def _image_view(sel_aids=sel_aids, **_kwargs):
try:
viz.show_image(ibs, gid, sel_aids=sel_aids, fnum=self.fnum, **_kwargs)
df2.set_figtitle('Image View')
except TypeError as ex:
ut.printex(ex, ut.dict_str(_kwargs))
raise
def TEST_QUERY(ibs):
print('[TEST_QUERY]')
valid_aids = ibs.get_valid_aids()
print('[TEST_QUERY] len(valid_aids)=%r' % (len(valid_aids)))
qaid_list = valid_aids[0:1]
print('[TEST_QUERY] len(qaid_list)=%r' % (len(qaid_list)))
ibs._init_query_requestor()
qreq = ibs.qreq
#query_helpers.find_matchable_chips(ibs)
aids = ibs.get_recognition_database_aids()
qres_dict = ibs.query_all(qaid_list)
for qaid in qaid_list:
qres = qres_dict[qaid]
top_aids = qres.get_top_aids(ibs)
top_aids = utool.safe_slice(top_aids, 3)
aid2 = top_aids[0]
fnum = df2.next_fnum()
df2.figure(fnum=fnum, doclf=True)
#viz_matches.show_matches(ibs, qres, aid2, fnum=fnum, in_image=True)
#viz.show_qres(ibs, qres, fnum=fnum, top_aids=top_aids, ensure=False)
interact.ishow_qres(ibs, qres, fnum=fnum, top_aids=top_aids,
ensure=False, annote_mode=1)
df2.set_figtitle('Query Result')
df2.adjust_subplots_safe(top=.8)
return locals()
def show_name(ibs, nid, nid2_aids=None, in_image=True, fnum=0, sel_aids=[], subtitle='',
annote=False, **kwargs):
print('[viz] show_name nid=%r' % nid)
aid_list = ibs.get_name_aids(nid)
name = ibs.get_name_text((nid,))
ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote)
print('[viz] show_name=%r aid_list=%r' % (name, aid_list))
nAids = len(aid_list)
if nAids > 0:
nRows, nCols = ph.get_square_row_cols(nAids)
print('[viz*] r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = df2.get_pnum_func(nRows, nCols)
fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
# Trigger computation of all chips in parallel
for px, aid in enumerate(aid_list):
show_chip(ibs, aid=aid, pnum=pnum_(px), annote=annote, in_image=in_image)
if aid in sel_aids:
ax = df2.gca()
df2.draw_border(ax, df2.GREEN, 4)
#plot_aid3(ibs, aid)
if isinstance(nid, np.ndarray):
nid = nid[0]
if isinstance(name, np.ndarray):
name = name[0]
else:
df2.imshow_null(fnum=fnum, **kwargs)
figtitle = 'Name View nid=%r name=%r' % (nid, name)
df2.set_figtitle(figtitle)
def make_hud(self):
""" Creates heads up display """
# Button positioning
nToggle = len(self.toggleable_kws)
# horizontal left, horizonal right
hl_slot, hr_slot = df2.make_bbox_positioners(y=.02, w=.08, h=.04,
xpad=.05, startx=0, stopx=1)
prev_rect = hl_slot(0) # left button
next_rect = hr_slot(0) # right button
tw = df2.width_from(nToggle, pad=.05, start=.13, stop=.87)
hlt_slot, hrt_slot = df2.make_bbox_positioners(y=.02, w=tw, h=.04,
xpad=.05, startx=.13,
stopx=.87)
# Create buttons
if self.current_pagenum != 0:
self.append_button('prev', callback=self.prev_page, rect=prev_rect)
if self.current_pagenum != self.nPages - 1:
self.append_button('next', callback=self.next_page, rect=next_rect)
for count, (text, keyword) in enumerate(six.iteritems(self.toggleable_kws)):
callback = functools.partial(self.toggle_kw, keyword=keyword)
rect = hlt_slot(count)
self.append_button(text, callback=callback, rect=rect)
figtitle_fmt = '''
Match Candidates ({start_index}-{stop_index}) / {nCands}
page {current_pagenum} / {nPages}
'''
# sexy: using object dict as format keywords
figtitle = figtitle_fmt.format(**self.__dict__)
df2.set_figtitle(figtitle)
def show_name(ibs, nid, in_image=True, fnum=0, sel_aids=[], subtitle='',
annote=False, aid_list=None, index_list=None, **kwargs):
r"""
Args:
ibs (IBEISController): ibeis controller object
nid (?):
in_image (bool):
fnum (int): figure number
sel_aids (list):
subtitle (str):
annote (bool):
CommandLine:
python -m ibeis.viz.viz_name --test-show_name --dpath ~/latex/crall-candidacy-2015 --save 'figures/{name}.jpg' --no-figtitle --notitle --db NNP_Master3 --figsize=9,4 --clipwhite --dpi=180 --adjust=.05 --index_list=[0,1,2,3] --rc=2,4 --append temp_out_figure.tex --name=IBEIS_PZ_0739 --no-draw_lbls --doboth --no-inimage --diskshow
python -m ibeis.viz.viz_name --test-show_name --no-figtitle --notitle --db NNP_Master3 --figsize=9,4 --clipwhite --dpi=180 --adjust=.05 --index_list=[0,1,2,3] --rc=2,4 --append temp_out_figure.tex --name=IBEIS_PZ_0739 --no-draw_lbls --doboth --no-inimage --show
python -m ibeis.viz.viz_name --test-show_name --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_name import * # NOQA
>>> ibs, nid, in_image, index_list = testdata_showname()
>>> fnum = 0
>>> sel_aids = []
>>> subtitle = ''
>>> annote = False
>>> # execute function
>>> show_name(ibs, nid, in_image, fnum, sel_aids, subtitle, annote, index_list=index_list)
>>> ut.show_if_requested()
"""
print('[viz_name] show_name nid=%r, index_list=%r, aid_list=%r' % (nid, index_list, aid_list))
if aid_list is None:
aid_list = ibs.get_name_aids(nid)
else:
assert ut.list_all_eq_to(ibs.get_annot_nids(aid_list), nid)
if index_list is not None:
aid_list = ut.take(aid_list, index_list)
name = ibs.get_name_texts((nid,))
print('[viz_name] * name=%r aid_list=%r' % (name, aid_list))
show_multiple_chips(ibs, aid_list, in_image=in_image, fnum=fnum,
sel_aids=sel_aids, annote=annote, **kwargs)
if isinstance(nid, np.ndarray):
nid = nid[0]
if isinstance(name, np.ndarray):
name = name[0]
use_figtitle = not ut.get_argflag('--no-figtitle')
if use_figtitle:
figtitle = 'Name View nid=%r name=%r' % (nid, name)
df2.set_figtitle(figtitle)
def TEST_VIZ(ibs):
valid_gids = ibs.get_valid_gids()
valid_aids = ibs.get_valid_aids()
print('len(valid_aids) = %r' % len(valid_aids))
print('len(valid_gids) = %r' % len(valid_gids))
assert len(valid_gids) > 0, 'database images cannot be empty for test'
gindex = 1
gid = valid_gids[gindex]
aid_list = ibs.get_image_aids(gid)
rindex = 0
aid = aid_list[rindex]
qaid = aid
aids = aid_list[1:3]
aid = aid_list[-1]
try:
qres = ibs._query_chips4([qaid], valid_aids)[qaid]
print(qres)
top_aids = qres.get_top_aids(ibs)
assert len(top_aids) > 0, 'Results seems to be empty'
aid2 = top_aids[0] # 294
query_failed = False
except Exception as ex:
query_failed = True
utool.printex(ex, 'QUERY FAILED!')
