def plot_keypoint_scales(hs, fnum=1):
print('[dev] plot_keypoint_scales()')
cx2_kpts = hs.feats.cx2_kpts
if len(cx2_kpts) == 0:
hs.refresh_features()
cx2_kpts = hs.feats.cx2_kpts
cx2_nFeats = map(len, cx2_kpts)
kpts = np.vstack(cx2_kpts)
print('[dev] --- LaTeX --- ')
_printopts = np.get_printoptions()
np.set_printoptions(precision=3)
print(util_latex.latex_scalar(r'\# keypoints, ', len(kpts)))
print(util_latex.latex_mystats(r'\# keypoints per image', cx2_nFeats))
scales = ktool.get_scales(kpts)
scales = np.array(sorted(scales))
print(util_latex.latex_mystats(r'keypoint scale', scales))
np.set_printoptions(**_printopts)
print('[dev] ---/LaTeX --- ')
#
df2.figure(fnum=fnum, docla=True, title='sorted scales')
df2.plot(scales)
df2.adjust_subplots_safe()
#ax = df2.gca()
#ax.set_yscale('log')
#ax.set_xscale('log')
#
fnum += 1
df2.figure(fnum=fnum, docla=True, title='hist scales')
df2.show_histogram(scales, bins=20)
df2.adjust_subplots_safe()
#ax = df2.gca()
#ax.set_yscale('log')
#ax.set_xscale('log')
return fnum
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 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 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 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 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 show_nearest_descriptors(ibs, qaid, qfx, fnum=None, stride=5,
qreq_=None, **kwargs):
r"""
Args:
ibs (ibeis.IBEISController): image analysis api
qaid (int): query annotation id
qfx (int): query feature index
fnum (int): figure number
stride (int):
consecutive_distance_compare (bool):
CommandLine:
# Find a good match to inspect
python -m ibeis.viz.interact.interact_matches --test-testdata_match_interact --show --db PZ_MTEST --qaid 3
# Now inspect it
python -m ibeis.viz.viz_nearest_descriptors --test-show_nearest_descriptors --show --db PZ_MTEST --qaid 3 --qfx 879
python -m ibeis.viz.viz_nearest_descriptors --test-show_nearest_descriptors --show
python -m ibeis.viz.viz_nearest_descriptors --test-show_nearest_descriptors --db PZ_MTEST --qaid 3 --qfx 879 --diskshow --save foo.png --dpi=256
SeeAlso:
plottool.viz_featrow
~/code/plottool/plottool/viz_featrow.py
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_nearest_descriptors import * # NOQA
>>> import ibeis
>>> # build test data
>>> qreq_ = ibeis.testdata_qreq_()
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qaid = qreq_.qaids[0]
>>> qfx = ut.get_argval('--qfx', type_=int, default=879)
>>> fnum = None
>>> stride = 5
>>> # execute function
>>> skip = False
>>> result = show_nearest_descriptors(ibs, qaid, qfx, fnum, stride,
>>> draw_chip=True,
>>> draw_warped=True,
>>> draw_unwarped=False,
>>> draw_desc=False, qreq_=qreq_)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
consecutive_distance_compare = True
draw_chip = kwargs.get('draw_chip', False)
draw_desc = kwargs.get('draw_desc', True)
draw_warped = kwargs.get('draw_warped', True)
draw_unwarped = kwargs.get('draw_unwarped', True)
#skip = kwargs.get('skip', True)
# Plots the nearest neighbors of a given feature (qaid, qfx)
if fnum is None:
fnum = df2.next_fnum()
try:
# Flann NN query
(qfx2_daid, qfx2_dfx, qfx2_dist, K, Knorm) = get_annotfeat_nn_index(ibs, qaid, qfx, qreq_=qreq_)
# Adds metadata to a feature match
def get_extract_tuple(aid, fx, k=-1):
rchip = ibs.get_annot_chips(aid)
kp = ibs.get_annot_kpts(aid)[fx]
sift = ibs.get_annot_vecs(aid)[fx]
if not ut.get_argflag('--texknormplot'):
aidstr = vh.get_aidstrs(aid)
