def draw_feat_row(chip,
fx,
kp,
sift,
fnum,
nRows,
nCols=None,
px=None,
prevsift=None,
origsift=None,
aid=None,
info='',
type_=None,
shape_labels=False,
vecfield=False,
multicolored_arms=False,
draw_chip=False,
draw_warped=True,
draw_unwarped=True,
draw_desc=True,
rect=True,
ori=True,
pts=False,
**kwargs):
"""
draw_feat_row
SeeAlso:
wbia.viz.viz_nearest_descriptors
~/code/wbia/wbia/viz/viz_nearest_descriptors.py
CommandLine:
# Use this to find the fx you want to visualize
python -m wbia.plottool.interact_keypoints --test-ishow_keypoints --show --fname zebra.png
# Use this to visualize the featrow
python -m wbia.plottool.viz_featrow --test-draw_feat_row --show
python -m wbia.plottool.viz_featrow --test-draw_feat_row --show --fname zebra.png --fx=121 --feat-all --no-sift
python -m wbia.plottool.viz_featrow --test-draw_feat_row --dpath figures --save ~/latex/crall-candidacy-2015/figures/viz_featrow.jpg
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.plottool.viz_featrow import * # NOQA
>>> import wbia.plottool as pt
>>> # build test data
>>> kpts, vecs, imgBGR = pt.viz_keypoints.testdata_kpts()
>>> chip = imgBGR
>>> print('There are %d features' % (len(vecs)))
>>> fx = ut.get_argval('--fx', type_=int, default=0)
>>> kp = kpts[fx]
>>> sift = vecs[fx]
>>> fnum = 1
>>> nRows = 1
>>> nCols = 2
>>> px = 0
>>> if True:
>>> from wbia.scripts.thesis import TMP_RC
>>> import matplotlib as mpl
>>> mpl.rcParams.update(TMP_RC)
>>> hack = ut.get_argflag('--feat-all')
>>> sift = sift if not ut.get_argflag('--no-sift') else None
>>> draw_desc = sift is not None
>>> kw = dict(
>>> prevsift=None, origsift=None, aid=None, info='', type_=None,
>>> shape_labels=False, vecfield=False, multicolored_arms=True,
>>> draw_chip=hack, draw_unwarped=hack, draw_warped=True, draw_desc=draw_desc
>>> )
>>> # execute function
>>> result = draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px,
>>> rect=False, ori=False, pts=False, **kw)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import numpy as np
import vtool as vt
# should not need ncols here
if nCols is not None:
if ut.VERBOSE:
print('Warning nCols is no longer needed')
# assert nCols_ == nCols
nCols = draw_chip + draw_unwarped + draw_warped + draw_desc
pnum_ = df2.make_pnum_nextgen(nRows, nCols, start=px)
# pnum_ = df2.get_pnum_func(nRows, nCols, base=1)
# countgen = itertools.count(1)
# pnumgen_ = df2.make_pnum_nextgen(nRows, nCols, base=1)
def _draw_patch(**kwargs):
return df2.draw_keypoint_patch(chip,
kp,
sift,
rect=rect,
ori=ori,
pts=pts,
ori_color=custom_constants.DEEP_PINK,
multicolored_arms=multicolored_arms,
**kwargs)
# Feature strings
xy_str, shape_str, scale, ori_str = ph.kp_info(kp)
if draw_chip:
pnum = pnum_()
df2.imshow(chip, fnum=fnum, pnum=pnum)
kpts_kw = dict(ell_linewidth=1, ell_alpha=1.0)
kpts_kw.update(kwargs)
df2.draw_kpts2([kp], **kpts_kw)
if draw_unwarped:
# Draw the unwarped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)))
# pnum = pnum_(px + six.next(countgen)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum)
ph.set_plotdat(ax, 'viztype', 'unwarped')
ph.set_plotdat(ax, 'aid', aid)
ph.set_plotdat(ax, 'fx', fx)
if shape_labels:
unwarped_lbl = 'affine feature invV =\n' + shape_str + '\n' + ori_str
custom_figure.set_xlabel(unwarped_lbl, ax)
if draw_warped:
# Draw the warped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)), warped=True)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum, warped=True, **kwargs)
ph.set_plotdat(ax, 'viztype', 'warped')
ph.set_plotdat(ax, 'aid', aid)
ph.set_plotdat(ax, 'fx', fx)
if shape_labels:
warped_lbl = ('warped feature\n' + 'fx=%r scale=%.1f\n' + '%s') % (
fx,
scale,
xy_str,
)
else:
warped_lbl = ''
warped_lbl += info
custom_figure.set_xlabel(warped_lbl, ax)
if draw_desc:
border_color = {
'None': None,
'query': None,
'match': custom_constants.BLUE,
'norm': custom_constants.ORANGE,
}.get(str(type_).lower(), None)
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
# pnum = pnum_(px + six.next(countgen))
pnum = pnum_()
sift_as_vecfield = ph.SIFT_OR_VECFIELD or vecfield
if sift_as_vecfield:
custom_figure.figure(fnum=fnum, pnum=pnum)
df2.draw_keypoint_gradient_orientations(chip, kp, sift=sift)
else:
if sift.dtype.type == np.uint8:
# sigtitle = 'sift histogram' if (px % 3) == 0 else ''
# ax = df2.plot_sift_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
ax = df2.plot_sift_signature(sift, fnum=fnum, pnum=pnum)
else:
# sigtitle = 'descriptor vector' if (px % 3) == 0 else ''
ax = df2.plot_descriptor_signature(sift, fnum=fnum, pnum=pnum)
ax._hs_viztype = 'histogram'
# dist_list = ['L1', 'L2', 'hist_isect', 'emd']
# dist_list = ['L2', 'hist_isect']
# dist_list = ['L2']
# dist_list = ['bar_L2_sift', 'cos_sift']
# dist_list = ['L2_sift', 'bar_cos_sift']
dist_list = ['L2_sift']
dist_str_list = []
if origsift is not None:
distmap_orig = vt.compute_distances(sift, origsift, dist_list)
dist_str_list.append('query_dist: ' + ', '.join([
'(%s, %s)' % (key, formatdist(val))
for key, val in six.iteritems(distmap_orig)
]))
if prevsift is not None:
distmap_prev = vt.compute_distances(sift, prevsift, dist_list)
dist_str_list.append('prev_dist: ' + ', '.join([
'(%s, %s)' % (key, formatdist(val))
for key, val in six.iteritems(distmap_prev)
]))
dist_str = '\n'.join(dist_str_list)
custom_figure.set_xlabel(dist_str)
return px + nCols
def get_training_desc_dist(cm, qreq_, fsv_col_lbls=[], namemode=True,
top_percent=None, data_annots=None,
query_annots=None, num=None):
r"""
computes custom distances on prematched descriptors
SeeAlso:
python -m ibeis --tf learn_featscore_normalizer --show --disttype=ratio
python -m ibeis --tf learn_featscore_normalizer --show --disttype=normdist -a timectrl -t default:K=1 --db PZ_Master1 --save pzmaster_normdist.png
python -m ibeis --tf learn_featscore_normalizer --show --disttype=normdist -a timectrl -t default:K=1 --db PZ_MTEST --save pzmtest_normdist.png
