def plot_image(self, index):
px = index - self.start_index
gpath = self.gpath_list[index]
if self.vizkw is None:
_vizkw = {}
else:
_vizkw = self.vizkw.copy()
_vizkw.update({
'fnum': self.fnum,
'pnum': self.pnum_(px),
})
if ut.is_funclike(gpath):
showfunc = gpath
# HACK
# override of plot image function
showfunc(**_vizkw)
import plottool as pt
ax = pt.gca()
else:
if isinstance(gpath, six.string_types):
img = vt.imread(gpath)
else:
img = gpath
bbox_list = self.bboxes_list[index]
#print('bbox_list %r in display for px: %r ' % (bbox_list, px))
theta_list = self.thetas_list[index]
label_list = [ix + 1 for ix in range(len(bbox_list))]
#Add true values for every bbox to display
sel_list = [True for ix in range(len(bbox_list))]
_vizkw.update({
#title should always be the image number
'title': str(index),
'bbox_list': bbox_list,
'theta_list': theta_list,
'sel_list': sel_list,
'label_list': label_list,
})
#print(utool.dict_str(_vizkw))
#print('vizkw = ' + utool.dict_str(_vizkw))
_, ax = viz_image2.show_image(img, **_vizkw)
if self.xlabel_list is not None:
import plottool as pt
pt.set_xlabel(self.xlabel_list[index])
#print(index)
ph.set_plotdat(ax, 'bbox_list', bbox_list)
ph.set_plotdat(ax, 'gpath', gpath)
ph.set_plotdat(ax, 'px', str(px))
ph.set_plotdat(ax, 'index', index)
def plot_image(self, index):
px = index - self.start_index
gpath = self.gpath_list[index]
if self.vizkw is None:
_vizkw = {}
else:
_vizkw = self.vizkw.copy()
_vizkw.update({
'fnum': self.fnum,
'pnum': self.pnum_(px),
})
if ut.is_funclike(gpath):
showfunc = gpath
# HACK
# override of plot image function
showfunc(**_vizkw)
import plottool as pt
ax = pt.gca()
else:
if isinstance(gpath, six.string_types):
img = vt.imread(gpath)
else:
img = gpath
bbox_list = self.bboxes_list[index]
#print('bbox_list %r in display for px: %r ' % (bbox_list, px))
theta_list = self.thetas_list[index]
label_list = [ix + 1 for ix in range(len(bbox_list))]
#Add true values for every bbox to display
sel_list = [True for ix in range(len(bbox_list))]
_vizkw.update({
#title should always be the image number
'title': str(index),
'bbox_list' : bbox_list,
'theta_list' : theta_list,
'sel_list' : sel_list,
'label_list' : label_list,
})
#print(utool.dict_str(_vizkw))
#print('vizkw = ' + utool.dict_str(_vizkw))
_, ax = viz_image2.show_image(img, **_vizkw)
if self.xlabel_list is not None:
import plottool as pt
pt.set_xlabel(self.xlabel_list[index])
#print(index)
ph.set_plotdat(ax, 'bbox_list', bbox_list)
ph.set_plotdat(ax, 'gpath', gpath)
ph.set_plotdat(ax, 'px', str(px))
ph.set_plotdat(ax, 'index', index)
def find_opt_ratio(pblm):
"""
script to help find the correct value for the ratio threshold
>>> from ibeis.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 plottool as pt
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 show_name_matches(ibs,
qaid,
name_daid_list,
name_fm_list,
name_fs_list,
name_H1_list,
name_featflag_list,
qreq_=None,
**kwargs):
"""
Called from chip_match.py
Args:
ibs (IBEISController): ibeis controller object
qaid (int): query annotation id
name_daid_list (list):
name_fm_list (list):
name_fs_list (list):
name_H1_list (list):
name_featflag_list (list):
qreq_ (QueryRequest): query request object with hyper-parameters(default = None)
Kwargs:
draw_fmatches, name_rank, fnum, pnum, colorbar_, nonvote_mode,
fastmode, show_matches, fs, fm_norm, lbl1, lbl2, rect, draw_border,
cmap, H1, H2, scale_factor1, scale_factor2, draw_pts, draw_ell,
