels,
fnum=fnum,
prefix=prefix + '\n',
**kwd)
makeplot_(1, 'centroid vecs', centroids)
def print_desc_distances_map(orgres2_distmap):
print('+-----------------------------')
print('| DESCRIPTOR MATCHE DISTANCES:')
for orgtype, distmap in six.iteritems(orgres2_distmap):
print('| orgtype(%r)' % (orgtype,))
for disttype, dists in six.iteritems(distmap):
print('| disttype(%12r): %s' % (disttype, ut.get_stats_str(dists)))
print('L-----------------------------')
def print_desc_distances_map(orgres2_distmap):
print('+-----------------------------')
print('| DESCRIPTOR MATCHE DISTANCES:')
for orgtype, distmap in six.iteritems(orgres2_distmap):
print('| orgtype(%r)' % (orgtype, ))
for disttype, dists in six.iteritems(distmap):
print('| disttype(%12r): %s' %
(disttype, ut.get_stats_str(dists)))
print('L-----------------------------')
def parse_img_from_arg(argstr_):
fpath = ut.get_argval(argstr_, type_=str, default='None')
if fpath is not None and fpath != 'None':
img = vt.imread(fpath, grayscale=True)
print('Reading %s with stats %s' % (fpath, ut.get_stats_str(img, axis=None)))
else:
print('Did not read %s' % (fpath))
img = None
return img
def parse_img_from_arg(argstr_):
fpath = ut.get_argval(argstr_, type_=str, default='None')
if fpath is not None and fpath != 'None':
img = vt.imread(fpath, grayscale=True)
print('Reading %s with stats %s' %
(fpath, ut.get_stats_str(img, axis=None)))
else:
print('Did not read %s' % (fpath))
img = None
return img
def learn_k():
r"""
CommandLine:
python -m ibeis.other.optimize_k --test-learn_k
python -m ibeis.other.optimize_k --test-learn_k --show
python -m ibeis.other.optimize_k --test-learn_k --show --dummy
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.other.optimize_k import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> # execute function
>>> known_nd_data, known_target_points, given_data_dims, opt_model_params = learn_k()
>>> # verify results
>>> ut.quit_if_noshow()
>>> plot_search_surface(known_nd_data, known_target_points, given_data_dims, opt_model_params)
>>> pt.all_figures_bring_to_front()
>>> pt.show_if_requested()
"""
# Compute Training Data
varydict = {
#'K': [4, 7, 10, 13, 16, 19, 22, 25][:4],
#'K': [1, 2, 3, 4, 8, 10, 13, 15],
'K': [1, 2, 4, 8, 16],
#'nDaids': [20, 100, 250, 500, 750, 1000],
}
nDaids_basis = [20, 30, 50, 75, 100, 200, 250, 300, 325, 350, 400, 500, 600, 750, 800, 900, 1000, 1500]
DUMMY = ut.get_argflag('--dummy')
if DUMMY:
nDaids_list, K_list, nError_list = test_training_data(varydict, nDaids_basis)
nError_list = nError_list.astype(np.float32) / nError_list.max()
else:
dbname = ut.get_argval('--db', default='PZ_Master0')
ibs = ibeis.opendb(dbname)
verbose = False
qaids, daids_list = collect_ibeis_training_annotations(ibs, nDaids_basis, verbose=verbose)
nDaids_list, K_list, nError_list = evaluate_training_data(ibs, qaids, daids_list, varydict, nDaids_basis, verbose=verbose)
nError_list = nError_list.astype(np.float32) / len(qaids)
print('\nFinished Get Training Data')
print('len(qaids) = %r' % (len(qaids)))
print(ut.get_stats_str(nError_list))
#unique_nDaids = np.unique(nDaids_list)
# Alias to general optimization problem
known_nd_data = np.vstack([nDaids_list, K_list]).T
known_target_points = nError_list
# Mark the data we are given vs what we want to learn
given_data_dims = [0]
#learn_data_dims = [1]
# Minimize K params
opt_model_params = minimize_compute_K_params(known_nd_data, known_target_points, given_data_dims)
return known_nd_data, known_target_points, given_data_dims, opt_model_params
def plot_weights(self, event=None):
scalars = self.infr.get_scalars()
import plottool as pt
inter = pt.ExpandableInteraction(fnum=1)
for px, (key, vals) in enumerate(scalars.items()):
print(key + ' = ' + ut.get_stats_str(vals, use_nan=True))
args = (np.arange(len(vals)), sorted(vals))
kw = dict(title=key, y_label=key, marker='-o', equal_aspect=False)
inter.append_partial(pt.plot2, *args, **kw)
inter.start()
inter.show_page()
pt.update()
def get_inspect_str(qres, ibs=None, name_scoring=False):
qres.assert_self()
#ut.embed()
top_lbls = [' top aids', ' scores', ' rawscores', ' ranks']
top_aids = np.array(qres.get_top_aids(num=6, name_scoring=name_scoring, ibs=ibs), dtype=np.int32)
top_scores = np.array(qres.get_aid_scores(top_aids), dtype=np.float64)
top_rawscores = np.array(qres.get_aid_scores(top_aids, rawscore=True), dtype=np.float64)
top_ranks = np.array(qres.get_aid_ranks(top_aids), dtype=np.int32)
top_list = [top_aids, top_scores, top_rawscores, top_ranks]
if ibs is not None:
top_lbls += [' isgt']
istrue = qres.get_aid_truth(ibs, top_aids)
top_list.append(np.array(istrue, dtype=np.int32))
if name_scoring:
top_lbls = ['top nid'] + top_lbls
top_list = [ibs.get_annot_name_rowids(top_aids)] + top_list
top_stack = np.vstack(top_list)
#top_stack = np.array(top_stack, dtype=object)
top_stack = np.array(top_stack, dtype=np.float32)
#np.int32)
top_str = np.array_str(top_stack, precision=3, suppress_small=True, max_line_width=200)
top_lbl = '\n'.join(top_lbls)
inspect_list = ['QueryResult',
qres.cfgstr,
]
if ibs is not None:
gt_ranks = qres.get_gt_ranks(ibs=ibs)
gt_scores = qres.get_gt_scores(ibs=ibs)
inspect_list.append('gt_ranks = %r' % gt_ranks)
inspect_list.append('gt_scores = %r' % gt_scores)
nFeatMatch_list = get_num_feats_in_matches(qres)
nFeatMatch_stats_str = ut.get_stats_str(nFeatMatch_list, newlines=True, exclude_keys=('nMin', 'nMax'))
inspect_list.extend([
'qaid=%r ' % qres.qaid,
ut.hz_str(top_lbl, ' ', top_str),
'num feat matches per annotation stats:',
#ut.indent(ut.dict_str(nFeatMatch_stats)),
ut.indent(nFeatMatch_stats_str),
])
inspect_str = '\n'.join(inspect_list)
#inspect_str = ut.indent(inspect_str, '[INSPECT] ')
return inspect_str
def get_inspect_str(qres, ibs=None, name_scoring=False):
qres.assert_self()
#ut.embed()
top_lbls = [' top aids', ' scores', ' rawscores', ' ranks']
top_aids = np.array(qres.get_top_aids(num=6, name_scoring=name_scoring, ibs=ibs), dtype=np.int32)
top_scores = np.array(qres.get_aid_scores(top_aids), dtype=np.float64)
top_rawscores = np.array(qres.get_aid_scores(top_aids, rawscore=True), dtype=np.float64)
top_ranks = np.array(qres.get_aid_ranks(top_aids), dtype=np.int32)
top_list = [top_aids, top_scores, top_rawscores, top_ranks]
if ibs is not None:
top_lbls += [' isgt']
istrue = qres.get_aid_truth(ibs, top_aids)
top_list.append(np.array(istrue, dtype=np.int32))
if name_scoring:
top_lbls = ['top nid'] + top_lbls
top_list = [ibs.get_annot_name_rowids(top_aids)] + top_list
top_stack = np.vstack(top_list)
#top_stack = np.array(top_stack, dtype=object)
top_stack = np.array(top_stack, dtype=np.float32)
#np.int32)
top_str = np.array_str(top_stack, precision=3, suppress_small=True, max_line_width=200)
top_lbl = '\n'.join(top_lbls)
inspect_list = ['QueryResult',
qres.cfgstr,
]
if ibs is not None:
gt_ranks = qres.get_gt_ranks(ibs=ibs)
gt_scores = qres.get_gt_scores(ibs=ibs)
inspect_list.append('gt_ranks = %r' % gt_ranks)
inspect_list.append('gt_scores = %r' % gt_scores)
nFeatMatch_list = get_num_feats_in_matches(qres)
nFeatMatch_stats_str = ut.get_stats_str(nFeatMatch_list, newlines=True, exclude_keys=('nMin', 'nMax'))
inspect_list.extend([
'qaid=%r ' % qres.qaid,
ut.hz_str(top_lbl, ' ', top_str),
'num feat matches per annotation stats:',
#ut.indent(ut.dict_str(nFeatMatch_stats)),
ut.indent(nFeatMatch_stats_str),
])
inspect_str = '\n'.join(inspect_list)
#inspect_str = ut.indent(inspect_str, '[INSPECT] ')
return inspect_str
def display_info(ibs, invindex, annots_df):