raise
#----------------------
#print('Show Image')
viz.show_image(ibs, gid, aids=aids, fnum=1)
df2.set_figtitle('Show Image')
#----------------------
#print('Show Chip')
kpts_kwgs = dict(ell=True,
ori=True,
rect=True,
eig=True,
pts=False,
kpts_subset=10)
viz.show_chip(ibs, aid, in_image=False, fnum=2, **kpts_kwgs)
df2.set_figtitle('Show Chip (normal)')
viz.show_chip(ibs, aid, in_image=True, fnum=3, **kpts_kwgs)
df2.set_figtitle('Show Chip (in_image)')
#----------------------
if not query_failed:
print('Show Query')
viz.show_matches(ibs, qres, aid2, fnum=4)
df2.set_figtitle('Show Chipres')
viz.show_qres(ibs, qres, fnum=5)
df2.set_figtitle('Show QRes')
##----------------------
print('return test locals')
return locals()
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 TEST_QUERY_COMP(ibs):
r"""
CommandLine:
python -m ibeis.tests.test_ibs_query_components --test-TEST_QUERY_COMP
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.tests.test_ibs_query_components import * # NOQA
>>> import ibeis
>>> # build test data
>>> ibs = ibeis.opendb('testdb1')
>>> # execute function
>>> TEST_QUERY_COMP(ibs)
"""
print('[TEST_QUERY_COMP]')
aids = ibs.get_valid_aids()
index = 0
index = utool.get_argval('--index', type_=int, default=index)
qaid_list = utool.safe_slice(aids, index, index + 1)
print('[TEST_QUERY_COMP] len(qaid_list)=%r' % (qaid_list))
try:
comp_locals_ = query_helpers.get_query_components(ibs, qaid_list)
qres_dict = OrderedDict([
('ORIG', comp_locals_['qres_ORIG']),
('FILT', comp_locals_['qres_FILT']),
('SVER', comp_locals_['qres_SVER']),
])
top_aids = qres_dict['SVER'].get_top_aids()
aid2 = top_aids[0]
except Exception as ex:
if 'qres_dict' in vars():
for name, qres in qres_dict.items():
print(name)
print(qres.get_inspect_str())
utool.printex(ex, keys=['qaid_list'], pad_stdout=True)
raise
for px, (lbl, qres) in enumerate(six.iteritems(qres_dict)):
print(lbl)
fnum = df2.next_fnum()
df2.figure(fnum=fnum, doclf=True)
qres.ishow_top(ibs, fnum=fnum, top_aids=top_aids, ensure=False)
df2.set_figtitle(lbl)
df2.adjust_subplots_safe(top=.8)
fnum = df2.next_fnum()
qaid2_svtups = comp_locals_['qaid2_svtups']
qaid2_chipmatch_FILT = comp_locals_['qaid2_chipmatch_FILT']
aid1 = qaid = comp_locals_['qaid']
aid2_svtup = qaid2_svtups[aid1]
chipmatch_FILT = qaid2_chipmatch_FILT[aid1]
viz.show_sver(ibs, aid1, aid2, chipmatch_FILT, aid2_svtup, fnum=fnum)
return locals()
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 _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
print('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
df2.figure(fnum=fnum, pnum=pnum, docla=True, doclf=True)
# Toggle no keypoints view
viz.show_chip(ibs, aid, fnum=fnum, pnum=pnum, config2_=config2_,
**kwargs)
df2.set_figtitle('Chip View')
def _chip_view(mode=0, pnum=(1, 1, 1), **kwargs):
print('... _chip_view mode=%r' % mode_ptr[0])
kwargs['ell'] = mode_ptr[0] == 1
kwargs['pts'] = mode_ptr[0] == 2
if not ischild:
df2.figure(fnum=fnum, pnum=pnum, docla=True, doclf=True)
# Toggle no keypoints view
viz.show_chip(ibs, aid, fnum=fnum, pnum=pnum, config2_=config2_,
**kwargs)
df2.set_figtitle('Chip View')
def _image_view(sel_aids=sel_aids, **_kwargs):
try:
viz.show_image(ibs,
gid,
sel_aids=sel_aids,
fnum=self.fnum,
**_kwargs)
df2.set_figtitle('Image View')
except TypeError as ex:
ut.printex(ex, ut.dict_str(_kwargs))
raise
def TEST_VIZ(ibs):
valid_gids = ibs.get_valid_gids()
valid_aids = ibs.get_valid_aids()
print('len(valid_aids) = %r' % len(valid_aids))
print('len(valid_gids) = %r' % len(valid_gids))
assert len(valid_gids) > 0, 'database images cannot be empty for test'
gindex = 1
gid = valid_gids[gindex]
aid_list = ibs.get_image_aids(gid)
rindex = 0
aid = aid_list[rindex]
qaid = aid
aids = aid_list[1:3]
aid = aid_list[-1]
try:
qres = ibs._query_chips4([qaid], valid_aids)[qaid]
print(qres)
top_aids = qres.get_top_aids(ibs)
assert len(top_aids) > 0, 'Results seems to be empty'
aid2 = top_aids[0] # 294
query_failed = False
except Exception as ex:
query_failed = True
utool.printex(ex, 'QUERY FAILED!')
raise
#----------------------
#print('Show Image')
viz.show_image(ibs, gid, aids=aids, fnum=1)
df2.set_figtitle('Show Image')
#----------------------
#print('Show Chip')
kpts_kwgs = dict(ell=True, ori=True, rect=True,
eig=True, pts=False, kpts_subset=10)
viz.show_chip(ibs, aid, in_image=False, fnum=2, **kpts_kwgs)
df2.set_figtitle('Show Chip (normal)')
viz.show_chip(ibs, aid, in_image=True, fnum=3, **kpts_kwgs)
df2.set_figtitle('Show Chip (in_image)')
#----------------------
if not query_failed:
print('Show Query')
viz.show_matches(ibs, qres, aid2, fnum=4)
df2.set_figtitle('Show Chipres')
viz.show_qres(ibs, qres, fnum=5)
df2.set_figtitle('Show QRes')
##----------------------
print('return test locals')
return locals()
def TEST_QUERY(ibs):
r"""
CommandLine:
python -m ibeis.tests.test_ibs_query --test-TEST_QUERY
python -m ibeis.tests.test_ibs_query --test-TEST_QUERY --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.tests.test_ibs_query import * # NOQA
>>> import plottool as pt
>>> import ibeis
>>> ibs = ibeis.opendb('testdb1')
>>> TEST_QUERY(ibs)
>>> pt.show_if_requested()
"""
print('[TEST_QUERY]')
daid_list = ibs.get_valid_aids()
print('[TEST_QUERY] len(daid_list)=%r' % (len(daid_list)))
qaid_list = daid_list[0:1]
print('[TEST_QUERY] len(qaid_list)=%r' % (len(qaid_list)))
qres_list = ibs.query_chips(qaid_list, daid_list, use_cache=False, use_bigcache=False)
qres_list_ = ibs.query_chips(qaid_list, daid_list)
try:
vals1 = qres_list
vals2 = qres_list_
assert len(vals1) == 1, 'expected 1 qres in result'
assert len(vals2) == 1, 'expected 1 qres in result'
#assert list(qres_dict.keys()) == list(qres_dict_.keys()), 'qres cache doesnt work. key error'
qres1 = vals1[0]
qres2 = vals2[0]
inspect_str1 = qres1.get_inspect_str(ibs)
inspect_str2 = qres2.get_inspect_str(ibs)
print(inspect_str1)
assert inspect_str1 == inspect_str2, 'qres cache inconsistency'
assert vals1 == vals2, 'qres cache doesnt work. val error'
except AssertionError as ex:
utool.printex(ex, key_list=list(locals().keys()))
raise
if ut.show_was_requested():
for qres in qres_list:
top_aids = qres.get_top_aids()
#top_aids = utool.safe_slice(top_aids, 3)
aid2 = top_aids[0]
fnum = df2.next_fnum()
df2.figure(fnum=fnum, doclf=True)
qres.ishow_top(ibs, fnum=fnum, top_aids=top_aids, ensure=False, annot_mode=1)
df2.set_figtitle('Query Result')
df2.adjust_subplots_safe(top=.8)
return locals()
def dump_orgres_matches(allres, orgres_type):
orgres = allres.__dict__[orgres_type]
ibs = allres.ibs
qrid2_qres = allres.qrid2_qres
# loop over each query / result of interest
for qrid, rid, score, rank in orgres.iter():
query_gname, _ = os.path.splitext(ibs.tables.gx2_gname[ibs.tables.cx2_gx[qrid]])
result_gname, _ = os.path.splitext(ibs.tables.gx2_gname[ibs.tables.cx2_gx[rid]])
qres = qrid2_qres[qrid]
df2.figure(fnum=1, plotnum=121)
df2.show_matches_annote_res(qres, ibs, rid, fnum=1, plotnum=121)
big_title = 'score=%.2f_rank=%d_q=%s_r=%s' % (score, rank, query_gname, result_gname)
df2.set_figtitle(big_title)
viz.__dump_or_browse(allres, orgres_type + '_matches' + allres.title_suffix)
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 dump_orgres_matches(allres, orgres_type):
orgres = allres.__dict__[orgres_type]
ibs = allres.ibs
qcx2_res = allres.qcx2_res
# loop over each query / result of interest
for qcx, cx, score, rank in orgres.iter():
query_gname, _ = os.path.splitext(ibs.tables.gx2_gname[ibs.tables.cx2_gx[qcx]])
result_gname, _ = os.path.splitext(ibs.tables.gx2_gname[ibs.tables.cx2_gx[cx]])
res = qcx2_res[qcx]
df2.figure(fnum=FIGNUM, pnum=121)
df2.show_matches3(res, ibs, cx, SV=False, fnum=FIGNUM, pnum=121)
df2.show_matches3(res, ibs, cx, SV=True, fnum=FIGNUM, pnum=122)
big_title = 'score=%.2f_rank=%d_q=%s_r=%s' % (score, rank, query_gname,
result_gname)
df2.set_figtitle(big_title)
__dump_or_browse(allres.ibs, orgres_type + '_matches' + allres.title_suffix)
def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel, fnum=1, figtitle=''):
from plottool import draw_func2 as df2
from plottool.viz_keypoints import _annotate_kpts, show_keypoints
from plottool.viz_featrow import draw_feat_row
import plottool
import vtool.patch as ptool
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
gori_weights = ptool.gaussian_weight_patch(gmag)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch,
gradx,
grady,
gmag,
gori,
hist,
centers,
fnum=fnum)
df2.set_figtitle(figtitle + 'Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=fnum + 1)
df2.set_figtitle(figtitle)
# TEST_figure2(imgBGR, kpts2, Desc2, sel, fnum=fnum + 2)
# df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def dump_orgres_matches(allres, orgres_type):
orgres = allres.__dict__[orgres_type]
ibs = allres.ibs
qrid2_qres = allres.qrid2_qres
# loop over each query / result of interest
for qrid, rid, score, rank in orgres.iter():
query_gname, _ = os.path.splitext(
ibs.tables.gx2_gname[ibs.tables.cx2_gx[qrid]])
result_gname, _ = os.path.splitext(
ibs.tables.gx2_gname[ibs.tables.cx2_gx[rid]])
qres = qrid2_qres[qrid]
df2.figure(fnum=1, plotnum=121)
df2.show_matches_annote_res(qres, ibs, rid, fnum=1, plotnum=121)
big_title = 'score=%.2f_rank=%d_q=%s_r=%s' % (score, rank, query_gname,
result_gname)