nidstr = vh.get_nidstrs(ibs.get_annot_nids(aid))
id_str = ' ' + aidstr + ' ' + nidstr + ' fx=%r' % (fx,)
else:
id_str = nidstr = aidstr = ''
info = ''
if k == -1:
if pt.is_texmode():
info = '\\vspace{1cm}'
info += 'Query $\\mathbf{d}_i$'
info += '\n\\_'
info += '\n\\_'
else:
if len(id_str) > '':
info = 'Query: %s' % (id_str,)
else:
info = 'Query'
type_ = 'Query'
elif k < K:
type_ = 'Match'
if ut.get_argflag('--texknormplot') and pt.is_texmode():
#info = 'Match:\n$k=%r$, $\\frac{||\\mathbf{d}_i - \\mathbf{d}_j||}{Z}=%.3f$' % (k, qfx2_dist[0, k])
info = '\\vspace{1cm}'
info += 'Match: $\\mathbf{d}_{j_%r}$\n$\\textrm{dist}=%.3f$' % (k, qfx2_dist[0, k])
info += '\n$s_{\\tt{LNBNN}}=%.3f$' % (qfx2_dist[0, K + Knorm - 1] - qfx2_dist[0, k])
else:
info = 'Match:%s\nk=%r, dist=%.3f' % (id_str, k, qfx2_dist[0, k])
info += '\nLNBNN=%.3f' % (qfx2_dist[0, K + Knorm - 1] - qfx2_dist[0, k])
elif k < Knorm + K:
type_ = 'Norm'
if ut.get_argflag('--texknormplot') and pt.is_texmode():
#info = 'Norm: $j_%r$\ndist=%.3f' % (id_str, k, qfx2_dist[0, k])
info = '\\vspace{1cm}'
info += 'Norm: $j_%r$\n$\\textrm{dist}=%.3f$' % (k, qfx2_dist[0, k])
info += '\n\\_'
else:
info = 'Norm: %s\n$k=%r$, dist=$%.3f$' % (id_str, k, qfx2_dist[0, k])
else:
raise Exception('[viz] problem k=%r')
return (rchip, kp, sift, fx, aid, info, type_)
extracted_list = []
# Remember the query sift feature
extracted_list.append(get_extract_tuple(qaid, qfx, -1))
origsift = extracted_list[0][2]
skipped = 0
for k in range(K + Knorm):
#if qfx2_daid[0, k] == qaid and qfx2_dfx[0, k] == qfx:
if qfx2_daid[0, k] == qaid:
skipped += 1
continue
tup = get_extract_tuple(qfx2_daid[0, k], qfx2_dfx[0, k], k)
extracted_list.append(tup)
# Draw the _select_ith_match plot
nRows = len(extracted_list)
if stride is None:
stride = nRows
# Draw selected feature matches
prevsift = None
px = 0 # plot offset
px_shift = 0 # plot stride shift
nExtracted = len(extracted_list)
featrow_kw = dict(
draw_chip=draw_chip, draw_desc=draw_desc, draw_warped=draw_warped,
draw_unwarped=draw_unwarped,
)
if ut.get_argflag('--texknormplot'):
featrow_kw['ell_color'] = pt.ORANGE
pass
for listx, tup in enumerate(extracted_list):
(rchip, kp, sift, fx, aid, info, type_) = tup
if listx % stride == 0:
# Create a temporary nRows and fnum in case we are splitting
# up nearest neighbors into separate figures with stride
_fnum = fnum + listx
_nRows = min(nExtracted - listx, stride)
px_shift = px
df2.figure(fnum=_fnum, docla=True, doclf=True)
px_ = px - px_shift
px = draw_feat_row(rchip, fx, kp, sift, _fnum, _nRows, px=px_,
prevsift=prevsift, origsift=origsift, aid=aid,
info=info, type_=type_, **featrow_kw)
px += px_shift
if prevsift is None or consecutive_distance_compare:
prevsift = sift
df2.adjust_subplots_safe(hspace=.85, wspace=0, top=.95, bottom=.087, left=.05, right=.95)
except Exception as ex:
print('[viz] Error in show nearest descriptors')
print(ex)
raise
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 draw():
df2.adjust_subplots_safe()
fig_presenter.draw()
def show_nearest_descriptors(ibs, qaid, qfx, fnum=None, stride=5,
consecutive_distance_compare=False):
# Plots the nearest neighbors of a given feature (qaid, qfx)
if fnum is None:
fnum = df2.next_fnum()
try:
# Flann NN query
(qfx2_aid, qfx2_fx, qfx2_dist, K, Knorm) = query_helpers.get_annotfeat_nn_index(ibs, qaid, qfx)
# Adds metadata to a feature match
def get_extract_tuple(aid, fx, k=-1):
rchip = ibs.get_annot_chips(aid)
kp = ibs.get_annot_kpts(aid)[fx]