from vtool import clustering2 as clustertool
################
from ibeis.other import dbinfo
print(ibs.get_infostr())
dbinfo.get_dbinfo(ibs, verbose=True)
################
print('Inverted Index Stats: vectors per word')
print(ut.get_stats_str(map(len, invindex.wx2_idxs.values())))
################
#qfx2_vec = annots_df['vecs'][1]
centroids = invindex.words
num_pca_dims = 2 # 3
whiten = False
kwd = dict(num_pca_dims=num_pca_dims,
whiten=whiten,)
#clustertool.rrr()
def makeplot_(fnum, prefix, data, labels='centroids', centroids=centroids):
return clustertool.plot_centroids(data, centroids, labels=labels,
fnum=fnum, prefix=prefix + '\n', **kwd)
makeplot_(1, 'centroid vecs', centroids)
def select_ith_match(self, mx):
"""
Selects the ith match and visualizes and prints information concerning
features weights, keypoint details, and sift descriptions
"""
import plottool as pt
from plottool import viz_featrow
from plottool import interact_helpers as ih
# <CLOSURE VARS>
fnum = self.fnum
same_fig = self.same_fig
rchip1 = self.rchip1
rchip2 = self.rchip2
# </CLOSURE VARS>
self.mx = mx
print("+--- SELECT --- ")
print("... selecting mx-th=%r feature match" % mx)
fsv = self.fsv
fs = self.fs
print("score stats:")
print(ut.get_stats_str(fsv, axis=0, newlines=True))
print("fsv[mx] = %r" % (fsv[mx],))
print("fs[mx] = %r" % (fs[mx],))
# ----------------------
# Get info for the select_ith_match plot
self.mode = 1
# Get the mx-th feature match
fx1, fx2 = self.fm[mx]
# Older info
fscore2 = self.fs[mx]
fk2 = None
kp1, kp2 = self.kpts1[fx1], self.kpts2[fx2]
vecs1, vecs2 = self.vecs1[fx1], self.vecs2[fx2]
info1 = "\nquery"
info2 = "\nk=%r fscore=%r" % (fk2, fscore2)
# self.last_fx = fx1
self.last_fx = fx1
# Extracted keypoints to draw
extracted_list = [(rchip1, kp1, vecs1, fx1, "aid1", info1), (rchip2, kp2, vecs2, fx2, "aid2", info2)]
# Normalizng Keypoint
# if hasattr(cm, 'filt2_meta') and 'lnbnn' in cm.filt2_meta:
# qfx2_norm = cm.filt2_meta['lnbnn']
# # Normalizing chip and feature
# (aid3, fx3, normk) = qfx2_norm[fx1]
# rchip3 = ibs.get_annot_chips(aid3)
# kp3 = ibs.get_annot_kpts(aid3)[fx3]
# sift3 = ibs.get_annot_vecs(aid3)[fx3]
# info3 = '\nnorm %s k=%r' % (vh.get_aidstrs(aid3), normk)
# extracted_list.append((rchip3, kp3, sift3, fx3, aid3, info3))
# else:
# pass
# #print('WARNING: meta doesnt exist')
# ----------------------
# Draw the select_ith_match plot
nRows, nCols = len(extracted_list) + same_fig, 3
# Draw matching chips and features
sel_fm = np.array([(fx1, fx2)])
pnum1 = (nRows, 1, 1) if same_fig else (1, 1, 1)
vert = self.vert if self.vert is not None else False
self.chipmatch_view(pnum=pnum1, ell_alpha=0.4, ell_linewidth=1.8, colors=pt.BLUE, sel_fm=sel_fm, vert=vert)
# Draw selected feature matches
px = nCols * same_fig # plot offset
prevsift = None
if not same_fig:
# fnum2 = fnum + len(viz.FNUMS)
fnum2 = self.fnum2
fig2 = pt.figure(fnum=fnum2, docla=True, doclf=True)
else:
fnum2 = fnum
for (rchip, kp, sift, fx, aid, info) in extracted_list:
px = viz_featrow.draw_feat_row(
rchip, fx, kp, sift, fnum2, nRows, nCols, px, prevsift=prevsift, aid=aid, info=info
)
prevsift = sift
if not same_fig:
ih.connect_callback(fig2, "button_press_event", self.on_click)
def viz_annot_with_metrics(ibs, invindex, aid, metrics,
metric_keys=['wx2_nMembers',
('wx2_pdist_stats', 'mean'),
('wx2_wdist_stats', 'mean')],
show_orig=True,
show_idf=True,
show_words=False,
show_analysis=True,
show_aveprecision=True,
show_featweights=True,
qfx2_closest_k_list=None,
show_word_correct_assignments=False,
qres_list=None):
"""
Args:
ibs (IBEISController):
invindex (InvertedIndex): object for fast vocab lookup
aid (int):
metrics (namedtuple):
Example:
>>> from ibeis.algo.hots.smk.smk_plots import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> from ibeis.algo.hots.smk import smk_repr
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=64000)
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=8000)
>>> #tup = smk_debug.testdata_raw_internals0(db='PZ_Master0', nWords=64000)
>>> tup = smk_debug.testdata_raw_internals0(db='PZ_Mothers', nWords=8000)
>>> ibs, annots_df, daids, qaids, invindex, qreq_ = tup
>>> smk_repr.compute_data_internals_(invindex, qreq_.qparams, delete_rawvecs=False)
>>> invindex.idx2_wxs = np.array(invindex.idx2_wxs)
>>> metric_keys=['wx2_nMembers', ('wx2_pdist_stats', 'mean'), ('wx2_wdist_stats', 'mean')]
>>> metrics = compute_word_metrics(invindex)
>>> aid = 1
"""
#viz_chip.rrr()
#df2.rrr()
kpts = ibs.get_annot_kpts(aid)
if ut.VERBOSE:
ut.super_print(kpts)
if show_word_correct_assignments or show_idf:
# Get only the first assigned word
# FIXME: need to look at multi-assignment
_mask = invindex.idx2_daid == aid
fxs = invindex.idx2_dfx[_mask]
wxs = invindex.idx2_wxs[_mask].T[0].T
assert len(fxs) == len(kpts)
assert len(fxs) == len(wxs)
fnum = 1
dbname = ibs.get_dbname()
def _plot(metric, fnum=1, lbl='', annote=True, darken=.1, colortype='score', **kwargs):
print('ploting fnum=%r' % fnum)
#lblaug = ' db=%r, nWords = %r' % (dbname, nWords)
lblaug = ' db=%r' % (dbname)
figtitle = lbl + lblaug
lbl = lbl
plot_chip_metric(ibs, aid, metric=metric, fnum=fnum, lbl=lbl, figtitle=figtitle,
annote=annote, darken=darken, colortype=colortype, **kwargs)
return fnum + 1
# Original Plot
if show_orig:
fnum = _plot(None, fnum=fnum, lbl='Orig Chip', annote=False, darken=None)
# IDF Plot
if show_idf:
idf_list = np.array(list(ut.dict_take_gen(invindex.wx2_idf, wxs)))
fnum = _plot(idf_list, fnum=fnum, lbl='IDF')
print('stats(idf_list) = ' + ut.get_stats_str(idf_list))
# Word Plot
if show_words:
fnum = _plot(wxs, fnum=fnum, lbl='Words', colortype='label')
# LNBNN Result Plots
if qfx2_closest_k_list is not None:
for qres, qfx2_closest_k in zip(qres_list, qfx2_closest_k_list):
print(' --- qres item ---')
if qres is not None:
from ibeis.algo.hots.hots_query_result import QueryResult
assert isinstance(qres, QueryResult)
if show_analysis:
qres.show_analysis(ibs=ibs, fnum=fnum, figtitle=qres.make_smaller_title())
fnum += 1
if show_aveprecision:
qres.show_precision_recall_curve(ibs=ibs, fnum=fnum)
fnum += 1
if qfx2_closest_k is not None:
# Plot ranked positions
qfx2_closest_k = np.array(qfx2_closest_k)
qfx2_closest_k_qeq0 = qfx2_closest_k[qfx2_closest_k >= 0]
qfx2_closest_k_lt0 = qfx2_closest_k[qfx2_closest_k < 0]
print('stats(qfx2_closest_k_qeq0) = ' + ut.get_stats_str(qfx2_closest_k_qeq0))
print('stats(qfx2_closest_k_lt0) = ' + ut.get_stats_str(qfx2_closest_k_lt0))
fnum = _plot(qfx2_closest_k, fnum=fnum, lbl='Correct Ranks ' + qres.make_smaller_title(), colortype='custom', reverse_cmap=True)
# Correct word assignment plots
if show_word_correct_assignments:
unique_wxs, unique_inverse = np.unique(wxs, return_inverse=True)
# Get the aids that belong to each word
_idxs_list = ut.dict_take(invindex.wx2_idxs, unique_wxs)
_aids_list = [invindex.idx2_daid.take(idxs) for idxs in _idxs_list]
# Check if this word will provide a correct assignment -
# two ground truth chip exist within the same word
gt_aids = np.array(ibs.get_annot_groundtruth(aid))
_hastp_list = np.array([len(np.intersect1d(aids, gt_aids)) > 0 for aids in _aids_list])
# Map back to the space of features
# mark each feature match as having a correct word mapping or not
hascorrectmatch = _hastp_list[unique_inverse]
hascorrectmatch_ = hascorrectmatch.astype(np.int32) * 3 - 2
lbl = 'Correct Words ' + qres.make_smaller_title() + '\n Yellow means the word contains a correct match in the word\'s invindex. Blue is the opposite.'