draw_lines, show_nMatches, all_kpts, in_image, show_query, draw_lbl,
name_annot_scores, score, rawscore, aid2_raw_rank, show_name,
show_nid, show_aid, show_annot_score, show_truth, name_score,
show_name_score, show_name_rank, show_timedelta
CommandLine:
python -m ibeis.viz.viz_matches --exec-show_name_matches
python -m ibeis.viz.viz_matches --test-show_name_matches --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_matches import * # NOQA
>>> from ibeis.algo.hots import chip_match
>>> from ibeis.algo.hots import name_scoring
>>> import vtool as vt
>>> from ibeis.algo.hots import _pipeline_helpers as plh # NOQA
>>> import numpy as np
>>> func = chip_match.ChipMatch.show_single_namematch
>>> sourcecode = ut.get_func_sourcecode(func, stripdef=True, stripret=True,
>>> strip_docstr=True)
>>> setup = ut.regex_replace('viz_matches.show_name_matches', '#', sourcecode)
>>> homog = False
>>> print(ut.indent(setup, '>>> '))
>>> ibs, qreq_, cm_list = plh.testdata_post_sver('PZ_MTEST', qaid_list=[1])
>>> cm = cm_list[0]
>>> cm.score_nsum(qreq_)
>>> dnid = ibs.get_annot_nids(cm.qaid)
>>> # +--- COPIED SECTION
>>> locals_ = locals()
>>> var_list = ut.exec_func_src(
>>> func, locals_=locals_,
>>> sentinal='name_annot_scores = cm.annot_score_list.take(sorted_groupxs')
>>> exec(ut.execstr_dict(var_list))
>>> # L___ COPIED SECTION
>>> kwargs = {}
>>> show_name_matches(ibs, qaid, name_daid_list, name_fm_list,
>>> name_fs_list, name_h1_list, name_featflag_list,
>>> qreq_=qreq_, **kwargs)
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
#print("SHOW NAME MATCHES")
#print(ut.repr2(kwargs, nl=True))
#from ibeis import constants as const
from ibeis import tag_funcs
draw_fmatches = kwargs.pop('draw_fmatches', True)
rchip1, kpts1 = get_query_annot_pair_info(ibs, qaid, qreq_, draw_fmatches)
rchip2_list, kpts2_list = get_data_annot_pair_info(ibs, name_daid_list,
qreq_, draw_fmatches)
fm_list = name_fm_list
fs_list = name_fs_list
featflag_list = name_featflag_list
offset_list, sf_list, bbox_list = show_multichip_match(
rchip1, rchip2_list, kpts1, kpts2_list, fm_list, fs_list,
featflag_list, **kwargs)
aid_list = [qaid] + name_daid_list
annotate_matches3(ibs,
aid_list,
bbox_list,
offset_list,
name_fm_list,
name_fs_list,
qreq_=None,
**kwargs)
ax = pt.gca()
title = vh.get_query_text(ibs,
None,
name_daid_list,
False,
qaid=qaid,
**kwargs)
pt.set_title(title, ax)
# Case tags
annotmatch_rowid_list = ibs.get_annotmatch_rowid_from_superkey(
[qaid] * len(name_daid_list), name_daid_list)
annotmatch_rowid_list = ut.filter_Nones(annotmatch_rowid_list)
tags_list = ibs.get_annotmatch_case_tags(annotmatch_rowid_list)
if not ut.get_argflag('--show'): # False:
tags_list = tag_funcs.consolodate_annotmatch_tags(tags_list)
tag_list = ut.unique_ordered(ut.flatten(tags_list))
name_rank = kwargs.get('name_rank', None)
truth = get_multitruth(ibs, aid_list)
xlabel = {1: 'Correct ID', 0: 'Incorrect ID', 2: 'Unknown ID'}[truth]
if False:
if name_rank is None:
xlabel = {1: 'Genuine', 0: 'Imposter', 2: 'Unknown'}[truth]
#xlabel = {1: 'True', 0: 'False', 2: 'Unknown'}[truth]
else:
if name_rank == 0:
xlabel = {
1: 'True Positive',
0: 'False Positive',
2: 'Unknown'
}[truth]
else:
xlabel = {
1: 'False Negative',
0: 'True Negative',
2: 'Unknown'
}[truth]
if len(tag_list) > 0:
xlabel += '\n' + ', '.join(tag_list)
pt.set_xlabel(xlabel)
return ax