df2.set_figtitle(big_title)
viz.__dump_or_browse(allres,
orgres_type + '_matches' + allres.title_suffix)
def plot_seperability(hs, qcx_list, fnum=1):
print('[dev] plot_seperability(fnum=%r)' % fnum)
qcx2_res = get_qcx2_res(hs, qcx_list)
qcx2_separability = get_seperatbility(hs, qcx2_res)
sep_score_list = qcx2_separability.values()
df2.figure(fnum=fnum, doclf=True, docla=True)
print('[dev] seperability stats: ' + utool.stats_str(sep_score_list))
sorted_sepscores = sorted(sep_score_list)
df2.plot(sorted_sepscores, color=df2.DEEP_PINK, label='seperation score',
yscale=YSCALE)
df2.set_xlabel('true chipmatch index (%d)' % len(sep_score_list))
df2.set_logyscale_from_data(sorted_sepscores)
df2.dark_background()
rowid = qcx2_res.itervalues().next().rowid
df2.set_figtitle('seperability\n' + rowid)
df2.legend()
fnum += 1
return fnum
def annotationmatch_scores(ibs, qaid_list):
print('[dev] annotationmatch_scores')
allres = get_allres(ibs, qaid_list)
# Get the descriptor distances of true matches
orgtype_list = ['false', 'true']
orgtype_list = ['top_false', 'top_true']
#markers_map = {'false': 'o', 'true': 'o-', 'top_true': 'o-', 'top_false': 'o'}
markers_map = defaultdict(lambda: 'o')
cmatch_scores_map = results_analyzer.get_orgres_annotationmatch_scores(allres, orgtype_list)
results_analyzer.print_annotationmatch_scores_map(cmatch_scores_map)
#true_cmatch_scores = cmatch_scores_map['true']
#false_cmatch_scores = cmatch_scores_map['false']
scores_list = [cmatch_scores_map[orgtype] for orgtype in orgtype_list]
scores_lbls = orgtype_list
scores_markers = [markers_map[orgtype] for orgtype in orgtype_list]
plottool.plots.draw_scores_cdf(scores_list, scores_lbls, scores_markers)
df2.set_figtitle('Chipmatch Scores ' + ibs.qreq.get_cfgstr())
return locals()
def TEST_QUERY_COMP(ibs):
print('[TEST_QUERY_COMP]')
qaid_list = ibs.get_valid_aids()[0:1]
print('[TEST_QUERY_COMP] len(qaid_list)=%r' % (qaid_list))
ibs._init_query_requestor()
qreq = ibs.qreq
#query_helpers.find_matchable_chips(ibs)
aids = ibs.get_recognition_database_aids()
index = 0
index = utool.get_arg('--index', type_=int, default=index)
qaid_list = utool.safe_slice(aids, index, index + 1)
comp_locals_ = query_helpers.get_query_components(ibs, qaid_list)
qres_dict = OrderedDict([
('ORIG', comp_locals_['qres_ORIG']),
('FILT', comp_locals_['qres_FILT']),
('SVER', comp_locals_['qres_SVER']),
])
top_aids = qres_dict['SVER'].get_top_aids(ibs)
top_aids = utool.safe_slice(top_aids, 3)
aid2 = top_aids[0]
for px, (lbl, qres) in enumerate(six.iteritems(qres_dict)):
print(lbl)
fnum = df2.next_fnum()
df2.figure(fnum=fnum, doclf=True)
#viz_matches.show_matches(ibs, qres, aid2, fnum=fnum, in_image=True)
#viz.show_qres(ibs, qres, fnum=fnum, top_aids=top_aids, ensure=False)
interact.ishow_qres(ibs, qres, fnum=fnum, top_aids=top_aids, ensure=False)
df2.set_figtitle(lbl)
df2.adjust_subplots_safe(top=.8)
fnum = df2.next_fnum()
qaid2_svtups = comp_locals_['qaid2_svtups']
qaid2_chipmatch_FILT = comp_locals_['qaid2_chipmatch_FILT']
aid1 = qaid = comp_locals_['qaid']
aid2_svtup = qaid2_svtups[aid1]
chipmatch_FILT = qaid2_chipmatch_FILT[aid1]
viz.show_sver(ibs, aid1, aid2, chipmatch_FILT, aid2_svtup, fnum=fnum)
return locals()
def dump_orgres_matches(allres, orgres_type):
orgres = allres.__dict__[orgres_type]
ibs = allres.ibs
qcx2_res = allres.qcx2_res
# loop over each query / result of interest
for qcx, cx, score, rank in orgres.iter():
query_gname, _ = os.path.splitext(
ibs.tables.gx2_gname[ibs.tables.cx2_gx[qcx]])
result_gname, _ = os.path.splitext(
ibs.tables.gx2_gname[ibs.tables.cx2_gx[cx]])
res = qcx2_res[qcx]
df2.figure(fnum=FIGNUM, pnum=121)
df2.show_matches3(res, ibs, cx, SV=False, fnum=FIGNUM, pnum=121)
df2.show_matches3(res, ibs, cx, SV=True, fnum=FIGNUM, pnum=122)
big_title = 'score=%.2f_rank=%d_q=%s_r=%s' % (score, rank, query_gname,
result_gname)
df2.set_figtitle(big_title)
__dump_or_browse(allres.ibs,
orgres_type + '_matches' + allres.title_suffix)
def desc_dists(ibs, qaid_list):
""" Plots the distances between matching descriptors
with groundtruth (true/false) data """
print('[dev] desc_dists')
allres = get_allres(ibs, qaid_list)
# Get the descriptor distances of true matches
orgtype_list = ['top_false', 'true']
disttype = 'L2'
orgres2_distmap = results_analyzer.get_orgres_desc_match_dists(allres, orgtype_list)
results_analyzer.print_desc_distances_map(orgres2_distmap)
#true_desc_dists = orgres2_distmap['true']['L2']
#false_desc_dists = orgres2_distmap['false']['L2']
#scores_list = [false_desc_dists, true_desc_dists]
dists_list = [orgres2_distmap[orgtype][disttype] for orgtype in orgtype_list]
dists_lbls = orgtype_list
dists_markers = ['x', 'o--']
plottool.plots.draw_scores_cdf(dists_list, dists_lbls, dists_markers)
df2.set_figtitle('Descriptor Distance CDF d(x)' + ibs.qreq.get_cfgstr())
return locals()
def make_hud(self):
""" Creates heads up display """
# Button positioning
nToggle = len(self.toggleable_kws)
# horizontal left, horizonal right
hl_slot, hr_slot = df2.make_bbox_positioners(y=.02,
w=.08,
h=.04,
xpad=.05,
startx=0,
stopx=1)
prev_rect = hl_slot(0) # left button
next_rect = hr_slot(0) # right button
tw = df2.width_from(nToggle, pad=.05, start=.13, stop=.87)
hlt_slot, hrt_slot = df2.make_bbox_positioners(y=.02,
w=tw,
h=.04,
xpad=.05,
startx=.13,
stopx=.87)
# Create buttons
if self.current_pagenum != 0:
self.append_button('prev', callback=self.prev_page, rect=prev_rect)
if self.current_pagenum != self.nPages - 1:
self.append_button('next', callback=self.next_page, rect=next_rect)
for count, (text,
keyword) in enumerate(six.iteritems(self.toggleable_kws)):
callback = functools.partial(self.toggle_kw, keyword=keyword)
rect = hlt_slot(count)
self.append_button(text, callback=callback, rect=rect)
figtitle_fmt = '''
Match Candidates ({start_index}-{stop_index}) / {nCands}
page {current_pagenum} / {nPages}
'''
# sexy: using object dict as format keywords
figtitle = figtitle_fmt.format(**self.__dict__)
df2.set_figtitle(figtitle)
def plot_tt_bt_tf_matches(ibs, allres, qcx):
#print('Visualizing result: ')
#res.printme()
res = allres.qcx2_res[qcx]
ranks = (allres.top_true_qcx_arrays[0][qcx],
allres.bot_true_qcx_arrays[0][qcx],
allres.top_false_qcx_arrays[0][qcx])
#scores = (allres.top_true_qcx_arrays[1][qcx],
#allres.bot_true_qcx_arrays[1][qcx],
#allres.top_false_qcx_arrays[1][qcx])
cxs = (allres.top_true_qcx_arrays[2][qcx],
allres.bot_true_qcx_arrays[2][qcx],
allres.top_false_qcx_arrays[2][qcx])
titles = ('best True rank=' + str(ranks[0]) + ' ',
'worst True rank=' + str(ranks[1]) + ' ',
'best False rank=' + str(ranks[2]) + ' ')
df2.figure(fnum=1, pnum=231)
res.plot_matches(res,
ibs,
cxs[0],
False,
fnum=1,
pnum=131,
title_aug=titles[0])
res.plot_matches(res,
ibs,
cxs[1],
False,
fnum=1,
pnum=132,
title_aug=titles[1])
res.plot_matches(res,
ibs,
cxs[2],
False,
fnum=1,
pnum=133,
title_aug=titles[2])
fig_title = 'fig q' + ibs.cidstr(
qcx) + ' TT BT TF -- ' + allres.title_suffix
df2.set_figtitle(fig_title)
def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel, fnum=1, figtitle=''):
from plottool import draw_func2 as df2
from plottool.viz_keypoints import _annotate_kpts, show_keypoints
from plottool.viz_featrow import draw_feat_row
import plottool
import vtool.patch as ptool
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
gori_weights = ptool.gaussian_weight_patch(gmag)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers, fnum=fnum)
df2.set_figtitle(figtitle + 'Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=fnum + 1)
df2.set_figtitle(figtitle)
# TEST_figure2(imgBGR, kpts2, Desc2, sel, fnum=fnum + 2)
# df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def plot_tt_bt_tf_matches(ibs, allres, qcx):
#print('Visualizing result: ')
#res.printme()
res = allres.qcx2_res[qcx]
ranks = (allres.top_true_qcx_arrays[0][qcx],
allres.bot_true_qcx_arrays[0][qcx],
allres.top_false_qcx_arrays[0][qcx])
#scores = (allres.top_true_qcx_arrays[1][qcx],
#allres.bot_true_qcx_arrays[1][qcx],
#allres.top_false_qcx_arrays[1][qcx])
cxs = (allres.top_true_qcx_arrays[2][qcx],
allres.bot_true_qcx_arrays[2][qcx],
allres.top_false_qcx_arrays[2][qcx])
titles = ('best True rank=' + str(ranks[0]) + ' ',
'worst True rank=' + str(ranks[1]) + ' ',
'best False rank=' + str(ranks[2]) + ' ')
df2.figure(fnum=1, pnum=231)
res.plot_matches(res, ibs, cxs[0], False, fnum=1, pnum=131, title_aug=titles[0])
res.plot_matches(res, ibs, cxs[1], False, fnum=1, pnum=132, title_aug=titles[1])
res.plot_matches(res, ibs, cxs[2], False, fnum=1, pnum=133, title_aug=titles[2])
fig_title = 'fig q' + ibs.cidstr(qcx) + ' TT BT TF -- ' + allres.title_suffix
df2.set_figtitle(fig_title)
def test_viz_image(img_fpath):
# Read image
img = cv2.imread(img_fpath)
tau = np.pi * 2 # References: tauday.com
# Create figure
fig = df2.figure(fnum=42, pnum=(1, 1, 1))
# Clear figure
fig.clf()
# Build parameters
bbox_list = [dummy_bbox(img), dummy_bbox(img, (-.25, -.25), .1)]
showkw = {
'title': 'test axis title',
# The list of bounding boxes to be drawn on the image
'bbox_list': bbox_list,
'theta_list': [tau * .7, tau * .9],
'sel_list': [True, False],
'label_list': ['test label', 'lbl2'],
}
# Print the keyword arguments to illustrate their format
print('showkw = ' + utool.dict_str(showkw))