fnum = _plot(hascorrectmatch_, fnum=fnum, lbl=lbl, colortype='custom', reverse_cmap=False)
# Feature Weight Plots
if show_featweights:
from ibeis.algo.preproc import preproc_featweight
featweights = preproc_featweight.compute_fgweights(ibs, [aid])[0]
# plot rf feature weights
detect_cfgstr = ibs.cfg.detect_cfg.get_cfgstr()
fnum = _plot(featweights, fnum=fnum, lbl='Feature Weights ' + detect_cfgstr, colortype='score')
# Word Metric Plots
for count, metrickey in enumerate(metric_keys):
if isinstance(metrickey, tuple):
#lbl = repr(metrickey)
def fixstr(str_):
return str_.replace('wx2_', '').replace('_stats', '')
lbl = '%s(%s)' % (metrickey[1].upper(), fixstr(metrickey[0]))
else:
lbl = str(metrickey)
metric_list = metric_clamped_stat(metrics, wxs, metrickey)
fnum = _plot(metric_list, fnum=fnum, lbl=lbl)
def learn_k():
r"""
CommandLine:
python -m ibeis.other.optimize_k --test-learn_k
python -m ibeis.other.optimize_k --test-learn_k --show
python -m ibeis.other.optimize_k --test-learn_k --show --dummy
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.other.optimize_k import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> # execute function
>>> known_nd_data, known_target_points, given_data_dims, opt_model_params = learn_k()
>>> # verify results
>>> ut.quit_if_noshow()
>>> plot_search_surface(known_nd_data, known_target_points, given_data_dims, opt_model_params)
>>> pt.all_figures_bring_to_front()
>>> pt.show_if_requested()
"""
# Compute Training Data
varydict = {
#'K': [4, 7, 10, 13, 16, 19, 22, 25][:4],
#'K': [1, 2, 3, 4, 8, 10, 13, 15],
'K': [1, 2, 4, 8, 16],
#'nDaids': [20, 100, 250, 500, 750, 1000],
}
nDaids_basis = [
20, 30, 50, 75, 100, 200, 250, 300, 325, 350, 400, 500, 600, 750, 800,
900, 1000, 1500
]
DUMMY = ut.get_argflag('--dummy')
if DUMMY:
nDaids_list, K_list, nError_list = test_training_data(
varydict, nDaids_basis)
nError_list = nError_list.astype(np.float32) / nError_list.max()
else:
dbname = ut.get_argval('--db', default='PZ_Master0')
ibs = ibeis.opendb(dbname)
verbose = False
qaids, daids_list = collect_ibeis_training_annotations(ibs,
nDaids_basis,
verbose=verbose)
nDaids_list, K_list, nError_list = evaluate_training_data(
ibs, qaids, daids_list, varydict, nDaids_basis, verbose=verbose)
nError_list = nError_list.astype(np.float32) / len(qaids)
print('\nFinished Get Training Data')
print('len(qaids) = %r' % (len(qaids)))
print(ut.get_stats_str(nError_list))
#unique_nDaids = np.unique(nDaids_list)
# Alias to general optimization problem
known_nd_data = np.vstack([nDaids_list, K_list]).T
known_target_points = nError_list
# Mark the data we are given vs what we want to learn
given_data_dims = [0]
#learn_data_dims = [1]
# Minimize K params
opt_model_params = minimize_compute_K_params(known_nd_data,
known_target_points,
given_data_dims)
return known_nd_data, known_target_points, given_data_dims, opt_model_params
def select_ith_match(self, mx):
"""
Selects the ith match and visualizes and prints information concerning
features weights, keypoint details, and sift descriptions
Args:
mx (int) - the ith match to visualize
qaid (int) - query annotation id
aid (int) - database annotation id
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-select_ith_match --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact(mx=1)
>>> pt.show_if_requested()
"""
ibs = self.ibs
qaid = self.qaid
aid = self.daid
fnum = self.fnum
figtitle = self.figtitle
rchip1 = self.rchip1
rchip2 = self.rchip2
aid = self.daid
same_fig = self.same_fig
self.mx = mx
print('+--- SELECT --- ')
print('qaid=%r, daid=%r' % (qaid, aid))
print('... selecting mx-th=%r feature match' % mx)
if False:
print('score stats:')
print(ut.get_stats_str(self.fsv, axis=0, newlines=True))
print('fsv[mx] = %r' % (self.fsv[mx], ))
print('fs[mx] = %r' % (self.fs[mx], ))
"""
# test feature weights of actual chips
fx1, fx2 = fm[mx]
daid = aid
ibs.get_annot_fgweights([daid])[0][fx2]
ibs.get_annot_fgweights([qaid])[0][fx1]
"""
#----------------------
# Get info for the select_ith_match plot
self.mode = 1
# Get the mx-th feature match
fx1, fx2 = self.fm[mx]
fscore2 = self.fs[mx]
fk2 = self.fk[mx]
kpts1 = ibs.get_annot_kpts([self.qaid],
config2_=self.query_config2_)[0]
kpts2 = ibs.get_annot_kpts([self.daid], config2_=self.data_config2_)[0]
desc1 = ibs.get_annot_vecs([self.qaid],
config2_=self.query_config2_)[0]
desc2 = ibs.get_annot_vecs([self.daid], config2_=self.data_config2_)[0]
kp1, kp2 = kpts1[fx1], kpts2[fx2]
sift1, sift2 = desc1[fx1], desc2[fx2]
info1 = '\nquery'
info2 = '\nk=%r fscore=%r' % (fk2, fscore2)
#last_state.last_fx = fx1
self.last_fx = fx1
# Extracted keypoints to draw
extracted_list = [(rchip1, kp1, sift1, fx1, self.qaid, info1),
(rchip2, kp2, sift2, fx2, self.daid, info2)]
# Normalizng Keypoint
#if hasattr(cm, 'filt2_meta') and 'lnbnn' in cm.filt2_meta:
# qfx2_norm = cm.filt2_meta['lnbnn']
# # Normalizing chip and feature
# (aid3, fx3, normk) = qfx2_norm[fx1]
# rchip3 = ibs.get_annot_chips(aid3)
# kp3 = ibs.get_annot_kpts(aid3)[fx3]
# sift3 = ibs.get_annot_vecs(aid3)[fx3]
# info3 = '\nnorm %s k=%r' % (vh.get_aidstrs(aid3), normk)
# extracted_list.append((rchip3, kp3, sift3, fx3, aid3, info3))
#else:
# pass
# print('WARNING: meta doesnt exist')
#----------------------
# Draw the select_ith_match plot
nRows, nCols = len(extracted_list) + same_fig, 3
# Draw matching chips and features
sel_fm = np.array([(fx1, fx2)])
pnum1 = (nRows, 1, 1) if same_fig else (1, 1, 1)
vert = self.vert if self.vert is not None else False
self.chipmatch_view(pnum1,
ell_alpha=.4,
ell_linewidth=1.8,
colors=df2.BLUE,
sel_fm=sel_fm,
vert=vert)
# Draw selected feature matches
px = nCols * same_fig # plot offset
prevsift = None
if not same_fig:
#fnum2 = fnum + len(viz.FNUMS)
fnum2 = self.fnum2
fig2 = df2.figure(fnum=fnum2, docla=True, doclf=True)
else:
fnum2 = fnum
for (rchip, kp, sift, fx, aid, info) in extracted_list:
px = viz_featrow.draw_feat_row(rchip,
fx,
kp,
sift,
fnum2,
nRows,
nCols,
px,
prevsift=prevsift,
aid=aid,
info=info)
prevsift = sift
if not same_fig:
ih.connect_callback(fig2, 'button_press_event', self.on_click)
df2.set_figtitle(figtitle + vh.get_vsstr(qaid, aid))
def print_weights(self, event=None):
scalars = self.infr.get_scalars()
for px, (key, vals) in enumerate(scalars.items()):
print(key + ' = ' + ut.get_stats_str(vals, use_nan=True))
def imgstats(img):
print('stats:')
print(' dtype = %r ' % (img.dtype,))
print(' ' + ut.get_stats_str(img, axis=None))
def imgstats(img):
print('stats:')
print(' dtype = %r ' % (img.dtype, ))
print(' ' + ut.get_stats_str(img, axis=None))
def select_ith_match(self, mx):
"""
Selects the ith match and visualizes and prints information concerning
features weights, keypoint details, and sift descriptions
"""
import plottool as pt
from plottool import viz_featrow
from plottool import interact_helpers as ih
# <CLOSURE VARS>
fnum = self.fnum
same_fig = self.same_fig
rchip1 = self.rchip1
rchip2 = self.rchip2
# </CLOSURE VARS>
self.mx = mx
print('+--- SELECT --- ')
print('... selecting mx-th=%r feature match' % mx)
fsv = self.fsv
fs = self.fs
print('score stats:')
print(ut.get_stats_str(fsv, axis=0, newlines=True))
print('fsv[mx] = %r' % (fsv[mx],))
print('fs[mx] = %r' % (fs[mx],))
#----------------------
# Get info for the select_ith_match plot
self.mode = 1
# Get the mx-th feature match
fx1, fx2 = self.fm[mx]
# Older info
fscore2 = self.fs[mx]
fk2 = None
kp1, kp2 = self.kpts1[fx1], self.kpts2[fx2]
vecs1, vecs2 = self.vecs1[fx1], self.vecs2[fx2]
info1 = '\nquery'
info2 = '\nk=%r fscore=%r' % (fk2, fscore2)
#self.last_fx = fx1
self.last_fx = fx1
# Extracted keypoints to draw
extracted_list = [(rchip1, kp1, vecs1, fx1, 'aid1', info1),
(rchip2, kp2, vecs2, fx2, 'aid2', info2)]
# Normalizng Keypoint
#if hasattr(cm, 'filt2_meta') and 'lnbnn' in cm.filt2_meta:
# qfx2_norm = cm.filt2_meta['lnbnn']
# # Normalizing chip and feature
# (aid3, fx3, normk) = qfx2_norm[fx1]
# rchip3 = ibs.get_annot_chips(aid3)
# kp3 = ibs.get_annot_kpts(aid3)[fx3]
# sift3 = ibs.get_annot_vecs(aid3)[fx3]
# info3 = '\nnorm %s k=%r' % (vh.get_aidstrs(aid3), normk)
# extracted_list.append((rchip3, kp3, sift3, fx3, aid3, info3))
#else:
# pass
# #print('WARNING: meta doesnt exist')
#----------------------
# Draw the select_ith_match plot
nRows, nCols = len(extracted_list) + same_fig, 3
# Draw matching chips and features
sel_fm = np.array([(fx1, fx2)])
pnum1 = (nRows, 1, 1) if same_fig else (1, 1, 1)
vert = self.vert if self.vert is not None else False
self.chipmatch_view(pnum=pnum1, ell_alpha=.4, ell_linewidth=1.8,
colors=pt.BLUE, sel_fm=sel_fm, vert=vert)
# Draw selected feature matches
px = nCols * same_fig # plot offset
prevsift = None
if not same_fig:
#fnum2 = fnum + len(viz.FNUMS)
fnum2 = self.fnum2
fig2 = pt.figure(fnum=fnum2, docla=True, doclf=True)
else:
fnum2 = fnum
for (rchip, kp, sift, fx, aid, info) in extracted_list:
px = viz_featrow.draw_feat_row(
rchip, fx, kp, sift, fnum2, nRows, nCols, px,
prevsift=prevsift, aid=aid, info=info)
prevsift = sift
if not same_fig:
ih.connect_callback(fig2, 'button_press_event', self.on_click)
def flann_add_time_experiment():
"""
builds plot of number of annotations vs indexer build time.
TODO: time experiment
CommandLine:
python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --show
python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_Master0 --show
utprof.py -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --show
valgrind --tool=memcheck --suppressions=valgrind-python.supp python -m wbia.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --no-with-reindex
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots._neighbor_experiment import * # NOQA
>>> import wbia
>>> #ibs = wbia.opendb('PZ_MTEST')
>>> result = flann_add_time_experiment()
>>> # verify results
>>> print(result)
>>> ut.show_if_requested()
"""
import wbia
import utool as ut
import numpy as np
import wbia.plottool as pt
def make_flann_index(vecs, flann_params):
flann = pyflann.FLANN()
flann.build_index(vecs, **flann_params)
return flann
db = ut.get_argval('--db')
ibs = wbia.opendb(db=db)
# Input
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
reindex_stride = 16
addition_stride = 4
max_ceiling = 120
elif ibs.get_dbname() == 'PZ_Master0':
# ibs = wbia.opendb(db='GZ_ALL')
initial = 32
reindex_stride = 32
addition_stride = 16
max_ceiling = 300001
else:
assert False
# max_ceiling = 32
all_daids = ibs.get_valid_aids()
max_num = min(max_ceiling, len(all_daids))
flann_params = vt.get_flann_params()
# Output
count_list, time_list_reindex = [], []
count_list2, time_list_addition = [], []
# Setup
# all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
all_randomize_daids_ = all_daids
# ensure all features are computed
ibs.get_annot_vecs(all_randomize_daids_)
def reindex_step(count, count_list, time_list_reindex):
daids = all_randomize_daids_[0:count]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann = make_flann_index(vecs, flann_params) # NOQA
count_list.append(count)
time_list_reindex.append(t.ellapsed)
def addition_step(count, flann, count_list2, time_list_addition):
daids = all_randomize_daids_[count:count + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann.add_points(vecs)
count_list2.append(count)
time_list_addition.append(t.ellapsed)
def make_initial_index(initial):
daids = all_randomize_daids_[0:initial + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
flann = make_flann_index(vecs, flann_params)
return flann
WITH_REINDEX = not ut.get_argflag('--no-with-reindex')
if WITH_REINDEX:
# Reindex Part
reindex_lbl = 'Reindexing'
_reindex_iter = range(1, max_num, reindex_stride)
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_lbl, freq=1)
for count in reindex_iter:
reindex_step(count, count_list, time_list_reindex)
# Add Part
flann = make_initial_index(initial)
addition_lbl = 'Addition'
_addition_iter = range(initial + 1, max_num, addition_stride)
addition_iter = ut.ProgressIter(_addition_iter, lbl=addition_lbl)
for count in addition_iter:
addition_step(count, flann, count_list2, time_list_addition)
logger.info('---')
logger.info('Reindex took time_list_reindex %.2s seconds' %
sum(time_list_reindex))
logger.info('Addition took time_list_reindex %.2s seconds' %
sum(time_list_addition))
logger.info('---')
statskw = dict(precision=2, newlines=True)
logger.info('Reindex stats ' +
ut.get_stats_str(time_list_reindex, **statskw))
logger.info('Addition stats ' +
ut.get_stats_str(time_list_addition, **statskw))
logger.info('Plotting')
# with pt.FigureContext:
next_fnum = iter(range(0, 2)).next # python3 PY3
pt.figure(fnum=next_fnum())
if WITH_REINDEX:
pt.plot2(
count_list,
time_list_reindex,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=reindex_lbl + ' Time',
dark=False,
)
# pt.figure(fnum=next_fnum())
pt.plot2(
count_list2,
time_list_addition,
marker='-o',
equal_aspect=False,
x_label='num_annotations',
label=addition_lbl + ' Time',
)
pt
pt.legend()
def get_dbinfo(ibs, verbose=True,
with_imgsize=False,
with_bytes=False,
with_contrib=False,
with_agesex=False,
with_header=True,
short=False,
tag='dbinfo',
aid_list=None):
"""
Returns dictionary of digestable database information
Infostr is a string summary of all the stats. Prints infostr in addition to
returning locals
Args:
ibs (IBEISController):
verbose (bool):
with_imgsize (bool):
with_bytes (bool):
Returns:
dict:
CommandLine:
python -m ibeis.other.dbinfo --exec-get_dbinfo:0
python -m ibeis.other.dbinfo --test-get_dbinfo:1
python -m ibeis.other.dbinfo --test-get_dbinfo:0 --db NNP_Master3
python -m ibeis.other.dbinfo --test-get_dbinfo:0 --db PZ_Master1
python -m ibeis.other.dbinfo --test-get_dbinfo:0 --db GZ_ALL
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 --db PZ_ViewPoints
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 --db GZ_Master1
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 -a ctrl
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 -a default:minqual=ok,require_timestamp=True --dbdir ~/lev/media/danger/LEWA
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 -a default:minqual=ok,require_timestamp=True --dbdir ~/lev/media/danger/LEWA --loadbackup=0
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 -a default: --dbdir ~/lev/media/danger/LEWA
python -m ibeis.other.dbinfo --exec-get_dbinfo:0 -a default: --dbdir ~/lev/media/danger/LEWA --loadbackup=0
Example1:
>>> # SCRIPT
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> defaultdb = 'testdb1'
>>> ibs, aid_list = ibeis.testdata_aids(defaultdb, a='default:minqual=ok,view=primary,view_ext1=1')
>>> kwargs = ut.get_kwdefaults(get_dbinfo)
>>> kwargs['verbose'] = False
>>> kwargs['aid_list'] = aid_list
>>> kwargs = ut.parse_dict_from_argv(kwargs)
>>> output = get_dbinfo(ibs, **kwargs)
>>> result = (output['info_str'])
>>> print(result)
>>> #ibs = ibeis.opendb(defaultdb='testdb1')
>>> # <HACK FOR FILTERING>
>>> #from ibeis.expt import cfghelpers
>>> #from ibeis.expt import annotation_configs
>>> #from ibeis.init import filter_annots
>>> #named_defaults_dict = ut.dict_take(annotation_configs.__dict__,
>>> # annotation_configs.TEST_NAMES)
>>> #named_qcfg_defaults = dict(zip(annotation_configs.TEST_NAMES,
>>> # ut.get_list_column(named_defaults_dict, 'qcfg')))
>>> #acfg = cfghelpers.parse_argv_cfg(('--annot-filter', '-a'), named_defaults_dict=named_qcfg_defaults, default=None)[0]
>>> #aid_list = ibs.get_valid_aids()
>>> # </HACK FOR FILTERING>
Example1:
>>> # ENABLE_DOCTEST
>>> from ibeis.other.dbinfo import * # NOQA
>>> import ibeis
>>> verbose = True
>>> short = True
>>> #ibs = ibeis.opendb(db='GZ_ALL')
>>> #ibs = ibeis.opendb(db='PZ_Master0')
>>> ibs = ibeis.opendb('testdb1')
>>> assert ibs.get_dbname() == 'testdb1', 'DO NOT DELETE CONTRIBUTORS OF OTHER DBS'
>>> ibs.delete_contributors(ibs.get_valid_contrib_rowids())
>>> ibs.delete_empty_nids()
>>> #ibs = ibeis.opendb(db='PZ_MTEST')
>>> output = get_dbinfo(ibs, with_contrib=False, verbose=False, short=True)
>>> result = (output['info_str'])
>>> print(result)
+============================
DB Info: testdb1
DB Notes: None
DB NumContrib: 0
----------
# Names = 7
# Names (unassociated) = 0
# Names (singleton) = 5
# Names (multiton) = 2
----------
# Annots = 13
# Annots (unknown) = 4
# Annots (singleton) = 5
# Annots (multiton) = 4
----------
# Img = 13
L============================
"""