sift = ibs.get_annot_desc(aid)[fx]
if k == -1:
info = '\nquery %s, fx=%r' % (vh.get_aidstrs(aid), fx)
type_ = 'query'
elif k < K:
type_ = 'match'
info = '\nmatch %s, fx=%r k=%r, dist=%r' % (vh.get_aidstrs(aid), fx, k, qfx2_dist[0, k])
elif k < Knorm + K:
type_ = 'norm'
info = '\nnorm %s, fx=%r k=%r, dist=%r' % (vh.get_aidstrs(aid), fx, k, qfx2_dist[0, k])
else:
raise Exception('[viz] problem k=%r')
return (rchip, kp, sift, fx, aid, info, type_)
extracted_list = []
extracted_list.append(get_extract_tuple(qaid, qfx, -1))
skipped = 0
for k in xrange(K + Knorm):
if qfx2_aid[0, k] == qaid and qfx2_fx[0, k] == qfx:
skipped += 1
continue
tup = get_extract_tuple(qfx2_aid[0, k], qfx2_fx[0, k], k)
extracted_list.append(tup)
# Draw the _select_ith_match plot
nRows, nCols = len(extracted_list), 3
if stride is None:
stride = nRows
# Draw selected feature matches
prevsift = None
px = 0 # plot offset
px_shift = 0 # plot stride shift
nExtracted = len(extracted_list)
for listx, tup in enumerate(extracted_list):
(rchip, kp, sift, fx, aid, info, type_) = tup
if listx % stride == 0:
# Create a temporary nRows and fnum in case we are splitting
# up nearest neighbors into separate figures with stride
_fnum = fnum + listx
_nRows = min(nExtracted - listx, stride)
px_shift = px
df2.figure(fnum=_fnum, docla=True, doclf=True)
printDBG('[viz] ' + info.replace('\n', ''))
px_ = px - px_shift
px = draw_feat_row(rchip, fx, kp, sift, _fnum, _nRows, nCols, px_,
prevsift=prevsift, aid=aid, info=info, type_=type_) + px_shift
if prevsift is None or consecutive_distance_compare:
prevsift = sift
df2.adjust_subplots_safe(hspace=1)
except Exception as ex:
print('[viz] Error in show nearest descriptors')
print(ex)
raise
def show_nearest_descriptors(ibs,
qaid,
qfx,
fnum=None,
stride=5,
qreq_=None,
**kwargs):
r"""
Args:
ibs (ibeis.IBEISController): image analysis api
qaid (int): query annotation id
qfx (int): query feature index
fnum (int): figure number
stride (int):
consecutive_distance_compare (bool):
CommandLine:
# Find a good match to inspect
python -m ibeis.viz.interact.interact_matches --test-testdata_match_interact --show --db PZ_MTEST --qaid 3
# Now inspect it
python -m ibeis.viz.viz_nearest_descriptors --test-show_nearest_descriptors --show --db PZ_MTEST --qaid 3 --qfx 879
python -m ibeis.viz.viz_nearest_descriptors --test-show_nearest_descriptors --show
python -m ibeis.viz.viz_nearest_descriptors --test-show_nearest_descriptors --db PZ_MTEST --qaid 3 --qfx 879 --diskshow --save foo.png --dpi=256
SeeAlso:
plottool.viz_featrow
~/code/plottool/plottool/viz_featrow.py
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_nearest_descriptors import * # NOQA
>>> import ibeis
>>> # build test data
>>> qreq_ = ibeis.testdata_qreq_()
>>> ibs = ibeis.opendb('PZ_MTEST')
>>> qaid = qreq_.qaids[0]
>>> qfx = ut.get_argval('--qfx', type_=None, default=879)
>>> fnum = None
>>> stride = 5
>>> # execute function
>>> skip = False
>>> result = show_nearest_descriptors(ibs, qaid, qfx, fnum, stride,
>>> draw_chip=True,
>>> draw_warped=True,
>>> draw_unwarped=False,
>>> draw_desc=False, qreq_=qreq_)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import plottool as pt # NOQA
consecutive_distance_compare = True
draw_chip = kwargs.get('draw_chip', False)
draw_desc = kwargs.get('draw_desc', True)
draw_warped = kwargs.get('draw_warped', True)
draw_unwarped = kwargs.get('draw_unwarped', True)
#skip = kwargs.get('skip', True)
