def query_aids(ibs, qaid_list, daid_list=None):
"""
CommandLine:
python dev.py -w --show -t query --db PZ_MTEST --qaid 72
"""
import wbia
if daid_list is None:
daid_list = ibs.get_valid_aids()
cm_list = ibs.query_chips(qaid_list, daid_list)
for cm in cm_list:
assert isinstance(cm, wbia.algo.hots.hots_query_result.QueryResult)
cm.ishow_top(ibs,
fnum=df2.next_fnum(),
annot_mode=1,
make_figtitle=True)
def sver_aids(ibs, qaid_list, daid_list=None):
"""
CommandLine:
python dev.py -w --show -t sver --db PZ_MTEST --qaid 72
python dev.py -w --show -t sver --db PZ_MTEST --qaid 1
"""
from wbia.viz import interact
if daid_list is None:
daid_list = ibs.get_valid_aids()
cm_list = ibs.query_chips(qaid_list, daid_list)
for cm in cm_list:
aid2 = cm.get_top_aids()[0]
interact.ishow_sver(ibs,
cm.qaid,
aid2,
fnum=df2.next_fnum(),
annot_mode=1)
def show_keypoint_gradient_orientations(ibs,
aid,
fx,
fnum=None,
pnum=None,
config2_=None):
# Draw the gradient vectors of a patch overlaying the keypoint
if fnum is None:
fnum = df2.next_fnum()
rchip = ibs.get_annot_chips(aid, config2_=config2_)
kp = ibs.get_annot_kpts(aid, config2_=config2_)[fx]
sift = ibs.get_annot_vecs(aid, config2_=config2_)[fx]
fig = df2.draw_keypoint_gradient_orientations(rchip,
kp,
sift=sift,
mode='vec',
fnum=fnum,
pnum=pnum)
fig.canvas.draw()
fig.show()
df2.set_title('Gradient orientation\n %s, fx=%d' % (get_aidstrs(aid), fx))
def ishow_image(ibs,
gid,
sel_aids=[],
fnum=None,
select_callback=None,
**kwargs):
if ut.VERBOSE:
print(ut.get_caller_name(range(9)))
print('[interact_image] gid=%r fnum=%r' % (gid, fnum))
if fnum is None:
fnum = df2.next_fnum()
# TODO: change to class based structure
self = ut.DynStruct()
self.fnum = fnum
fig = ih.begin_interaction('image', fnum)
# printDBG(utool.func_str(interact_image, [], locals()))
kwargs['draw_lbls'] = kwargs.get('draw_lbls', True)
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.repr2(_kwargs))
raise
# Create callback wrapper
def _on_image_click(event):
print('[inter] clicked image')
if ih.clicked_outside_axis(event):
# Toggle draw lbls
kwargs['draw_lbls'] = not kwargs.get('draw_lbls', True)
_image_view(**kwargs)
else:
ax = event.inaxes
viztype = vh.get_ibsdat(ax, 'viztype')
annotation_centers = vh.get_ibsdat(ax,
'annotation_centers',
default=[])
print(' annotation_centers=%r' % annotation_centers)
print(' viztype=%r' % viztype)
if len(annotation_centers) == 0:
print(' ...no chips exist to click')
return
x, y = event.xdata, event.ydata
# Find ANNOTATION center nearest to the clicked point
aid_list = vh.get_ibsdat(ax, 'aid_list', default=[])
import vtool as vt
centx, _dist = vt.nearest_point(x, y, annotation_centers)
aid = aid_list[centx]
print(' ...clicked aid=%r' % aid)
if select_callback is not None:
# HACK, should just implement this correctly here
select_callback(gid, sel_aids=[aid], fnum=self.fnum)
else:
_image_view(sel_aids=[aid])
viz.draw()
_image_view(**kwargs)
viz.draw()
ih.connect_callback(fig, 'button_press_event', _on_image_click)
def show_nearest_descriptors(ibs,
qaid,
qfx,
fnum=None,
stride=5,
qreq_=None,
**kwargs):
r"""
Args:
ibs (wbia.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 wbia.viz.interact.interact_matches --test-testdata_match_interact --show --db PZ_MTEST --qaid 3
# Now inspect it
python -m wbia.viz.viz_nearest_descriptors --test-show_nearest_descriptors --show --db PZ_MTEST --qaid 3 --qfx 879
python -m wbia.viz.viz_nearest_descriptors --test-show_nearest_descriptors --show
python -m wbia.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 wbia.viz.viz_nearest_descriptors import * # NOQA
>>> import wbia
>>> # build test data
>>> if True:
>>> import matplotlib as mpl
>>> from wbia.scripts.thesis import TMP_RC
>>> mpl.rcParams.update(TMP_RC)
>>> qreq_ = wbia.testdata_qreq_()
>>> ibs = wbia.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 wbia.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])
info += '\n$s=%.3f$' % (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
featrow_kw['ell_linewidth'] = 1
featrow_kw['arm1_lw'] = 0.5
featrow_kw['stroke'] = 0
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(hspace=.85, wspace=0, top=.95, bottom=.087, left=.05, right=.95)
except Exception as ex:
logger.info('[viz] Error in show nearest descriptors')
logger.info(ex)
raise
def show_aids(ibs, qaid_list):
from wbia.viz import interact
for aid in qaid_list:
interact.ishow_chip(ibs, aid, fnum=df2.next_fnum())