# TODO Database size in bytes
# TODO: occurrence, contributors, etc...
# Basic variables
request_annot_subset = False
_input_aid_list = aid_list # NOQA
if aid_list is None:
valid_aids = ibs.get_valid_aids()
valid_nids = ibs.get_valid_nids()
valid_gids = ibs.get_valid_gids()
else:
if isinstance(aid_list, str):
# Hack to get experiment stats on aids
acfg_name_list = [aid_list]
print('Specified custom aids via acfgname %s' % (acfg_name_list,))
from ibeis.expt import experiment_helpers
acfg_list, expanded_aids_list = experiment_helpers.get_annotcfg_list(
ibs, acfg_name_list)
aid_list = sorted(list(set(ut.flatten(ut.flatten(expanded_aids_list)))))
#aid_list =
if verbose:
print('Specified %d custom aids' % (len(aid_list,)))
request_annot_subset = True
valid_aids = aid_list
valid_nids = list(
set(ibs.get_annot_nids(aid_list, distinguish_unknowns=False)) -
{const.UNKNOWN_NAME_ROWID}
)
valid_gids = list(set(ibs.get_annot_gids(aid_list)))
#associated_nids = ibs.get_valid_nids(filter_empty=True) # nids with at least one annotation
FILTER_HACK = True
if FILTER_HACK:
# HUGE HACK - get only images and names with filtered aids
valid_aids_ = ibs.filter_aids_custom(valid_aids)
valid_nids_ = ibs.filter_nids_custom(valid_nids)
valid_gids_ = ibs.filter_gids_custom(valid_gids)
if verbose:
print('Filtered %d names' % (len(valid_nids) - len(valid_nids_)))
print('Filtered %d images' % (len(valid_gids) - len(valid_gids_)))
print('Filtered %d annots' % (len(valid_aids) - len(valid_aids_)))
valid_gids = valid_gids_
valid_nids = valid_nids_
valid_aids = valid_aids_
#associated_nids = ut.compress(associated_nids, map(any,
#ibs.unflat_map(ibs.get_annot_custom_filterflags,
# ibs.get_name_aids(associated_nids))))
# Image info
if verbose:
print('Checking Image Info')
gx2_aids = ibs.get_image_aids(valid_gids)
if FILTER_HACK:
gx2_aids = [ibs.filter_aids_custom(aids) for aids in gx2_aids] # HACK FOR FILTER
if request_annot_subset:
# remove annots not in this subset
valid_aids_set = set(valid_aids)
gx2_aids = [list(set(aids).intersection(valid_aids_set)) for aids in gx2_aids]
gx2_nAnnots = np.array(list(map(len, gx2_aids)))
image_without_annots = len(np.where(gx2_nAnnots == 0)[0])
gx2_nAnnots_stats = ut.get_stats_str(gx2_nAnnots, newlines=True, use_median=True)
image_reviewed_list = ibs.get_image_reviewed(valid_gids)
# Name stats
if verbose:
print('Checking Name Info')
nx2_aids = ibs.get_name_aids(valid_nids)
if FILTER_HACK:
nx2_aids = [ibs.filter_aids_custom(aids) for aids in nx2_aids] # HACK FOR FILTER
if request_annot_subset:
# remove annots not in this subset
valid_aids_set = set(valid_aids)
nx2_aids = [list(set(aids).intersection(valid_aids_set)) for aids in nx2_aids]
associated_nids = ut.compress(valid_nids, list(map(len, nx2_aids)))
ibs.check_name_mapping_consistency(nx2_aids)
# Occurrence Info
def compute_annot_occurrence_ids(ibs, aid_list):
from ibeis.algo.preproc import preproc_occurrence
gid_list = ibs.get_annot_gids(aid_list)
gid2_aids = ut.group_items(aid_list, gid_list)
flat_imgsetids, flat_gids = preproc_occurrence.ibeis_compute_occurrences(ibs, gid_list, seconds_thresh=4 * 60 * 60, verbose=False)
occurid2_gids = ut.group_items(flat_gids, flat_imgsetids)
occurid2_aids = {oid: ut.flatten(ut.take(gid2_aids, gids)) for oid, gids in occurid2_gids.items()}
return occurid2_aids
import utool
with utool.embed_on_exception_context:
occurid2_aids = compute_annot_occurrence_ids(ibs, valid_aids)
occur_nids = ibs.unflat_map(ibs.get_annot_nids, occurid2_aids.values())
occur_unique_nids = [ut.unique(nids) for nids in occur_nids]
nid2_occurxs = ut.ddict(list)
for occurx, nids in enumerate(occur_unique_nids):
for nid in nids:
nid2_occurxs[nid].append(occurx)
nid2_occurx_single = {nid: occurxs for nid, occurxs in nid2_occurxs.items() if len(occurxs) <= 1}
nid2_occurx_resight = {nid: occurxs for nid, occurxs in nid2_occurxs.items() if len(occurxs) > 1}
singlesight_encounters = ibs.get_name_aids(nid2_occurx_single.keys())
singlesight_annot_stats = ut.get_stats(list(map(len, singlesight_encounters)), use_median=True, use_sum=True)
resight_name_stats = ut.get_stats(list(map(len, nid2_occurx_resight.values())), use_median=True, use_sum=True)
try:
aid_pairs = ibs.filter_aidpairs_by_tags(min_num=0)
undirected_tags = ibs.get_aidpair_tags(aid_pairs.T[0], aid_pairs.T[1], directed=False)
tagged_pairs = list(zip(aid_pairs.tolist(), undirected_tags))
tag_dict = ut.groupby_tags(tagged_pairs, undirected_tags)
pair_tag_info = ut.map_dict_vals(len, tag_dict)
num_reviewed_pairs = sum(ibs.get_annot_pair_is_reviewed(aid_pairs.T[0], aid_pairs.T[1]))
pair_tag_info['num_reviewed'] = num_reviewed_pairs
except Exception:
pair_tag_info = {}
#print(ut.dict_str(pair_tag_info))
# Annot Stats
# TODO: number of images where chips cover entire image
# TODO: total image coverage of annotation
# TODO: total annotation overlap
"""
ax2_unknown = ibs.is_aid_unknown(valid_aids)
ax2_nid = ibs.get_annot_name_rowids(valid_aids)
assert all([nid < 0 if unknown else nid > 0 for nid, unknown in
zip(ax2_nid, ax2_unknown)]), 'bad annot nid'
"""
#
if verbose:
print('Checking Annot Species')
unknown_aids = ut.compress(valid_aids, ibs.is_aid_unknown(valid_aids))
species_list = ibs.get_annot_species_texts(valid_aids)
species2_aids = ut.group_items(valid_aids, species_list)
species2_nAids = {key: len(val) for key, val in species2_aids.items()}
if verbose:
print('Checking Multiton/Singleton Species')
nx2_nAnnots = np.array(list(map(len, nx2_aids)))
# Seperate singleton / multitons
multiton_nxs = np.where(nx2_nAnnots > 1)[0]
singleton_nxs = np.where(nx2_nAnnots == 1)[0]
unassociated_nxs = np.where(nx2_nAnnots == 0)[0]
assert len(np.intersect1d(singleton_nxs, multiton_nxs)) == 0, 'intersecting names'
valid_nxs = np.hstack([multiton_nxs, singleton_nxs])
num_names_with_gt = len(multiton_nxs)
# Annot Info
if verbose:
print('Checking Annot Info')
multiton_aids_list = ut.take(nx2_aids, multiton_nxs)
assert len(set(multiton_nxs)) == len(multiton_nxs)
if len(multiton_aids_list) == 0:
multiton_aids = np.array([], dtype=np.int)
else:
multiton_aids = np.hstack(multiton_aids_list)
assert len(set(multiton_aids)) == len(multiton_aids), 'duplicate annot'
singleton_aids = ut.take(nx2_aids, singleton_nxs)
multiton_nid2_nannots = list(map(len, multiton_aids_list))
# Image size stats
if with_imgsize:
if verbose:
print('Checking ImageSize Info')
gpath_list = ibs.get_image_paths(valid_gids)
def wh_print_stats(wh_list):
if len(wh_list) == 0:
return '{empty}'
wh_list = np.asarray(wh_list)
stat_dict = OrderedDict(
[( 'max', wh_list.max(0)),
( 'min', wh_list.min(0)),
('mean', wh_list.mean(0)),
( 'std', wh_list.std(0))])
def arr2str(var):
return ('[' + (
', '.join(list(map(lambda x: '%.1f' % x, var)))
) + ']')
ret = (',\n '.join([
'%s:%s' % (key, arr2str(val))
for key, val in stat_dict.items()
]))
return '{\n ' + ret + '\n}'
print('reading image sizes')
