def _select_ith_match(mx, qaid, aid):
#----------------------
# Get info for the _select_ith_match plot
annote_ptr[0] = 1
# Get the mx-th feature match
aid1, aid2 = qaid, aid
fx1, fx2 = fm[mx]
fscore2 = qres.aid2_fs[aid2][mx]
fk2 = qres.aid2_fk[aid2][mx]
kpts1, kpts2 = ibs.get_annot_kpts([aid1, aid2])
desc1, desc2 = ibs.get_annot_desc([aid1, aid2])
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
# Extracted keypoints to draw
extracted_list = [(rchip1, kp1, sift1, fx1, aid1, info1),
(rchip2, kp2, sift2, fx2, aid2, info2)]
# Normalizng Keypoint
if hasattr(qres, 'filt2_meta') and 'lnbnn' in qres.filt2_meta:
qfx2_norm = qres.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_desc(aid3)[fx3]
info3 = '\nnorm %s k=%r' % (vh.get_aidstrs(aid3), normk)
extracted_list.append((rchip3, kp3, sift3, fx3, aid3, info3))
else:
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)
_chipmatch_view(pnum1, vert=False, ell_alpha=.4, ell_linewidth=1.8,
colors=df2.BLUE, sel_fm=sel_fm, **kwargs)
# Draw selected feature matches
px = nCols * same_fig # plot offset
prevsift = None
if not same_fig:
fnum2 = fnum + len(viz.FNUMS)
fig2 = df2.figure(fnum=fnum2, docla=True, doclf=True)
else:
fnum2 = fnum
for (rchip, kp, sift, fx, aid, info) in extracted_list:
px = 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', _click_matches_click)
df2.set_figtitle(figtitle + vh.get_vsstr(qaid, aid))
def chipmatch_view(self, pnum=(1, 1, 1), **kwargs_):
"""
just visualizes the matches using some type of lines
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-chipmatch_view --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact()
>>> self.chipmatch_view()
>>> pt.show_if_requested()
"""
ibs = self.ibs
aid = self.daid
qaid = self.qaid
fnum = self.fnum
figtitle = self.figtitle
xywh2_ptr = self.xywh2_ptr
# drawing mode draw: with/without lines/feats
mode = self.mode
draw_ell = mode >= 1
draw_lines = mode == 2
self.mode = (self.mode + 1) % 3
df2.figure(fnum=fnum, docla=True, doclf=True)
show_matches_kw = self.kwargs.copy()
show_matches_kw.update(
dict(fnum=fnum,
pnum=pnum,
draw_lines=draw_lines,
draw_ell=draw_ell,
colorbar_=True,
vert=self.vert))
show_matches_kw.update(kwargs_)
if self.use_homog:
show_matches_kw['H1'] = self.H1
#show_matches_kw['score'] = self.score
show_matches_kw['rawscore'] = self.score
#ut.embed()
show_matches_kw['aid2_raw_rank'] = self.rank
tup = viz.viz_matches.show_matches2(ibs,
self.qaid,
self.daid,
self.fm,
self.fs,
qreq_=self.qreq_,
**show_matches_kw)
ax, xywh1, xywh2 = tup
xywh2_ptr[0] = xywh2
df2.set_figtitle(figtitle + ' ' + vh.get_vsstr(qaid, aid))
def _chipmatch_view(pnum=(1, 1, 1), **kwargs):
mode = annote_ptr[0] # drawing mode draw: with/without lines/feats
draw_ell = mode >= 1
draw_lines = mode == 2
annote_ptr[0] = (annote_ptr[0] + 1) % 3
df2.figure(fnum=fnum, docla=True, doclf=True)
# TODO RENAME This to remove qres and rectify with show_matches
tup = viz.show_matches(ibs, qres, aid, fnum=fnum, pnum=pnum,
draw_lines=draw_lines, draw_ell=draw_ell,
colorbar_=True, **kwargs)
ax, xywh1, xywh2 = tup
xywh2_ptr[0] = xywh2
df2.set_figtitle(figtitle + ' ' + vh.get_vsstr(qaid, aid))
def chipmatch_view(self, pnum=(1, 1, 1), **kwargs_):
"""
just visualizes the matches using some type of lines
CommandLine:
python -m ibeis.viz.interact.interact_matches --test-chipmatch_view --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.interact.interact_matches import * # NOQA
>>> self = testdata_match_interact()
>>> self.chipmatch_view()
>>> pt.show_if_requested()
"""
ibs = self.ibs
aid = self.daid
qaid = self.qaid
fnum = self.fnum
figtitle = self.figtitle
xywh2_ptr = self.xywh2_ptr
# drawing mode draw: with/without lines/feats
mode = self.mode
draw_ell = mode >= 1
draw_lines = mode == 2
self.mode = (self.mode + 1) % 3
df2.figure(fnum=fnum, docla=True, doclf=True)
show_matches_kw = self.kwargs.copy()
show_matches_kw.update(
dict(fnum=fnum, pnum=pnum, draw_lines=draw_lines,
draw_ell=draw_ell, colorbar_=True, vert=self.vert))
show_matches_kw.update(kwargs_)
if self.use_homog:
show_matches_kw['H1'] = self.H1
#show_matches_kw['score'] = self.score
show_matches_kw['rawscore'] = self.score
#ut.embed()
show_matches_kw['aid2_raw_rank'] = self.rank
tup = viz.viz_matches.show_matches2(ibs, self.qaid, self.daid,
self.fm, self.fs,
qreq_=self.qreq_,
**show_matches_kw)
ax, xywh1, xywh2 = tup
xywh2_ptr[0] = xywh2
df2.set_figtitle(figtitle + ' ' + vh.get_vsstr(qaid, aid))
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 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))