def show_name_matches(ibs, qaid, name_daid_list, name_fm_list, name_fs_list,
name_H1_list, name_featflag_list, qreq_=None, **kwargs):
"""
Called from chip_match.py
Args:
ibs (IBEISController): ibeis controller object
qaid (int): query annotation id
name_daid_list (list):
name_fm_list (list):
name_fs_list (list):
name_H1_list (list):
name_featflag_list (list):
qreq_ (QueryRequest): query request object with hyper-parameters(default = None)
Kwargs:
draw_fmatches, name_rank, fnum, pnum, colorbar_, nonvote_mode,
fastmode, show_matches, fs, fm_norm, lbl1, lbl2, rect, draw_border,
cmap, H1, H2, scale_factor1, scale_factor2, draw_pts, draw_ell,
draw_lines, show_nMatches, all_kpts, in_image, show_query, draw_lbl,
name_annot_scores, score, rawscore, aid2_raw_rank, show_name,
show_nid, show_aid, show_annot_score, show_truth, name_score,
show_name_score, show_name_rank, show_timedelta
CommandLine:
python -m ibeis.viz.viz_matches --exec-show_name_matches
python -m ibeis.viz.viz_matches --test-show_name_matches --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_matches import * # NOQA
>>> from ibeis.algo.hots import chip_match
>>> from ibeis.algo.hots import name_scoring
>>> import vtool as vt
>>> from ibeis.algo.hots import _pipeline_helpers as plh # NOQA
>>> import numpy as np
>>> func = chip_match.ChipMatch.show_single_namematch
>>> sourcecode = ut.get_func_sourcecode(func, stripdef=True, stripret=True,
>>> strip_docstr=True)
>>> setup = ut.regex_replace('viz_matches.show_name_matches', '#', sourcecode)
>>> homog = False
>>> print(ut.indent(setup, '>>> '))
>>> ibs, qreq_, cm_list = plh.testdata_post_sver('PZ_MTEST', qaid_list=[1])
>>> cm = cm_list[0]
>>> cm.score_nsum(qreq_)
>>> dnid = ibs.get_annot_nids(cm.qaid)
>>> # +--- COPIED SECTION
>>> locals_ = locals()
>>> var_list = ut.exec_func_src(
>>> func, locals_=locals_,
>>> sentinal='name_annot_scores = cm.annot_score_list.take(sorted_groupxs')
>>> exec(ut.execstr_dict(var_list))
>>> # L___ COPIED SECTION
>>> kwargs = {}
>>> show_name_matches(ibs, qaid, name_daid_list, name_fm_list,
>>> name_fs_list, name_h1_list, name_featflag_list,
>>> qreq_=qreq_, **kwargs)
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
#print("SHOW NAME MATCHES")
#print(ut.repr2(kwargs, nl=True))
#from ibeis import constants as const
from ibeis import tag_funcs
draw_fmatches = kwargs.pop('draw_fmatches', True)
rchip1, kpts1 = get_query_annot_pair_info(ibs, qaid, qreq_, draw_fmatches)
rchip2_list, kpts2_list = get_data_annot_pair_info(ibs, name_daid_list,
qreq_, draw_fmatches)
fm_list = name_fm_list
fs_list = name_fs_list
featflag_list = name_featflag_list
offset_list, sf_list, bbox_list = show_multichip_match(rchip1, rchip2_list,
kpts1, kpts2_list,
fm_list, fs_list,
featflag_list,
**kwargs)
aid_list = [qaid] + name_daid_list
annotate_matches3(ibs, aid_list, bbox_list, offset_list, name_fm_list,
name_fs_list, qreq_=None, **kwargs)
ax = pt.gca()
title = vh.get_query_text(ibs, None, name_daid_list, False, qaid=qaid,
**kwargs)
pt.set_title(title, ax)
# Case tags
annotmatch_rowid_list = ibs.get_annotmatch_rowid_from_superkey(
[qaid] * len(name_daid_list), name_daid_list)
annotmatch_rowid_list = ut.filter_Nones(annotmatch_rowid_list)
tags_list = ibs.get_annotmatch_case_tags(annotmatch_rowid_list)
if not ut.get_argflag('--show'): # False:
tags_list = tag_funcs.consolodate_annotmatch_tags(tags_list)
tag_list = ut.unique_ordered(ut.flatten(tags_list))