# Image size stats
img_size_list = ibs.get_image_sizes(valid_gids)
img_size_stats = wh_print_stats(img_size_list)
# Chip size stats
annotation_bbox_list = ibs.get_annot_bboxes(valid_aids)
annotation_bbox_arr = np.array(annotation_bbox_list)
if len(annotation_bbox_arr) == 0:
annotation_size_list = []
else:
annotation_size_list = annotation_bbox_arr[:, 2:4]
chip_size_stats = wh_print_stats(annotation_size_list)
imgsize_stat_lines = [
(' # Img in dir = %d' % len(gpath_list)),
(' Image Size Stats = %s' % (img_size_stats,)),
(' * Chip Size Stats = %s' % (chip_size_stats,)),
]
else:
imgsize_stat_lines = []
if verbose:
print('Building Stats String')
multiton_stats = ut.get_stats_str(multiton_nid2_nannots, newlines=True, use_median=True)
# Time stats
unixtime_list = ibs.get_image_unixtime(valid_gids)
unixtime_list = ut.list_replace(unixtime_list, -1, float('nan'))
#valid_unixtime_list = [time for time in unixtime_list if time != -1]
#unixtime_statstr = ibs.get_image_time_statstr(valid_gids)
if ut.get_argflag('--hackshow-unixtime'):
show_time_distributions(ibs, unixtime_list)
ut.show_if_requested()
unixtime_statstr = ut.get_timestats_str(unixtime_list, newlines=True, full=True)
# GPS stats
gps_list_ = ibs.get_image_gps(valid_gids)
gpsvalid_list = [gps != (-1, -1) for gps in gps_list_]
gps_list = ut.compress(gps_list_, gpsvalid_list)
def get_annot_age_stats(aid_list):
annot_age_months_est_min = ibs.get_annot_age_months_est_min(aid_list)
annot_age_months_est_max = ibs.get_annot_age_months_est_max(aid_list)
age_dict = ut.ddict((lambda : 0))
for min_age, max_age in zip(annot_age_months_est_min, annot_age_months_est_max):
if (min_age is None or min_age < 12) and max_age < 12:
age_dict['Infant'] += 1
elif 12 <= min_age and min_age < 36 and 12 <= max_age and max_age < 36:
age_dict['Juvenile'] += 1
elif 36 <= min_age and (36 <= max_age or max_age is None):
age_dict['Adult'] += 1
else:
print('Found UNKNOWN Age: %r, %r' % (min_age, max_age, ))
age_dict['UNKNOWN'] += 1
return age_dict
def get_annot_sex_stats(aid_list):
annot_sextext_list = ibs.get_annot_sex_texts(aid_list)
sextext2_aids = ut.group_items(aid_list, annot_sextext_list)
sex_keys = list(ibs.const.SEX_TEXT_TO_INT.keys())
assert set(sex_keys) >= set(annot_sextext_list), 'bad keys: ' + str(set(annot_sextext_list) - set(sex_keys))
sextext2_nAnnots = ut.odict([(key, len(sextext2_aids.get(key, []))) for key in sex_keys])
# Filter 0's
sextext2_nAnnots = {key: val for key, val in six.iteritems(sextext2_nAnnots) if val != 0}
return sextext2_nAnnots
if verbose:
print('Checking Other Annot Stats')
qualtext2_nAnnots = ibs.get_annot_qual_stats(valid_aids)
yawtext2_nAnnots = ibs.get_annot_yaw_stats(valid_aids)
agetext2_nAnnots = get_annot_age_stats(valid_aids)
sextext2_nAnnots = get_annot_sex_stats(valid_aids)
if verbose:
print('Checking Contrib Stats')
# Contributor Statistics
# hack remove colon for image alignment
def fix_tag_list(tag_list):
return [None if tag is None else tag.replace(':', ';') for tag in tag_list]
image_contrib_tags = fix_tag_list(ibs.get_image_contributor_tag(valid_gids))
annot_contrib_tags = fix_tag_list(ibs.get_annot_image_contributor_tag(valid_aids))
contrib_tag_to_gids = ut.group_items(valid_gids, image_contrib_tags)
contrib_tag_to_aids = ut.group_items(valid_aids, annot_contrib_tags)
contrib_tag_to_qualstats = {key: ibs.get_annot_qual_stats(aids) for key, aids in six.iteritems(contrib_tag_to_aids)}
contrib_tag_to_viewstats = {key: ibs.get_annot_yaw_stats(aids) for key, aids in six.iteritems(contrib_tag_to_aids)}
contrib_tag_to_nImages = {key: len(val) for key, val in six.iteritems(contrib_tag_to_gids)}
contrib_tag_to_nAnnots = {key: len(val) for key, val in six.iteritems(contrib_tag_to_aids)}
if verbose:
print('Summarizing')
# Summarize stats
num_names = len(valid_nids)
num_names_unassociated = len(valid_nids) - len(associated_nids)
num_names_singleton = len(singleton_nxs)
num_names_multiton = len(multiton_nxs)
num_singleton_annots = len(singleton_aids)
num_multiton_annots = len(multiton_aids)
num_unknown_annots = len(unknown_aids)
num_annots = len(valid_aids)
if with_bytes:
if verbose:
print('Checking Disk Space')
ibsdir_space = ut.byte_str2(ut.get_disk_space(ibs.get_ibsdir()))
dbdir_space = ut.byte_str2(ut.get_disk_space(ibs.get_dbdir()))
imgdir_space = ut.byte_str2(ut.get_disk_space(ibs.get_imgdir()))
cachedir_space = ut.byte_str2(ut.get_disk_space(ibs.get_cachedir()))
if True:
if verbose:
print('Check asserts')
try:
bad_aids = np.intersect1d(multiton_aids, unknown_aids)
_num_names_total_check = num_names_singleton + num_names_unassociated + num_names_multiton
_num_annots_total_check = num_unknown_annots + num_singleton_annots + num_multiton_annots
assert len(bad_aids) == 0, 'intersecting multiton aids and unknown aids'
assert _num_names_total_check == num_names, 'inconsistent num names'
#if not request_annot_subset:
# dont check this if you have an annot subset
assert _num_annots_total_check == num_annots, 'inconsistent num annots'
except Exception as ex:
ut.printex(ex, keys=[
'_num_names_total_check',
'num_names',
'_num_annots_total_check',
'num_annots',
'num_names_singleton',
'num_names_multiton',
'num_unknown_annots',
'num_multiton_annots',
'num_singleton_annots',
])
raise
# Get contributor statistics
contrib_rowids = ibs.get_valid_contrib_rowids()
num_contributors = len(contrib_rowids)
# print
num_tabs = 5
def align2(str_):
return ut.align(str_, ':', ' :')
def align_dict2(dict_):
str_ = ut.dict_str(dict_)
return align2(str_)
header_block_lines = (
[('+============================'), ] + (
[
('+ singleton := single sighting'),
('+ multiton := multiple sightings'),
('--' * num_tabs),
] if not short and with_header else []
)
)
source_block_lines = [
('DB Info: ' + ibs.get_dbname()),
('DB Notes: ' + ibs.get_dbnotes()),
('DB NumContrib: %d' % num_contributors),
]
bytes_block_lines = [
('--' * num_tabs),
('DB Bytes: '),
(' +- dbdir nBytes: ' + dbdir_space),
(' | +- _ibsdb nBytes: ' + ibsdir_space),
(' | | +-imgdir nBytes: ' + imgdir_space),
(' | | +-cachedir nBytes: ' + cachedir_space),
] if with_bytes else []
name_block_lines = [
('--' * num_tabs),
('# Names = %d' % num_names),
('# Names (unassociated) = %d' % num_names_unassociated),
('# Names (singleton) = %d' % num_names_singleton),
('# Names (multiton) = %d' % num_names_multiton),
]
subset_str = ' ' if not request_annot_subset else '(SUBSET)'
annot_block_lines = [
('--' * num_tabs),
('# Annots %s = %d' % (subset_str, num_annots,)),
('# Annots (unknown) = %d' % num_unknown_annots),
('# Annots (singleton) = %d' % num_singleton_annots),
('# Annots (multiton) = %d' % num_multiton_annots),
]
annot_per_basic_block_lines = [
('--' * num_tabs),
('# Annots per Name (multiton) = %s' % (align2(multiton_stats),)),
('# Annots per Image = %s' % (align2(gx2_nAnnots_stats),)),
('# Annots per Species = %s' % (align_dict2(species2_nAids),)),
] if not short else []
occurrence_block_lines = [