# Display the image in figure-num 42, using a 1x1 axis grid in the first
# axis. Pass showkw as keyword arguments.
viz_image2.show_image(img, fnum=42, pnum=(1, 1, 1), **showkw)
df2.set_figtitle('Test figure title')
def test_viz_image(img_fpath):
# Read image
img = cv2.imread(img_fpath)
tau = np.pi * 2 # References: tauday.com
# Create figure
fig = df2.figure(fnum=42, pnum=(1, 1, 1))
# Clear figure
fig.clf()
# Build parameters
bbox_list = [dummy_bbox(img), dummy_bbox(img, (-0.25, -0.25), 0.1)]
showkw = {
"title": "test axis title",
# The list of bounding boxes to be drawn on the image
"bbox_list": bbox_list,
"theta_list": [tau * 0.7, tau * 0.9],
"sel_list": [True, False],
"label_list": ["test label", "lbl2"],
}
# Print the keyword arguments to illustrate their format
print("showkw = " + utool.dict_str(showkw))
# Display the image in figure-num 42, using a 1x1 axis grid in the first
# axis. Pass showkw as keyword arguments.
viz_image2.show_image(img, fnum=42, pnum=(1, 1, 1), **showkw)
df2.set_figtitle("Test figure title")
def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel):
import pyhesaff
import vtool.patch as ptool
from plottool import draw_func2 as df2
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch, gaussian_weighted=False)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
gori_weights = ptool.gaussian_weight_patch(gmag)
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
# Get dominant direction in radians
kpts2 = TEST_ptool_find_kpts_direction(imgBGR, kpts)
kpts2, desc2 = pyhesaff.vtool_adapt_rotation(img_fpath, kpts)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers)
df2.set_figtitle('Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=2)
df2.set_figtitle('Gravity Vector')
TEST_figure2(imgBGR, kpts2, desc2, sel, fnum=3)
df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def test_sver(chip1, chip2, kpts1, kpts2, fm, nShow=6):
r"""
Args:
chip1 (ndarray[uint8_t, ndim=2]): annotation image data
chip2 (ndarray[uint8_t, ndim=2]): annotation image data
kpts1 (ndarray[float32_t, ndim=2]): keypoints
kpts2 (ndarray[float32_t, ndim=2]): keypoints
fm (list): list of feature matches as tuples (qfx, dfx)
nShow (int):
Returns:
?:
CommandLine:
python -m vtool.tests.test_spatial_verification --test-test_sver:0 --show
python -m vtool.tests.test_spatial_verification --test-test_sver:1 --show
Example0:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from vtool.tests.test_spatial_verification import * # NOQA
>>> # build test data
>>> nShow = utool.get_argval('--nShow', int, 1)
>>> chip1, chip2, kpts1, kpts2, fm = get_dummy_test_vars()
>>> # execute function
>>> result = test_sver(chip1, chip2, kpts1, kpts2, fm, nShow)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
Example1:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from vtool.tests.test_spatial_verification import * # NOQA
>>> # build test data
>>> nShow = utool.get_argval('--nShow', int, 1)
>>> chip1, chip2, kpts1, kpts2, fm = get_dummy_test_vars1()
>>> # execute function
>>> result = test_sver(chip1, chip2, kpts1, kpts2, fm, nShow)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
xy_thresh_sqrd = ktool.get_diag_extent_sqrd(kpts2) * xy_thresh
def pack_errors(xy_err, scale_err, ori_err):
""" makes human readable errors """
def _pack(bits, errs, thresh):
return utool.indentjoin(['%5s %f < %f' % (bit, err, thresh) for
(bit, err) in zip(bits, errs)])
xy_flag = xy_err < xy_thresh_sqrd
scale_flag = scale_err < scale_thresh_sqrd
ori_flag = ori_err < ori_thresh
errors_dict = {
'xy_err': _pack(xy_flag, np.sqrt(xy_err), np.sqrt(xy_thresh_sqrd)),
'scale_err': _pack(scale_flag, np.sqrt(scale_err), np.sqrt(scale_thresh_sqrd)),
'ori_err': _pack(ori_flag, ori_err, ori_thresh),
}
return errors_dict
# Test each affine hypothesis
#assert kpts1.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere kpts1.dtype=%r' % (kpts1.dtype)
#assert kpts2.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere kpts2.dtype=%r' % (kpts2.dtype)
#assert xy_thresh_sqrd.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere
#xy_thresh_sqrd.dtype=%r' % (xy_thresh_sqrd.dtype)
aff_hypo_tups = sver.get_affine_inliers(kpts1, kpts2, fm, xy_thresh_sqrd,
scale_thresh_sqrd, ori_thresh)
inliers_list, errors_list, Aff_mats = aff_hypo_tups
# Determine best hypothesis
nInliers_list = np.array(list(map(len, inliers_list)))
best_mxs = nInliers_list.argsort()[::-1]
for fnum, mx in enumerate(best_mxs[0:min(len(best_mxs), nShow)]):
Aff = Aff_mats[mx]
aff_inliers = inliers_list[mx]
if utool.get_argflag('--print-error'):
errors = pack_errors(*errors_list[mx]) # NOQA
print(utool.dict_str(errors, strvals=True))
homog_inliers, homog_errors, H = sver.get_homography_inliers(kpts1,
kpts2, fm,
aff_inliers,
xy_thresh_sqrd)
kpts1_At = ktool.transform_kpts(kpts1, Aff)
kpts1_Ht = ktool.transform_kpts(kpts1, H)
kpts = kpts1
M = H
homog_tup = (homog_inliers, H)
aff_tup = (aff_inliers, Aff)
_args = (chip1, chip2, kpts1, kpts2, fm)
_kw = dict(show_assign=True, show_kpts=True, mx=mx, fnum=fnum * 3)
draw_sv.show_sv(*_args, aff_tup=aff_tup, homog_tup=homog_tup, **_kw)
#draw_sv.show_sv(*_args, aff_tup=aff_tup, mx=mx, fnum=fnum * 3)
#draw_sv.show_sv(*_args, homog_tup=homog_tup, mx=mx, fnum=3)
df2.set_figtitle('# %r inliers (in rects, hypo in bold)' % (nInliers_list[mx],))
return locals()
def test_sver(chip1, chip2, kpts1, kpts2, fm, nShow=6):
r"""
Args:
chip1 (ndarray[uint8_t, ndim=2]): annotation image data
chip2 (ndarray[uint8_t, ndim=2]): annotation image data
kpts1 (ndarray[float32_t, ndim=2]): keypoints
kpts2 (ndarray[float32_t, ndim=2]): keypoints
fm (list): list of feature matches as tuples (qfx, dfx)
nShow (int):
Returns:
?:
CommandLine:
python -m vtool.tests.test_spatial_verification --test-test_sver:0 --show
python -m vtool.tests.test_spatial_verification --test-test_sver:1 --show
Example0:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from vtool.tests.test_spatial_verification import * # NOQA
>>> # build test data
>>> nShow = utool.get_argval('--nShow', int, 1)
>>> chip1, chip2, kpts1, kpts2, fm = get_dummy_test_vars()
>>> # execute function
>>> result = test_sver(chip1, chip2, kpts1, kpts2, fm, nShow)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
Example1:
>>> # DISABLE_DOCTEST
>>> import plottool as pt
>>> from vtool.tests.test_spatial_verification import * # NOQA
>>> # build test data
>>> nShow = utool.get_argval('--nShow', int, 1)
>>> chip1, chip2, kpts1, kpts2, fm = get_dummy_test_vars1()
>>> # execute function
>>> result = test_sver(chip1, chip2, kpts1, kpts2, fm, nShow)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
xy_thresh_sqrd = ktool.get_diag_extent_sqrd(kpts2) * xy_thresh
def pack_errors(xy_err, scale_err, ori_err):
""" makes human readable errors """
def _pack(bits, errs, thresh):
return utool.indentjoin([
'%5s %f < %f' % (bit, err, thresh)
for (bit, err) in zip(bits, errs)
])
xy_flag = xy_err < xy_thresh_sqrd
scale_flag = scale_err < scale_thresh_sqrd
ori_flag = ori_err < ori_thresh
errors_dict = {
'xy_err':
_pack(xy_flag, np.sqrt(xy_err), np.sqrt(xy_thresh_sqrd)),
'scale_err':
_pack(scale_flag, np.sqrt(scale_err), np.sqrt(scale_thresh_sqrd)),
'ori_err':
_pack(ori_flag, ori_err, ori_thresh),
}
return errors_dict
# Test each affine hypothesis
#assert kpts1.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere kpts1.dtype=%r' % (kpts1.dtype)
#assert kpts2.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere kpts2.dtype=%r' % (kpts2.dtype)
#assert xy_thresh_sqrd.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere
#xy_thresh_sqrd.dtype=%r' % (xy_thresh_sqrd.dtype)
aff_hypo_tups = sver.get_affine_inliers(kpts1, kpts2, fm, xy_thresh_sqrd,
scale_thresh_sqrd, ori_thresh)
inliers_list, errors_list, Aff_mats = aff_hypo_tups
# Determine best hypothesis
nInliers_list = np.array(list(map(len, inliers_list)))
best_mxs = nInliers_list.argsort()[::-1]
for fnum, mx in enumerate(best_mxs[0:min(len(best_mxs), nShow)]):
Aff = Aff_mats[mx]
aff_inliers = inliers_list[mx]
if utool.get_argflag('--print-error'):
errors = pack_errors(*errors_list[mx]) # NOQA
print(utool.dict_str(errors, strvals=True))
homog_inliers, homog_errors, H = sver.get_homography_inliers(
kpts1, kpts2, fm, aff_inliers, xy_thresh_sqrd)
kpts1_At = ktool.transform_kpts(kpts1, Aff)
kpts1_Ht = ktool.transform_kpts(kpts1, H)
kpts = kpts1
M = H
homog_tup = (homog_inliers, H)
aff_tup = (aff_inliers, Aff)
_args = (chip1, chip2, kpts1, kpts2, fm)
_kw = dict(show_assign=True, show_kpts=True, mx=mx, fnum=fnum * 3)
draw_sv.show_sv(*_args, aff_tup=aff_tup, homog_tup=homog_tup, **_kw)
#draw_sv.show_sv(*_args, aff_tup=aff_tup, mx=mx, fnum=fnum * 3)
#draw_sv.show_sv(*_args, homog_tup=homog_tup, mx=mx, fnum=3)
df2.set_figtitle('# %r inliers (in rects, hypo in bold)' %
(nInliers_list[mx], ))
return locals()
def show_name(ibs,
nid,
in_image=True,
fnum=0,
sel_aids=[],
subtitle='',
annote=False,
aid_list=None,
index_list=None,
**kwargs):
r"""
Args:
ibs (IBEISController): ibeis controller object
nid (?):
in_image (bool):
fnum (int): figure number
sel_aids (list):
subtitle (str):
annote (bool):
CommandLine:
python -m ibeis.viz.viz_name --test-show_name --dpath ~/latex/crall-candidacy-2015 --save 'figures/{name}.jpg' --no-figtitle --notitle --db NNP_Master3 --figsize=9,4 --clipwhite --dpi=180 --adjust=.05 --index_list=[0,1,2,3] --rc=2,4 --append temp_out_figure.tex --name=IBEIS_PZ_0739 --no-draw_lbls --doboth --no-inimage --diskshow
python -m ibeis.viz.viz_name --test-show_name --no-figtitle --notitle --db NNP_Master3 --figsize=9,4 --clipwhite --dpi=180 --adjust=.05 --index_list=[0,1,2,3] --rc=2,4 --append temp_out_figure.tex --name=IBEIS_PZ_0739 --no-draw_lbls --doboth --no-inimage --show
python -m ibeis.viz.viz_name --test-show_name --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_name import * # NOQA
>>> ibs, nid, in_image, index_list = testdata_showname()
>>> fnum = 0
>>> sel_aids = []
>>> subtitle = ''
>>> annote = False
>>> # execute function
>>> show_name(ibs, nid, in_image, fnum, sel_aids, subtitle, annote, index_list=index_list)
>>> ut.show_if_requested()
"""
print('[viz_name] show_name nid=%r, index_list=%r, aid_list=%r' %
(nid, index_list, aid_list))
if aid_list is None:
aid_list = ibs.get_name_aids(nid)
else:
assert ut.list_all_eq_to(ibs.get_annot_nids(aid_list), nid)
if index_list is not None:
aid_list = ut.take(aid_list, index_list)
name = ibs.get_name_texts((nid, ))
print('[viz_name] * name=%r aid_list=%r' % (name, aid_list))
show_multiple_chips(ibs,
aid_list,
in_image=in_image,
fnum=fnum,
sel_aids=sel_aids,
annote=annote,
**kwargs)
if isinstance(nid, np.ndarray):
nid = nid[0]
if isinstance(name, np.ndarray):
name = name[0]
use_figtitle = not ut.get_argflag('--no-figtitle')
if use_figtitle:
figtitle = 'Name View nid=%r name=%r' % (nid, name)
df2.set_figtitle(figtitle)
def dump_feature_pair_analysis(allres):
print('[rr2] Doing: feature pair analysis')
# TODO: Measure score consistency over a spatial area.
# Measures entropy of matching vs nonmatching descriptors
# Measures scale of m vs nm desc
ibs = allres.ibs
qrid2_qres = allres.qrid2_qres
def _hist_prob_x(desc, bw_factor):
# Choose number of bins based on the bandwidth
bin_range = (0, 256) # assuming input is uint8
bins = bin_range[1] // bw_factor
bw_factor = bin_range[1] / bins
# Compute the probabilty mass function, each w.r.t a single descriptor
hist_params = dict(bins=bins, range=bin_range, density=True)
hist_func = np.histogram
desc_pmf = [hist_func(d, **hist_params)[0] for d in desc]
# Compute the probability that you saw what you saw
# TODO: could use linear interpolation for a bit more robustness here
bin_vals = [
np.array(np.floor(d / bw_factor), dtype=np.uint8) for d in desc
]
hist_prob_x = [pmf[vals] for pmf, vals in zip(desc_pmf, bin_vals)]
return hist_prob_x
def _gkde_prob_x(desc, bw_factor):
# Estimate the probabilty density function, each w.r.t a single descriptor
gkde_func = scipy.stats.gaussian_kde
desc_pdf = [gkde_func(d, bw_factor) for d in desc]
gkde_prob_x = [pdf(d) for pdf, d in zip(desc_pdf, desc)]
return gkde_prob_x
def descriptor_entropy(desc, bw_factor=4):
'computes the shannon entropy of each descriptor in desc'
# Compute shannon entropy = -sum(p(x)*log(p(x)))
prob_x = _hist_prob_x(desc, bw_factor)
entropy = [-(px * np.log2(px)).sum() for px in prob_x]
return entropy
# Load features if we need to
if ibs.feats.cx2_desc.size == 0:
print(' * forcing load of descriptors')
ibs.load_features()
cx2_desc = ibs.feats.cx2_desc
cx2_kpts = ibs.feats.cx2_kpts
def measure_feat_pairs(allres, orgtype='top_true'):
print('Measure ' + orgtype + ' pairs')
orgres = allres.__dict__[orgtype]
entropy_list = []
scale_list = []
score_list = []
lbl = 'Measuring ' + orgtype + ' pair '
fmt_str = utool.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qrid, rid, score, rank) in enumerate(orgres.iter()):
utool.print_(fmt_str % (ix + 1, ))
# Skip low ranks
if rank > 5:
rank_skips.append(qrid)
continue
other_rids = ibs.get_other_indexed_rids(qrid)
# Skip no groundtruth
if len(other_rids) == 0:
gt_skips.append(qrid)
continue
qres = qrid2_qres[qrid]
# Get matching feature indexes
fm = qres.cx2_fm[rid]
# Get their scores
fs = qres.cx2_fs[rid]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape, ))
desc1 = cx2_desc[qrid][fm[:, 0]]
desc2 = cx2_desc[rid][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qrid][fm[:, 0]]
kpts2 = cx2_kpts[rid][fm[:, 1]]
# Get their scale
scale1_m = ktool.get_scales(kpts1)
scale2_m = ktool.get_scales(kpts2)
# Get their entropy
entropy1 = descriptor_entropy(desc1, bw_factor=1)
entropy2 = descriptor_entropy(desc2, bw_factor=1)
# Append to results
entropy_tup = np.array(zip(entropy1, entropy2))
scale_tup = np.array(zip(scale1_m, scale2_m))
entropy_tup = entropy_tup.reshape(len(entropy_tup), 2)
scale_tup = scale_tup.reshape(len(scale_tup), 2)
entropy_list.append(entropy_tup)
scale_list.append(scale_tup)
score_list.append(fs)
print('Skipped %d total.' % (len(rank_skips) + len(gt_skips), ))
print('Skipped %d for rank > 5, %d for no gt' % (
len(rank_skips),
len(gt_skips),
))
print(np.unique(map(len, entropy_list)))
def evstack(tup):
return np.vstack(tup) if len(tup) > 0 else np.empty((0, 2))
def ehstack(tup):
return np.hstack(tup) if len(tup) > 0 else np.empty((0, 2))
entropy_pairs = evstack(entropy_list)
scale_pairs = evstack(scale_list)
scores = ehstack(score_list)
print('\n * Measured %d pairs' % len(entropy_pairs))
return entropy_pairs, scale_pairs, scores
tt_entropy, tt_scale, tt_scores = measure_feat_pairs(allres, 'top_true')
tf_entropy, tf_scale, tf_scores = measure_feat_pairs(allres, 'top_false')
# Measure ratios
def measure_ratio(arr):
return arr[:, 0] / arr[:, 1] if len(arr) > 0 else np.array([])
tt_entropy_ratio = measure_ratio(tt_entropy)
tf_entropy_ratio = measure_ratio(tf_entropy)
tt_scale_ratio = measure_ratio(tt_scale)
tf_scale_ratio = measure_ratio(tf_scale)
title_suffix = allres.title_suffix
# Entropy vs Score
df2.figure(fnum=1, docla=True)
df2.figure(fnum=1, plotnum=(2, 2, 1))
df2.plot2(tt_entropy[:, 0], tt_scores, 'gx', 'entropy1', 'score',
'Top True')
df2.figure(fnum=1, plotnum=(2, 2, 2))