name_rank = kwargs.get('name_rank', None)
truth = get_multitruth(ibs, aid_list)
xlabel = {1: 'Correct ID', 0: 'Incorrect ID', 2: 'Unknown ID'}[truth]
if False:
if name_rank is None:
xlabel = {1: 'Genuine', 0: 'Imposter', 2: 'Unknown'}[truth]
#xlabel = {1: 'True', 0: 'False', 2: 'Unknown'}[truth]
else:
if name_rank == 0:
xlabel = {
1: 'True Positive', 0: 'False Positive', 2: 'Unknown'}[truth]
else:
xlabel = {
1: 'False Negative', 0: 'True Negative', 2: 'Unknown'}[truth]
if len(tag_list) > 0:
xlabel += '\n' + ', '.join(tag_list)
pt.set_xlabel(xlabel)
return ax
def shadowform_probability():
""" its hearthstone, but whatev
probability of
raza + no shadowform on turn 5 +
probability of
raza + shadowform on turn 5 +
probability of
kazakus turn 4, raza turn 5, + no shadowform
"""
from scipy.stats import hypergeom
def p_badstuff_shadowform(turn=5, hand_size=3):
deck_size = 30
num_shadowform = 2
def prob_nohave_card_never_mulled(copies=2, hand_size=3):
deck_size = 30
prb = hypergeom(deck_size, copies, hand_size)
# P(initial_miss)
p_none_premul = prb.cdf(0)
# GIVEN that we mul our first 3 what is prob we still are unlucky
# P(miss_turn0 | initial_miss)
prb = hypergeom(deck_size - hand_size, copies, hand_size)
p_none_in_mul = prb.cdf(0)
# TODO: add constraints about 2 drops
# P(miss_turn0) = P(miss_turn0 | initial_miss) * P(initial_miss)
p_none_at_start = p_none_in_mul * p_none_premul
return p_none_at_start
def prob_nohave_card_always_mulled(copies=2, hand_size=3):
# probability of getting the card initially
p_none_premul = hypergeom(deck_size, copies, hand_size).cdf(0)
# probability of getting the card if everything is thrown away
# (TODO: factor in the probability that you need to keep something)
# for now its fine because if we keep shadowform the end calculation is fine
p_nohave_postmul_given_nohave = hypergeom(deck_size - hand_size, copies, hand_size).cdf(0)
# not necessary, but it shows the theory
p_nohave_postmul_given_had = 1
p_nohave_turn0 = (
p_nohave_postmul_given_nohave * p_none_premul + (1 - p_none_premul) * p_nohave_postmul_given_had
)
return p_nohave_turn0
def prob_nohave_by_turn(p_none_turn0, turn, copies, hand_size):
# P(miss_turnN | miss_mul)
p_none_turnN_given_mulmis = hypergeom(deck_size - hand_size, copies, turn).cdf(0)
# P(miss_turnN) = P(miss_turnN | miss_mul) P(miss_mul)
p_none_turnN = p_none_turnN_given_mulmis * p_none_turn0
return p_none_turnN
p_no_shadowform_on_turn0 = prob_nohave_card_never_mulled(copies=num_shadowform, hand_size=hand_size)
no_shadowform_turnN = prob_nohave_by_turn(p_no_shadowform_on_turn0, turn, num_shadowform, hand_size)
# Assume you always mul raza
p_noraza_initial = prob_nohave_card_always_mulled(copies=1, hand_size=hand_size)
p_noraza_turnN = prob_nohave_by_turn(p_noraza_initial, turn, copies=1, hand_size=hand_size)
p_raza_turnN = 1 - p_noraza_turnN
# probability that you have raza and no shadowform by turn 5
p_raza_and_noshadowform_turnN = p_raza_turnN * no_shadowform_turnN
return p_raza_and_noshadowform_turnN
import plottool as pt # NOQA
turns = list(range(0, 26))
probs = [p_badstuff_shadowform(turn, hand_size=3) for turn in turns]
pt.plot(turns, probs, label="on play")
probs = [p_badstuff_shadowform(turn, hand_size=4) for turn in turns]
pt.plot(turns, probs, label="with coin")
pt.set_xlabel("turn")
pt.set_ylabel("probability")
pt.set_title("Probability of Having Raza without a Shadowform")
pt.legend()
pt.gca().set_ylim(0, 1)