('--' * num_tabs),
('# Occurrence Per Name (Resights) = %s' % (align_dict2(resight_name_stats),)),
('# Annots per Encounter (Singlesights) = %s' % (align_dict2(singlesight_annot_stats),)),
('# Pair Tag Info (annots) = %s' % (align_dict2(pair_tag_info),)),
] if not short else []
annot_per_qualview_block_lines = [
None if short else '# Annots per Viewpoint = %s' % align_dict2(yawtext2_nAnnots),
None if short else '# Annots per Quality = %s' % align_dict2(qualtext2_nAnnots),
]
annot_per_agesex_block_lines = [
'# Annots per Age = %s' % align_dict2(agetext2_nAnnots),
'# Annots per Sex = %s' % align_dict2(sextext2_nAnnots),
] if not short and with_agesex else []
contrib_block_lines = [
'# Images per contributor = ' + align_dict2(contrib_tag_to_nImages),
'# Annots per contributor = ' + align_dict2(contrib_tag_to_nAnnots),
'# Quality per contributor = ' + ut.dict_str(contrib_tag_to_qualstats, sorted_=True),
'# Viewpoint per contributor = ' + ut.dict_str(contrib_tag_to_viewstats, sorted_=True),
] if with_contrib else []
img_block_lines = [
('--' * num_tabs),
('# Img = %d' % len(valid_gids)),
None if short else ('# Img reviewed = %d' % sum(image_reviewed_list)),
None if short else ('# Img with gps = %d' % len(gps_list)),
#('# Img with timestamp = %d' % len(valid_unixtime_list)),
None if short else ('Img Time Stats = %s' % (align2(unixtime_statstr),)),
]
info_str_lines = (
header_block_lines +
bytes_block_lines +
source_block_lines +
name_block_lines +
annot_block_lines +
annot_per_basic_block_lines +
occurrence_block_lines +
annot_per_qualview_block_lines +
annot_per_agesex_block_lines +
img_block_lines +
contrib_block_lines +
imgsize_stat_lines +
[('L============================'), ]
)
info_str = '\n'.join(ut.filter_Nones(info_str_lines))
info_str2 = ut.indent(info_str, '[{tag}]'.format(tag=tag))
if verbose:
print(info_str2)
locals_ = locals()
return locals_
def select_ith_match(self, mx):
"""
Selects the ith match and visualizes and prints information concerning
features weights, keypoint details, and sift descriptions
Args:
mx (int) - the ith match to visualize
qaid (int) - query annotation id
aid (int) - database annotation id
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-select_ith_match --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact(mx=1)
>>> pt.show_if_requested()
"""
ibs = self.ibs
qaid = self.qaid
aid = self.daid
fnum = self.fnum
figtitle = self.figtitle
rchip1 = self.rchip1
rchip2 = self.rchip2
aid = self.daid
same_fig = self.same_fig
self.mx = mx
print('+--- SELECT --- ')
print('qaid=%r, daid=%r' % (qaid, aid))
print('... selecting mx-th=%r feature match' % mx)
if False:
print('score stats:')
print(ut.get_stats_str(self.fsv, axis=0, newlines=True))
print('fsv[mx] = %r' % (self.fsv[mx],))
print('fs[mx] = %r' % (self.fs[mx],))
"""
# test feature weights of actual chips
fx1, fx2 = fm[mx]
daid = aid
ibs.get_annot_fgweights([daid])[0][fx2]
ibs.get_annot_fgweights([qaid])[0][fx1]
"""
#----------------------
# Get info for the select_ith_match plot
self.mode = 1
# Get the mx-th feature match
fx1, fx2 = self.fm[mx]
fscore2 = self.fs[mx]
fk2 = self.fk[mx]
kpts1 = ibs.get_annot_kpts([self.qaid], config2_=self.query_config2_)[0]
kpts2 = ibs.get_annot_kpts([self.daid], config2_=self.data_config2_)[0]
desc1 = ibs.get_annot_vecs([self.qaid], config2_=self.query_config2_)[0]
desc2 = ibs.get_annot_vecs([self.daid], config2_=self.data_config2_)[0]
kp1, kp2 = kpts1[fx1], kpts2[fx2]
sift1, sift2 = desc1[fx1], desc2[fx2]
info1 = '\nquery'
info2 = '\nk=%r fscore=%r' % (fk2, fscore2)
#last_state.last_fx = fx1
self.last_fx = fx1
# Extracted keypoints to draw
extracted_list = [(rchip1, kp1, sift1, fx1, self.qaid, info1),
(rchip2, kp2, sift2, fx2, self.daid, info2)]
# Normalizng Keypoint
#if hasattr(cm, 'filt2_meta') and 'lnbnn' in cm.filt2_meta:
# qfx2_norm = cm.filt2_meta['lnbnn']
# # Normalizing chip and feature
# (aid3, fx3, normk) = qfx2_norm[fx1]
# rchip3 = ibs.get_annot_chips(aid3)
# kp3 = ibs.get_annot_kpts(aid3)[fx3]
# sift3 = ibs.get_annot_vecs(aid3)[fx3]
# info3 = '\nnorm %s k=%r' % (vh.get_aidstrs(aid3), normk)
# extracted_list.append((rchip3, kp3, sift3, fx3, aid3, info3))
#else:
# pass
# print('WARNING: meta doesnt exist')
#----------------------
# Draw the select_ith_match plot
nRows, nCols = len(extracted_list) + same_fig, 3
# Draw matching chips and features
sel_fm = np.array([(fx1, fx2)])
pnum1 = (nRows, 1, 1) if same_fig else (1, 1, 1)
vert = self.vert if self.vert is not None else False
self.chipmatch_view(pnum1, ell_alpha=.4, ell_linewidth=1.8,
colors=df2.BLUE, sel_fm=sel_fm, vert=vert)
# Draw selected feature matches
px = nCols * same_fig # plot offset
prevsift = None
if not same_fig:
#fnum2 = fnum + len(viz.FNUMS)
fnum2 = self.fnum2
fig2 = df2.figure(fnum=fnum2, docla=True, doclf=True)
else:
fnum2 = fnum
for (rchip, kp, sift, fx, aid, info) in extracted_list:
px = viz_featrow.draw_feat_row(rchip, fx, kp, sift, fnum2, nRows, nCols, px,
prevsift=prevsift, aid=aid, info=info)
prevsift = sift
if not same_fig:
ih.connect_callback(fig2, 'button_press_event', self.on_click)
df2.set_figtitle(figtitle + vh.get_vsstr(qaid, aid))
def viz_annot_with_metrics(ibs,
invindex,
aid,
metrics,
metric_keys=[
'wx2_nMembers', ('wx2_pdist_stats', 'mean'),
('wx2_wdist_stats', 'mean')
],
show_orig=True,
show_idf=True,
show_words=False,
show_analysis=True,
show_aveprecision=True,
qfx2_closest_k_list=None,
show_word_correct_assignments=False,
qres_list=None):
"""
Args:
ibs (IBEISController):
invindex (InvertedIndex): object for fast vocab lookup
aid (int):
metrics (namedtuple):
Example:
>>> from ibeis.algo.hots.smk.smk_plots import * # NOQA
>>> from ibeis.algo.hots.smk import smk_debug
>>> from ibeis.algo.hots.smk import smk_repr
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=64000)
>>> #tup = smk_debug.testdata_raw_internals0(db='GZ_ALL', nWords=8000)
>>> #tup = smk_debug.testdata_raw_internals0(db='PZ_Master0', nWords=64000)
>>> tup = smk_debug.testdata_raw_internals0(db='PZ_Mothers', nWords=8000)
>>> ibs, annots_df, daids, qaids, invindex, qreq_ = tup
>>> smk_repr.compute_data_internals_(invindex, qreq_.qparams, delete_rawvecs=False)
>>> invindex.idx2_wxs = np.array(invindex.idx2_wxs)
>>> metric_keys=['wx2_nMembers', ('wx2_pdist_stats', 'mean'), ('wx2_wdist_stats', 'mean')]
>>> metrics = compute_word_metrics(invindex)
>>> aid = 1
"""
#viz_chip.rrr()
#df2.rrr()
kpts = ibs.get_annot_kpts(aid)
if ut.VERBOSE:
ut.super_print(kpts)
if show_word_correct_assignments or show_idf:
# Get only the first assigned word
# FIXME: need to look at multi-assignment
_mask = invindex.idx2_daid == aid
fxs = invindex.idx2_dfx[_mask]
wxs = invindex.idx2_wxs[_mask].T[0].T
assert len(fxs) == len(kpts)
assert len(fxs) == len(wxs)
fnum = 1
dbname = ibs.get_dbname()
def _plot(metric,
fnum=1,
lbl='',
annote=True,
darken=.1,
colortype='score',
**kwargs):
print('ploting fnum=%r' % fnum)