# Plots the nearest neighbors of a given feature (qaid, qfx)
if fnum is None:
fnum = df2.next_fnum()
try:
# Flann NN query
(qfx, qfx2_daid, qfx2_dfx, qfx2_dist, K,
Knorm) = get_annotfeat_nn_index(ibs, qaid, qfx, qreq_=qreq_)
# Adds metadata to a feature match
def get_extract_tuple(aid, fx, k=-1):
rchip = ibs.get_annot_chips(aid)
kp = ibs.get_annot_kpts(aid)[fx]
sift = ibs.get_annot_vecs(aid)[fx]
if not ut.get_argflag('--texknormplot'):
aidstr = vh.get_aidstrs(aid)
nidstr = vh.get_nidstrs(ibs.get_annot_nids(aid))
id_str = ' ' + aidstr + ' ' + nidstr + ' fx=%r' % (fx, )
else:
id_str = nidstr = aidstr = ''
info = ''
if k == -1:
if pt.is_texmode():
info = '\\vspace{1cm}'
info += 'Query $\\mathbf{d}_i$'
info += '\n\\_'
info += '\n\\_'
else:
if len(id_str) > '':
info = 'Query: %s' % (id_str, )
else:
info = 'Query'
type_ = 'Query'
elif k < K:
type_ = 'Match'
if ut.get_argflag('--texknormplot') and pt.is_texmode():
#info = 'Match:\n$k=%r$, $\\frac{||\\mathbf{d}_i - \\mathbf{d}_j||}{Z}=%.3f$' % (k, qfx2_dist[0, k])
info = '\\vspace{1cm}'
info += 'Match: $\\mathbf{d}_{j_%r}$\n$\\textrm{dist}=%.3f$' % (
k, qfx2_dist[0, k])
info += '\n$s_{\\tt{LNBNN}}=%.3f$' % (
qfx2_dist[0, K + Knorm - 1] - qfx2_dist[0, k])
else:
info = 'Match:%s\nk=%r, dist=%.3f' % (id_str, k,
qfx2_dist[0, k])
info += '\nLNBNN=%.3f' % (qfx2_dist[0, K + Knorm - 1] -
qfx2_dist[0, k])
elif k < Knorm + K:
type_ = 'Norm'
if ut.get_argflag('--texknormplot') and pt.is_texmode():
#info = 'Norm: $j_%r$\ndist=%.3f' % (id_str, k, qfx2_dist[0, k])
info = '\\vspace{1cm}'
info += 'Norm: $j_%r$\n$\\textrm{dist}=%.3f$' % (
k, qfx2_dist[0, k])
info += '\n\\_'
else:
info = 'Norm: %s\n$k=%r$, dist=$%.3f$' % (id_str, k,
qfx2_dist[0, k])
else:
raise Exception('[viz] problem k=%r')
return (rchip, kp, sift, fx, aid, info, type_)
extracted_list = []
# Remember the query sift feature
extracted_list.append(get_extract_tuple(qaid, qfx, -1))
origsift = extracted_list[0][2]
skipped = 0
for k in range(K + Knorm):
#if qfx2_daid[0, k] == qaid and qfx2_dfx[0, k] == qfx:
if qfx2_daid[0, k] == qaid:
skipped += 1
continue
tup = get_extract_tuple(qfx2_daid[0, k], qfx2_dfx[0, k], k)
extracted_list.append(tup)
# Draw the _select_ith_match plot
nRows = len(extracted_list)
if stride is None:
stride = nRows
# Draw selected feature matches
prevsift = None
px = 0 # plot offset
px_shift = 0 # plot stride shift
nExtracted = len(extracted_list)
featrow_kw = dict(
draw_chip=draw_chip,
draw_desc=draw_desc,
draw_warped=draw_warped,
draw_unwarped=draw_unwarped,
)
if ut.get_argflag('--texknormplot'):
featrow_kw['ell_color'] = pt.ORANGE
pass
for listx, tup in enumerate(extracted_list):
(rchip, kp, sift, fx, aid, info, type_) = tup
if listx % stride == 0:
# Create a temporary nRows and fnum in case we are splitting
# up nearest neighbors into separate figures with stride
_fnum = fnum + listx
_nRows = min(nExtracted - listx, stride)
px_shift = px
df2.figure(fnum=_fnum, docla=True, doclf=True)
px_ = px - px_shift
px = draw_feat_row(rchip,
fx,
kp,
sift,
_fnum,
_nRows,
px=px_,
prevsift=prevsift,
origsift=origsift,
aid=aid,
info=info,
type_=type_,
**featrow_kw)
px += px_shift
if prevsift is None or consecutive_distance_compare:
prevsift = sift
df2.adjust_subplots_safe(hspace=.85,
wspace=0,
top=.95,
bottom=.087,
left=.05,
right=.95)
except Exception as ex:
print('[viz] Error in show nearest descriptors')
print(ex)
raise
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 draw():
df2.adjust_subplots_safe()
fig_presenter.draw()