df2.plot2(tf_entropy[:, 0], tf_scores, 'rx', 'entropy1', 'score',
'Top False')
df2.figure(fnum=1, plotnum=(2, 2, 3))
df2.plot2(tt_entropy[:, 1], tt_scores, 'gx', 'entropy2', 'score',
'Top True')
df2.figure(fnum=1, plotnum=(2, 2, 4))
df2.plot2(tf_entropy[:, 1], tf_scores, 'rx', 'entropy2', 'score',
'Top False')
df2.set_figtitle('Entropy vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
# Scale vs Score
df2.figure(fnum=2, plotnum=(2, 2, 1), docla=True)
df2.plot2(tt_scale[:, 0], tt_scores, 'gx', 'scale1', 'score', 'Top True')
df2.figure(fnum=2, plotnum=(2, 2, 2))
df2.plot2(tf_scale[:, 0], tf_scores, 'rx', 'scale1', 'score', 'Top False')
df2.figure(fnum=2, plotnum=(2, 2, 3))
df2.plot2(tt_scale[:, 1], tt_scores, 'gx', 'scale2', 'score', 'Top True')
df2.figure(fnum=2, plotnum=(2, 2, 4))
df2.plot2(tf_scale[:, 1], tf_scores, 'rx', 'scale2', 'score', 'Top False')
df2.set_figtitle('Scale vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
# Entropy Ratio vs Score
df2.figure(fnum=3, plotnum=(1, 2, 1), docla=True)
df2.plot2(tt_entropy_ratio, tt_scores, 'gx', 'entropy-ratio', 'score',
'Top True')
df2.figure(fnum=3, plotnum=(1, 2, 2))
df2.plot2(tf_entropy_ratio, tf_scores, 'rx', 'entropy-ratio', 'score',
'Top False')
df2.set_figtitle('Entropy Ratio vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
# Scale Ratio vs Score
df2.figure(fnum=4, plotnum=(1, 2, 1), docla=True)
df2.plot2(tt_scale_ratio, tt_scores, 'gx', 'scale-ratio', 'score',
'Top True')
df2.figure(fnum=4, plotnum=(1, 2, 2))
df2.plot2(tf_scale_ratio, tf_scores, 'rx', 'scale-ratio', 'score',
'Top False')
df2.set_figtitle('Entropy Ratio vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
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))
def _viz_keypoints(fnum, pnum=(1, 1, 1), **kwargs):
df2.figure(fnum=fnum, docla=True, doclf=True)
show_keypoints(chip, kpts, fnum=fnum, pnum=pnum, **kwargs)
if figtitle is not None:
df2.set_figtitle(figtitle)
def show_descriptors_match_distances(orgres2_distance, fnum=1, db_name='', **kwargs):
disttype_list = orgres2_distance.itervalues().next().keys()
orgtype_list = orgres2_distance.keys()
(nRow, nCol) = len(orgtype_list), len(disttype_list)
nColors = nRow * nCol
color_list = df2.distinct_colors(nColors)
df2.figure(fnum=fnum, docla=True, doclf=True)
pnum_ = lambda px: (nRow, nCol, px + 1)
plot_type = utool.get_arg('--plot-type', default='plot')
# Remember min and max val for each distance type (l1, emd...)
distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list}
distkey2_max = {distkey: 0 for distkey in disttype_list}
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label, yscale='linear')
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper left')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
px = 0
for orgkey in orgtype_list:
for distkey in disttype_list:
dists = orgres2_distance[orgkey][distkey]
if len(dists) == 0:
continue
min_ = dists.min()
max_ = dists.max()
distkey2_min[distkey] = min(distkey2_min[distkey], min_)
distkey2_max[distkey] = max(distkey2_max[distkey], max_)
for count, orgkey in enumerate(orgtype_list):
for distkey in disttype_list:
printDBG('[allres-viz] plotting: %r' % ((orgkey, distkey),))
dists = orgres2_distance[orgkey][distkey]
df2.figure(fnum=fnum, pnum=pnum_(px))
color = color_list[px]
title = distkey + ' ' + orgkey
label = 'P(%s | %s)' % (distkey, orgkey)
_distplot(dists, color, label, distkey, **kwargs)
if count == 0:
ax = df2.gca()
ax.set_title(distkey)
px += 1
subtitle = 'the matching distances between sift descriptors'
title = '(sift) matching distances'
if db_name != '':
title = db_name + ' ' + title
df2.set_figtitle(title, subtitle)
df2.adjust_subplots_safe()
def dump_feature_pair_analysis(allres):
print('[rr2] Doing: feature pair analysis')
# TODO: Measure score consistency over a spatial area.
# Measures entropy of matching vs nonmatching descriptors
# Measures scale of m vs nm desc
ibs = allres.ibs
qrid2_qres = allres.qrid2_qres
def _hist_prob_x(desc, bw_factor):
# Choose number of bins based on the bandwidth
bin_range = (0, 256) # assuming input is uint8
bins = bin_range[1] // bw_factor
bw_factor = bin_range[1] / bins
# Compute the probabilty mass function, each w.r.t a single descriptor
hist_params = dict(bins=bins, range=bin_range, density=True)
hist_func = np.histogram
desc_pmf = [hist_func(d, **hist_params)[0] for d in desc]
# Compute the probability that you saw what you saw
# TODO: could use linear interpolation for a bit more robustness here
bin_vals = [np.array(np.floor(d / bw_factor), dtype=np.uint8) for d in desc]
hist_prob_x = [pmf[vals] for pmf, vals in zip(desc_pmf, bin_vals)]
return hist_prob_x
def _gkde_prob_x(desc, bw_factor):
# Estimate the probabilty density function, each w.r.t a single descriptor
gkde_func = scipy.stats.gaussian_kde
desc_pdf = [gkde_func(d, bw_factor) for d in desc]
gkde_prob_x = [pdf(d) for pdf, d in zip(desc_pdf, desc)]
return gkde_prob_x
def descriptor_entropy(desc, bw_factor=4):
'computes the shannon entropy of each descriptor in desc'
# Compute shannon entropy = -sum(p(x)*log(p(x)))
prob_x = _hist_prob_x(desc, bw_factor)
entropy = [-(px * np.log2(px)).sum() for px in prob_x]
return entropy
# Load features if we need to
if ibs.feats.cx2_desc.size == 0:
print(' * forcing load of descriptors')
ibs.load_features()
cx2_desc = ibs.feats.cx2_desc
cx2_kpts = ibs.feats.cx2_kpts
def measure_feat_pairs(allres, orgtype='top_true'):
print('Measure ' + orgtype + ' pairs')
orgres = allres.__dict__[orgtype]
entropy_list = []
scale_list = []
score_list = []
lbl = 'Measuring ' + orgtype + ' pair '
fmt_str = utool.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qrid, rid, score, rank) in enumerate(orgres.iter()):
utool.print_(fmt_str % (ix + 1,))
# Skip low ranks
if rank > 5:
rank_skips.append(qrid)
continue
other_rids = ibs.get_other_indexed_rids(qrid)
# Skip no groundtruth
if len(other_rids) == 0:
gt_skips.append(qrid)
continue
qres = qrid2_qres[qrid]
# Get matching feature indexes
fm = qres.cx2_fm[rid]
# Get their scores
fs = qres.cx2_fs[rid]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape,))
desc1 = cx2_desc[qrid][fm[:, 0]]
desc2 = cx2_desc[rid][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qrid][fm[:, 0]]
kpts2 = cx2_kpts[rid][fm[:, 1]]
# Get their scale
scale1_m = ktool.get_scales(kpts1)
scale2_m = ktool.get_scales(kpts2)
# Get their entropy
entropy1 = descriptor_entropy(desc1, bw_factor=1)
entropy2 = descriptor_entropy(desc2, bw_factor=1)
# Append to results
entropy_tup = np.array(zip(entropy1, entropy2))
scale_tup = np.array(zip(scale1_m, scale2_m))
entropy_tup = entropy_tup.reshape(len(entropy_tup), 2)
scale_tup = scale_tup.reshape(len(scale_tup), 2)
entropy_list.append(entropy_tup)
scale_list.append(scale_tup)
score_list.append(fs)
print('Skipped %d total.' % (len(rank_skips) + len(gt_skips),))
print('Skipped %d for rank > 5, %d for no gt' % (len(rank_skips), len(gt_skips),))
print(np.unique(map(len, entropy_list)))
def evstack(tup):
return np.vstack(tup) if len(tup) > 0 else np.empty((0, 2))
def ehstack(tup):
return np.hstack(tup) if len(tup) > 0 else np.empty((0, 2))
entropy_pairs = evstack(entropy_list)
scale_pairs = evstack(scale_list)
scores = ehstack(score_list)
print('\n * Measured %d pairs' % len(entropy_pairs))
return entropy_pairs, scale_pairs, scores
tt_entropy, tt_scale, tt_scores = measure_feat_pairs(allres, 'top_true')
tf_entropy, tf_scale, tf_scores = measure_feat_pairs(allres, 'top_false')
# Measure ratios
def measure_ratio(arr):
return arr[:, 0] / arr[:, 1] if len(arr) > 0 else np.array([])