#lblaug = ' db=%r, nWords = %r' % (dbname, nWords)
lblaug = ' db=%r' % (dbname)
figtitle = lbl + lblaug
lbl = lbl
plot_chip_metric(ibs,
aid,
metric=metric,
fnum=fnum,
lbl=lbl,
figtitle=figtitle,
annote=annote,
darken=darken,
colortype=colortype,
**kwargs)
return fnum + 1
# Original Plot
if show_orig:
fnum = _plot(None,
fnum=fnum,
lbl='Orig Chip',
annote=False,
darken=None)
# IDF Plot
if show_idf:
idf_list = np.array(list(ut.dict_take_gen(invindex.wx2_idf, wxs)))
fnum = _plot(idf_list, fnum=fnum, lbl='IDF')
print('stats(idf_list) = ' + ut.get_stats_str(idf_list))
# Word Plot
if show_words:
fnum = _plot(wxs, fnum=fnum, lbl='Words', colortype='label')
# LNBNN Result Plots
if qfx2_closest_k_list is not None:
for qres, qfx2_closest_k in zip(qres_list, qfx2_closest_k_list):
print(' --- qres item ---')
if qres is not None:
from ibeis.algo.hots.hots_query_result import QueryResult
assert isinstance(qres, QueryResult)
if show_analysis:
qres.show_analysis(ibs=ibs,
fnum=fnum,
figtitle=qres.make_smaller_title())
fnum += 1
if show_aveprecision:
qres.show_precision_recall_curve(ibs=ibs, fnum=fnum)
fnum += 1
if qfx2_closest_k is not None:
# Plot ranked positions
qfx2_closest_k = np.array(qfx2_closest_k)
qfx2_closest_k_qeq0 = qfx2_closest_k[qfx2_closest_k >= 0]
qfx2_closest_k_lt0 = qfx2_closest_k[qfx2_closest_k < 0]
print('stats(qfx2_closest_k_qeq0) = ' +
ut.get_stats_str(qfx2_closest_k_qeq0))
print('stats(qfx2_closest_k_lt0) = ' +
ut.get_stats_str(qfx2_closest_k_lt0))
fnum = _plot(qfx2_closest_k,
fnum=fnum,
lbl='Correct Ranks ' + qres.make_smaller_title(),
colortype='custom',
reverse_cmap=True)
# Correct word assignment plots
if show_word_correct_assignments:
unique_wxs, unique_inverse = np.unique(wxs, return_inverse=True)
# Get the aids that belong to each word
_idxs_list = ut.dict_take(invindex.wx2_idxs, unique_wxs)
_aids_list = [invindex.idx2_daid.take(idxs) for idxs in _idxs_list]
# Check if this word will provide a correct assignment -
# two ground truth chip exist within the same word
gt_aids = np.array(ibs.get_annot_groundtruth(aid))
_hastp_list = np.array(
[len(np.intersect1d(aids, gt_aids)) > 0 for aids in _aids_list])
# Map back to the space of features
# mark each feature match as having a correct word mapping or not
hascorrectmatch = _hastp_list[unique_inverse]
hascorrectmatch_ = hascorrectmatch.astype(np.int32) * 3 - 2
lbl = 'Correct Words ' + qres.make_smaller_title(
) + '\n Yellow means the word contains a correct match in the word\'s invindex. Blue is the opposite.'
fnum = _plot(hascorrectmatch_,
fnum=fnum,
lbl=lbl,
colortype='custom',
reverse_cmap=False)
# Word Metric Plots
for count, metrickey in enumerate(metric_keys):
if isinstance(metrickey, tuple):
#lbl = repr(metrickey)
def fixstr(str_):
return str_.replace('wx2_', '').replace('_stats', '')
lbl = '%s(%s)' % (metrickey[1].upper(), fixstr(metrickey[0]))
else:
lbl = str(metrickey)
metric_list = metric_clamped_stat(metrics, wxs, metrickey)
fnum = _plot(metric_list, fnum=fnum, lbl=lbl)
def flann_add_time_experiment():
"""
builds plot of number of annotations vs indexer build time.
TODO: time experiment
CommandLine:
python -m ibeis.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --show
python -m ibeis.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_Master0 --show
utprof.py -m ibeis.algo.hots._neighbor_experiment --test-flann_add_time_experiment --show
valgrind --tool=memcheck --suppressions=valgrind-python.supp python -m ibeis.algo.hots._neighbor_experiment --test-flann_add_time_experiment --db PZ_MTEST --no-with-reindex
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots._neighbor_experiment import * # NOQA
>>> import ibeis
>>> #ibs = ibeis.opendb('PZ_MTEST')
>>> result = flann_add_time_experiment()
>>> # verify results
>>> print(result)
>>> ut.show_if_requested()
"""
import ibeis
import utool as ut
import numpy as np
import plottool as pt
def make_flann_index(vecs, flann_params):
flann = pyflann.FLANN()
flann.build_index(vecs, **flann_params)
return flann
db = ut.get_argval('--db')
ibs = ibeis.opendb(db=db)
# Input
if ibs.get_dbname() == 'PZ_MTEST':
initial = 1
reindex_stride = 16
addition_stride = 4
max_ceiling = 120
elif ibs.get_dbname() == 'PZ_Master0':
#ibs = ibeis.opendb(db='GZ_ALL')
initial = 32
reindex_stride = 32
addition_stride = 16
max_ceiling = 300001
else:
assert False
#max_ceiling = 32
all_daids = ibs.get_valid_aids()
max_num = min(max_ceiling, len(all_daids))
flann_params = ibs.cfg.query_cfg.flann_cfg.get_flann_params()
# Output
count_list, time_list_reindex = [], []
count_list2, time_list_addition = [], []
# Setup
#all_randomize_daids_ = ut.deterministic_shuffle(all_daids[:])
all_randomize_daids_ = all_daids
# ensure all features are computed
ibs.get_annot_vecs(all_randomize_daids_)
def reindex_step(count, count_list, time_list_reindex):
daids = all_randomize_daids_[0:count]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann = make_flann_index(vecs, flann_params) # NOQA
count_list.append(count)
time_list_reindex.append(t.ellapsed)
def addition_step(count, flann, count_list2, time_list_addition):
daids = all_randomize_daids_[count:count + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
with ut.Timer(verbose=False) as t:
flann.add_points(vecs)
count_list2.append(count)
time_list_addition.append(t.ellapsed)
def make_initial_index(initial):
daids = all_randomize_daids_[0:initial + 1]
vecs = np.vstack(ibs.get_annot_vecs(daids))
flann = make_flann_index(vecs, flann_params)
return flann
WITH_REINDEX = not ut.get_argflag('--no-with-reindex')
if WITH_REINDEX:
# Reindex Part
reindex_lbl = 'Reindexing'
_reindex_iter = range(1, max_num, reindex_stride)
reindex_iter = ut.ProgressIter(_reindex_iter, lbl=reindex_lbl, freq=1)
for count in reindex_iter:
reindex_step(count, count_list, time_list_reindex)
# Add Part
flann = make_initial_index(initial)
addition_lbl = 'Addition'
_addition_iter = range(initial + 1, max_num, addition_stride)
addition_iter = ut.ProgressIter(_addition_iter, lbl=addition_lbl)
for count in addition_iter:
addition_step(count, flann, count_list2, time_list_addition)
print('---')
print('Reindex took time_list_reindex %.2s seconds' % sum(time_list_reindex))
print('Addition took time_list_reindex %.2s seconds' % sum(time_list_addition))
print('---')
statskw = dict(precision=2, newlines=True)
print('Reindex stats ' + ut.get_stats_str(time_list_reindex, **statskw))
print('Addition stats ' + ut.get_stats_str(time_list_addition, **statskw))
print('Plotting')
#with pt.FigureContext:
next_fnum = iter(range(0, 2)).next # python3 PY3
pt.figure(fnum=next_fnum())
if WITH_REINDEX:
pt.plot2(count_list, time_list_reindex, marker='-o', equal_aspect=False,
x_label='num_annotations', label=reindex_lbl + ' Time', dark=False)
#pt.figure(fnum=next_fnum())
pt.plot2(count_list2, time_list_addition, marker='-o', equal_aspect=False,
x_label='num_annotations', label=addition_lbl + ' Time')
pt
pt.legend()