tt_entropy_ratio = measure_ratio(tt_entropy)
tf_entropy_ratio = measure_ratio(tf_entropy)
tt_scale_ratio = measure_ratio(tt_scale)
tf_scale_ratio = measure_ratio(tf_scale)
title_suffix = allres.title_suffix
# Entropy vs Score
df2.figure(fnum=1, docla=True)
df2.figure(fnum=1, plotnum=(2, 2, 1))
df2.plot2(tt_entropy[:, 0], tt_scores, 'gx', 'entropy1', 'score', 'Top True')
df2.figure(fnum=1, plotnum=(2, 2, 2))
df2.plot2(tf_entropy[:, 0], tf_scores, 'rx', 'entropy1', 'score', 'Top False')
df2.figure(fnum=1, plotnum=(2, 2, 3))
df2.plot2(tt_entropy[:, 1], tt_scores, 'gx', 'entropy2', 'score', 'Top True')
df2.figure(fnum=1, plotnum=(2, 2, 4))
df2.plot2(tf_entropy[:, 1], tf_scores, 'rx', 'entropy2', 'score', 'Top False')
df2.set_figtitle('Entropy vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
# Scale vs Score
df2.figure(fnum=2, plotnum=(2, 2, 1), docla=True)
df2.plot2(tt_scale[:, 0], tt_scores, 'gx', 'scale1', 'score', 'Top True')
df2.figure(fnum=2, plotnum=(2, 2, 2))
df2.plot2(tf_scale[:, 0], tf_scores, 'rx', 'scale1', 'score', 'Top False')
df2.figure(fnum=2, plotnum=(2, 2, 3))
df2.plot2(tt_scale[:, 1], tt_scores, 'gx', 'scale2', 'score', 'Top True')
df2.figure(fnum=2, plotnum=(2, 2, 4))
df2.plot2(tf_scale[:, 1], tf_scores, 'rx', 'scale2', 'score', 'Top False')
df2.set_figtitle('Scale vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
# Entropy Ratio vs Score
df2.figure(fnum=3, plotnum=(1, 2, 1), docla=True)
df2.plot2(tt_entropy_ratio, tt_scores, 'gx', 'entropy-ratio', 'score', 'Top True')
df2.figure(fnum=3, plotnum=(1, 2, 2))
df2.plot2(tf_entropy_ratio, tf_scores, 'rx', 'entropy-ratio', 'score', 'Top False')
df2.set_figtitle('Entropy Ratio vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
# Scale Ratio vs Score
df2.figure(fnum=4, plotnum=(1, 2, 1), docla=True)
df2.plot2(tt_scale_ratio, tt_scores, 'gx', 'scale-ratio', 'score', 'Top True')
df2.figure(fnum=4, plotnum=(1, 2, 2))
df2.plot2(tf_scale_ratio, tf_scores, 'rx', 'scale-ratio', 'score', 'Top False')
df2.set_figtitle('Entropy Ratio vs Score -- ' + title_suffix)
viz.__dump_or_browse(allres, 'pair_analysis')
def plot_scores2(hs, qcx_list, fnum=1):
print('[dev] plot_scores(fnum=%r)' % fnum)
qcx2_res = get_qcx2_res(hs, qcx_list)
all_score_list = []
gtscore_ys = []
gtscore_xs = []
gtscore_ranks = []
EXCLUDE_ZEROS = False
N = 1
# Append all scores to a giant list
for res in qcx2_res.itervalues():
cx2_score = res.cx2_score
# Get gt scores first
#gt_cxs = hs.get_other_indexed_cxs(res.qcx)
gt_cxs = np.array(res.topN_cxs(hs, N=N, only_gt=True))
gt_ys = cx2_score[gt_cxs]
if EXCLUDE_ZEROS:
nonzero_cxs = np.where(cx2_score != 0)[0]
gt_cxs = gt_cxs[gt_ys != 0]
gt_ranks = res.get_gt_ranks(gt_cxs)
gt_cxs = np.array(utool.list_index(nonzero_cxs, gt_cxs))
gt_ys = gt_ys[gt_ys != 0]
score_list = cx2_score[nonzero_cxs].tolist()
else:
score_list = cx2_score.tolist()
gt_ranks = res.get_gt_ranks(gt_cxs)
gtscore_ys.extend(gt_ys)
gtscore_xs.extend(gt_cxs + len(all_score_list))
gtscore_ranks.extend(gt_ranks)
# Append all scores
all_score_list.extend(score_list)
all_score_list = np.array(all_score_list)
gtscore_ranks = np.array(gtscore_ranks)
gtscore_ys = np.array(gtscore_ys)
# Sort all chipmatch scores
allx_sorted = all_score_list.argsort() # mapping from sortedallx to allx
allscores_sorted = all_score_list[allx_sorted]
# Change the groundtruth positions to correspond to sorted cmatch scores
# Find position of gtscore_xs in allx_sorted
gtscore_sortxs = utool.list_index(allx_sorted, gtscore_xs)
gtscore_sortxs = np.array(gtscore_sortxs)
# Draw and info
rank_bounds = [
(0, 1),
(1, 5),
(5, None)
]
rank_colors = [
df2.TRUE_GREEN,
df2.UNKNOWN_PURP,
df2.FALSE_RED
]
print('[dev] matching chipscore stats: ' + utool.stats_str(all_score_list))
df2.figure(fnum=fnum, doclf=True, docla=True)
# Finds the knee
df2.plot(allscores_sorted, color=df2.ORANGE, label='all scores')
# get positions which are within rank bounds
for count, ((low, high), rankX_color) in reversed(list(enumerate(zip(rank_bounds, rank_colors)))):
rankX_flag_low = gtscore_ranks >= low
if high is not None:
rankX_flag_high = gtscore_ranks < high
rankX_flag = np.logical_and(rankX_flag_low, rankX_flag_high)
else:
rankX_flag = rankX_flag_low
rankX_allgtx = np.where(rankX_flag)[0]
rankX_gtxs = gtscore_sortxs[rankX_allgtx]
rankX_gtys = gtscore_ys[rankX_allgtx]
rankX_label = '%d <= gt rank' % low
if high is not None:
rankX_label += ' < %d' % high
if len(rankX_gtxs) > 0:
df2.plot(rankX_gtxs, rankX_gtys, 'o', color=rankX_color, label=rankX_label)
rowid = qcx2_res.itervalues().next().rowid
df2.set_logyscale_from_data(allscores_sorted)
df2.set_xlabel('chipmatch index')
df2.dark_background()
df2.set_figtitle('matching scores\n' + rowid)
df2.legend(loc='upper left')
#xmin = 0
#xmax = utool.order_of_magnitude_ceil(len(allscores_sorted))
#print('len_ = %r' % (len(allscores_sorted),))
#print('xmin = %r' % (xmin,))
#print('xmax = %r' % (xmax,))
#df2.gca().set_xlim(xmin, xmax)
df2.update()
#utool.embed()
# Second Plot
#data = sorted(zip(gtscore_sortxs, gtscore_ys, gtscore_ranks))
#gtxs = [x for (x, y, z) in data]
#gtys = [y for (x, y, z) in data]
#gtrs = [z for (x, y, z) in data]
#nongtxs = np.setdiff1d(np.arange(gtxs[0], gtxs[-1]), gtxs)
#min_ = min(gtxs)
#max_ = len(allscores_sorted)
#len_ = max_ - min_
#normsum = 0
#ratsum = 0
#gtxs = np.array(gtxs)
#nongtxs = np.array(nongtxs)
#for ix in xrange(min_, max_):
#nongtxs_ = nongtxs[nongtxs >= ix]
#gtxs_ = gtxs[gtxs >= ix]
#numer = allscores_sorted[gtxs_].sum()
#denom = allscores_sorted[nongtxs_].sum()
#ratio = (1 + numer) / (denom + 1)
#total_support = (len(gtxs_) + len(nongtxs_))
#normrat = ratio / total_support
#ratsum += ratio
#normsum += normrat
#print(total_support)
#print(ratsum / len_)
#print(normsum / len_)
#print(ratsum / allscores_sorted[min_:max_].sum())
#print(normsum / allscores_sorted[min_:max_].sum())
#index_gap = np.diff(gtxs)
#score_gap = np.diff(gtys)
#badness = (index_gap - 1) * score_gap
#np.arange(len(gtxs))
fnum += 1
return fnum
def show_qres(ibs, qres, **kwargs):
"""
Display Query Result Logic
Defaults to: query chip, groundtruth matches, and top 5 matches
"""
annote_mode = kwargs.get('annote_mode', 1) % 3 # this is toggled
figtitle = kwargs.get('figtitle', '')
aug = kwargs.get('aug', '')
top_aids = kwargs.get('top_aids', 6)
gt_aids = kwargs.get('gt_aids', [])
all_kpts = kwargs.get('all_kpts', False)
show_query = kwargs.get('show_query', False)
in_image = kwargs.get('in_image', False)
sidebyside = kwargs.get('sidebyside', True)
fnum = df2.kwargs_fnum(kwargs)
fig = df2.figure(fnum=fnum, docla=True, doclf=True)
if isinstance(top_aids, int):
top_aids = qres.get_top_aids(num=top_aids)
all_gts = ibs.get_annot_groundtruth(qres.qaid)
nTop = len(top_aids)
nSelGt = len(gt_aids)
nAllGt = len(all_gts)
max_nCols = 5
if nTop in [6, 7]:
max_nCols = 3
if nTop in [8]:
max_nCols = 4
printDBG('[show_qres]========================')
printDBG('[show_qres]----------------')
printDBG('[show_qres] #nTop=%r #missed_gts=%r/%r' % (nTop, nSelGt, nAllGt))
printDBG('[show_qres] * fnum=%r' % (fnum,))
printDBG('[show_qres] * figtitle=%r' % (figtitle,))
printDBG('[show_qres] * max_nCols=%r' % (max_nCols,))
printDBG('[show_qres] * show_query=%r' % (show_query,))
printDBG('[show_qres] * kwargs=%s' % (utool.dict_str(kwargs),))
printDBG(qres.get_inspect_str())
ranked_aids = qres.get_top_aids()
# Build a subplot grid
nQuerySubplts = 1 if show_query else 0
nGtSubplts = nQuerySubplts + (0 if gt_aids is None else len(gt_aids))
nTopNSubplts = nTop
nTopNCols = min(max_nCols, nTopNSubplts)
nGTCols = min(max_nCols, nGtSubplts)
nGTCols = max(nGTCols, nTopNCols)
nTopNCols = nGTCols
nGtRows = 0 if nGTCols == 0 else int(np.ceil(nGtSubplts / nGTCols))
nTopNRows = 0 if nTopNCols == 0 else int(np.ceil(nTopNSubplts / nTopNCols))
nGtCells = nGtRows * nGTCols
nRows = nTopNRows + nGtRows
# HACK:
_color_list = df2.distinct_colors(nTop)
aid2_color = {aid: _color_list[ox] for ox, aid in enumerate(top_aids)}
# Helpers
def _show_query_fn(plotx_shift, rowcols):
""" helper for show_qres """
plotx = plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
#print('[viz] Plotting Query: pnum=%r' % (pnum,))
_kwshow = dict(draw_kpts=annote_mode)
_kwshow.update(kwargs)
_kwshow['prefix'] = 'q'
_kwshow['pnum'] = pnum
_kwshow['aid2_color'] = aid2_color
_kwshow['draw_ell'] = annote_mode >= 1
viz_chip.show_chip(ibs, qres.qaid, annote=False, **_kwshow)
def _plot_matches_aids(aid_list, plotx_shift, rowcols):
""" helper for show_qres to draw many aids """
def _show_matches_fn(aid, orank, pnum):
""" Helper function for drawing matches to one aid """
aug = 'rank=%r\n' % orank
#printDBG('[show_qres()] plotting: %r' % (pnum,))
_kwshow = dict(draw_ell=annote_mode, draw_pts=False, draw_lines=annote_mode,
ell_alpha=.5, all_kpts=all_kpts)
_kwshow.update(kwargs)
_kwshow['fnum'] = fnum
_kwshow['pnum'] = pnum
_kwshow['title_aug'] = aug
# If we already are showing the query dont show it here
if sidebyside:
_kwshow['draw_ell'] = annote_mode == 1
_kwshow['draw_lines'] = annote_mode >= 1
viz_matches.show_matches(ibs, qres, aid, in_image=in_image, **_kwshow)
else:
_kwshow['draw_ell'] = annote_mode >= 1
if annote_mode == 2:
# TODO Find a better name
_kwshow['color'] = aid2_color[aid]
_kwshow['sel_fx2'] = qres.aid2_fm[aid][:, 1]
viz_chip.show_chip(ibs, aid, in_image=in_image, annote=False, **_kwshow)
viz_matches.annotate_matches(ibs, qres, aid, in_image=in_image, show_query=not show_query)
printDBG('[show_qres()] Plotting Chips %s:' % vh.get_aidstrs(aid_list))
if aid_list is None:
return
# Do lazy load before show
vh.get_chips(ibs, aid_list, **kwargs)
vh.get_kpts(ibs, aid_list, **kwargs)
for ox, aid in enumerate(aid_list):
plotx = ox + plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
oranks = np.where(ranked_aids == aid)[0]
if len(oranks) == 0:
orank = -1
continue
orank = oranks[0] + 1
_show_matches_fn(aid, orank, pnum)
shift_topN = nGtCells
if nGtSubplts == 1:
nGTCols = 1
if nRows == 0:
df2.imshow_null(fnum=fnum)
else:
fig = df2.figure(fnum=fnum, pnum=(nRows, nGTCols, 1), docla=True, doclf=True)
#df2.disconnect_callback(fig, 'button_press_event')
df2.plt.subplot(nRows, nGTCols, 1)
# Plot Query
if show_query:
_show_query_fn(0, (nRows, nGTCols))
# Plot Ground Truth
_plot_matches_aids(gt_aids, nQuerySubplts, (nRows, nGTCols))
_plot_matches_aids(top_aids, shift_topN, (nRows, nTopNCols))
#figtitle += ' q%s name=%s' % (ibsfuncs.aidstr(qres.qaid), ibs.aid2_name(qres.qaid))
figtitle += aug
df2.set_figtitle(figtitle, incanvas=not vh.NO_LBL_OVERRIDE)
# Result Interaction
df2.adjust_subplots_safe()
printDBG('[show_qres()] Finished')
return fig
def plot_scores(hs, qcx_list, fnum=1):
print('[dev] plot_scores(fnum=%r)' % fnum)
topN_gt = 1
topN_ranks = 3
qcx2_res = get_qcx2_res(hs, qcx_list)
data_scores = [] # matching scores
data_qpairs = [] # query info (qcx, cx)
data_gtranks = [] # query info (gtrank)
# Append all scores to a giant list
for res in qcx2_res.itervalues():
# Get gt scores first
qcx = res.qcx
top_cxs = np.array(res.topN_cxs(hs, N=topN_ranks, only_gt=False))
gt_cxs = np.array(res.topN_cxs(hs, N=topN_gt, only_gt=True))
top_scores = res.cx2_score[top_cxs]
np.intersect1d(top_cxs, gt_cxs)
# list of ranks if score is ground truth otherwise -1
isgt_ranks = [tx if cx in set(gt_cxs) else -1 for (tx, cx) in enumerate(top_cxs)]
qcx_pairs = [(hs.cx2_cid(qcx), hs.cx2_cid(cx)) for cx in top_cxs]
# Append all scores
data_scores.extend(top_scores.tolist())
data_qpairs.extend(qcx_pairs)
data_gtranks.extend(isgt_ranks)
data_scores = np.array(data_scores)
data_qpairs = np.array(data_qpairs)
data_gtranks = np.array(data_gtranks)
data_sortx = data_scores.argsort()
sorted_scores = data_scores[data_sortx]
# Draw and info
rank_colorbounds = [
((-1, 0), df2.GRAY),
((0, 1), df2.TRUE_GREEN),
((1, 5), df2.UNKNOWN_PURP),
((5, None), df2.FALSE_RED),
]
print('[dev] matching chipscore stats: ' + utool.stats_str(data_scores))
df2.figure(fnum=fnum, doclf=True, docla=True)
# Finds the knee
df2.plot(sorted_scores, color=df2.ORANGE, label='all scores')
# Plot results with ranks within (low, high) bounds
colorbounds_iter = reversed(list(enumerate(rank_colorbounds)))
for count, ((low, high), rankX_color) in colorbounds_iter:
datarank_flag = utool.inbounds(data_gtranks, low, high)
rankX_xs = np.where(datarank_flag[data_sortx])[0]
rankX_ys = sorted_scores[rankX_xs]
if high is None:
rankX_label = '%d <= gt rank' % low
else:
rankX_label = '%d <= gt rank < %d' % (low, high)
if len(rankX_ys) > 0:
df2.plot(rankX_xs, rankX_ys, 'o', color=rankX_color, label=rankX_label, alpha=.5)
rowid = qcx2_res.itervalues().next().rowid
df2.set_logyscale_from_data(data_scores)
df2.set_xlabel('chipmatch index')
df2.dark_background()
df2.set_figtitle('matching scores\n' + rowid)
df2.legend(loc='upper left')
df2.iup()
fnum += 1
score_table = np.vstack((data_scores, data_gtranks, data_qpairs.T)).T
score_table = score_table[data_sortx[::-1]]
column_labels = ['score', 'gtrank', 'qcid', 'cid']
header = 'score_table\nuid=%r' % rowid
column_type = [float, int, int, int]
csv_txt = utool.util_csv.numpy_to_csv(score_table, column_labels, header, column_type)
print(csv_txt)
#utool.embed()
return fnum
def show_descriptors_match_distances(orgres2_distance,
fnum=1,
db_name='',
**kwargs):
disttype_list = orgres2_distance.itervalues().next().keys()
orgtype_list = orgres2_distance.keys()
(nRow, nCol) = len(orgtype_list), len(disttype_list)
nColors = nRow * nCol
color_list = df2.distinct_colors(nColors)
df2.figure(fnum=fnum, docla=True, doclf=True)
pnum_ = lambda px: (nRow, nCol, px + 1)
plot_type = utool.get_arg('--plot-type', default='plot')
# Remember min and max val for each distance type (l1, emd...)
distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list}
distkey2_max = {distkey: 0 for distkey in disttype_list}
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label, yscale='linear')
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper left')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
px = 0
for orgkey in orgtype_list:
for distkey in disttype_list:
dists = orgres2_distance[orgkey][distkey]
if len(dists) == 0:
continue
min_ = dists.min()
max_ = dists.max()
distkey2_min[distkey] = min(distkey2_min[distkey], min_)
distkey2_max[distkey] = max(distkey2_max[distkey], max_)
for count, orgkey in enumerate(orgtype_list):
for distkey in disttype_list:
printDBG('[allres-viz] plotting: %r' % ((orgkey, distkey), ))
dists = orgres2_distance[orgkey][distkey]
df2.figure(fnum=fnum, pnum=pnum_(px))
color = color_list[px]
title = distkey + ' ' + orgkey
label = 'P(%s | %s)' % (distkey, orgkey)
_distplot(dists, color, label, distkey, **kwargs)
if count == 0:
ax = df2.gca()
ax.set_title(distkey)
px += 1
subtitle = 'the matching distances between sift descriptors'
title = '(sift) matching distances'
if db_name != '':
title = db_name + ' ' + title
df2.set_figtitle(title, subtitle)
df2.adjust_subplots_safe()
def _viz_keypoints(fnum, pnum=(1, 1, 1), **kwargs):
df2.figure(fnum=fnum, docla=True, doclf=True)
show_keypoints(chip, kpts, fnum=fnum, pnum=pnum, **kwargs)
if figtitle is not None:
df2.set_figtitle(figtitle)