python -m ibeis --tf learn_featscore_normalizer --show --disttype=normdist -a timectrl -t default:K=1 --db GZ_ALL
python -m ibeis --tf learn_featscore_normalizer --show --disttype=L2_sift -a timectrl -t default:K=1 --db PZ_MTEST
python -m ibeis --tf learn_featscore_normalizer --show --disttype=L2_sift -a timectrl -t default:K=1 --db PZ_Master1
python -m ibeis --tf compare_featscores --show --disttype=L2_sift,normdist -a timectrl -t default:K=1 --db GZ_ALL
CommandLine:
python -m ibeis.algo.hots.scorenorm --exec-get_training_desc_dist
python -m ibeis.algo.hots.scorenorm --exec-get_training_desc_dist:1
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.scorenorm import * # NOQA
>>> import ibeis
>>> cm, qreq_ = ibeis.testdata_cm(defaultdb='PZ_MTEST')
>>> fsv_col_lbls = ['ratio', 'lnbnn', 'L2_sift']
>>> namemode = False
>>> (tp_fsv, tn_fsv) = get_training_desc_dist(cm, qreq_, fsv_col_lbls,
>>> namemode=namemode)
>>> result = ut.repr2((tp_fsv.T, tn_fsv.T), nl=1)
>>> print(result)
Example1:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.scorenorm import * # NOQA
>>> import ibeis
>>> cm, qreq_ = ibeis.testdata_cm(defaultdb='PZ_MTEST')
>>> fsv_col_lbls = cm.fsv_col_lbls
>>> num = None
>>> namemode = False
>>> top_percent = None
>>> data_annots = None
>>> (tp_fsv1, tn_fsv1) = get_training_fsv(cm, namemode=namemode,
>>> top_percent=top_percent)
>>> (tp_fsv, tn_fsv) = get_training_desc_dist(cm, qreq_, fsv_col_lbls,
>>> namemode=namemode,
>>> top_percent=top_percent)
>>> vt.asserteq(tp_fsv1, tp_fsv)
>>> vt.asserteq(tn_fsv1, tn_fsv)
"""
if namemode:
tp_idxs, tn_idxs = get_topname_training_idxs(cm, num=num)
else:
tp_idxs, tn_idxs = get_topannot_training_idxs(cm, num=num)
if top_percent is not None:
cm_orig = cm
cm_orig.assert_self(qreq_, verbose=False)
# Keep only the top scoring half of the feature matches
tophalf_indicies = [
ut.take_percentile(fs.argsort()[::-1], top_percent)
for fs in cm.get_fsv_prod_list()
]
cm = cm_orig.take_feature_matches(tophalf_indicies, keepscores=True)
assert np.all(cm_orig.daid_list.take(tp_idxs) == cm.daid_list.take(tp_idxs))
assert np.all(cm_orig.daid_list.take(tn_idxs) == cm.daid_list.take(tn_idxs))
cm.assert_self(qreq_, verbose=False)
ibs = qreq_.ibs
query_config2_ = qreq_.extern_query_config2
data_config2_ = qreq_.extern_data_config2
special_xs, dist_xs = vt.index_partition(fsv_col_lbls, ['fg', 'ratio', 'lnbnn', 'normdist'])
dist_lbls = ut.take(fsv_col_lbls, dist_xs)
special_lbls = ut.take(fsv_col_lbls, special_xs)
qaid = cm.qaid
# cm.assert_self(qreq_=qreq_)
fsv_list = []
for idxs in [tp_idxs, tn_idxs]:
daid_list = cm.daid_list.take(idxs)
# Matching indices in query / databas images
qfxs_list = ut.take(cm.qfxs_list, idxs)
dfxs_list = ut.take(cm.dfxs_list, idxs)
need_norm = len(ut.setintersect_ordered(['ratio', 'lnbnn', 'normdist'], special_lbls)) > 0
#need_norm |= 'parzen' in special_lbls
#need_norm |= 'norm_parzen' in special_lbls
need_dists = len(dist_xs) > 0
if need_dists or need_norm:
qaid_list = [qaid] * len(qfxs_list)
qvecs_flat_m = np.vstack(ibs.get_annot_vecs_subset(qaid_list, qfxs_list, config2_=query_config2_))
dvecs_flat_m = np.vstack(ibs.get_annot_vecs_subset(daid_list, dfxs_list, config2_=data_config2_))
if need_norm:
assert any(x is not None for x in cm.filtnorm_aids), 'no normalizer known'
naids_list = ut.take(cm.naids_list, idxs)
nfxs_list = ut.take(cm.nfxs_list, idxs)
nvecs_flat = ibs.lookup_annot_vecs_subset(naids_list, nfxs_list, config2_=data_config2_,
annots=data_annots)
#import utool
#with utool.embed_on_exception_context:
#nvecs_flat_m = np.vstack(ut.compress(nvecs_flat, nvecs_flat))
_nvecs_flat_m = ut.compress(nvecs_flat, nvecs_flat)
nvecs_flat_m = vt.safe_vstack(_nvecs_flat_m, qvecs_flat_m.shape, qvecs_flat_m.dtype)
vdist = vt.L2_sift(qvecs_flat_m, dvecs_flat_m)
ndist = vt.L2_sift(qvecs_flat_m, nvecs_flat_m)
#assert np.all(vdist <= ndist)
#import utool
#utool.embed()
#vdist = vt.L2_sift_sqrd(qvecs_flat_m, dvecs_flat_m)
#ndist = vt.L2_sift_sqrd(qvecs_flat_m, nvecs_flat_m)
#vdist = vt.L2_root_sift(qvecs_flat_m, dvecs_flat_m)
#ndist = vt.L2_root_sift(qvecs_flat_m, nvecs_flat_m)
#x = cm.fsv_list[0][0:5].T[0]
#y = (ndist - vdist)[0:5]
if len(special_xs) > 0:
special_dist_list = []
# assert special_lbls[0] == 'fg'
if 'fg' in special_lbls:
# hack for fgweights (could get them directly from fsv)
qfgweights_flat_m = np.hstack(ibs.get_annot_fgweights_subset([qaid] * len(qfxs_list), qfxs_list, config2_=query_config2_))
dfgweights_flat_m = np.hstack(ibs.get_annot_fgweights_subset(daid_list, dfxs_list, config2_=data_config2_))
fgweights = np.sqrt(qfgweights_flat_m * dfgweights_flat_m)
special_dist_list.append(fgweights)
if 'ratio' in special_lbls:
# Integrating ratio test
ratio_dist = (vdist / ndist)
special_dist_list.append(ratio_dist)
if 'lnbnn' in special_lbls:
lnbnn_dist = ndist - vdist
special_dist_list.append(lnbnn_dist)
#if 'parzen' in special_lbls:
# parzen = vt.gauss_parzen_est(vdist, sigma=.38)
# special_dist_list.append(parzen)
#if 'norm_parzen' in special_lbls:
# parzen = vt.gauss_parzen_est(ndist, sigma=.38)
# special_dist_list.append(parzen)
if 'normdist' in special_lbls:
special_dist_list.append(ndist)
special_dists = np.vstack(special_dist_list).T
else:
special_dists = np.empty((0, 0))
if len(dist_xs) > 0:
# Get descriptors
# Compute descriptor distnaces
_dists = vt.compute_distances(qvecs_flat_m, dvecs_flat_m, dist_lbls)
dists = np.vstack(_dists.values()).T
else:
dists = np.empty((0, 0))
fsv = vt.rebuild_partition(special_dists.T, dists.T,
special_xs, dist_xs)
fsv = np.array(fsv).T
fsv_list.append(fsv)
tp_fsv, tn_fsv = fsv_list
return tp_fsv, tn_fsv
def draw_feat_row(
chip,
fx,
kp,
sift,
fnum,
nRows,
nCols=None,
px=None,
prevsift=None,
origsift=None,
aid=None,
info="",
type_=None,
shape_labels=False,
vecfield=False,
multicolored_arms=False,
draw_chip=False,
draw_warped=True,
draw_unwarped=True,
draw_desc=True,
rect=True,
ori=True,
pts=False,
**kwargs
):
"""
draw_feat_row
SeeAlso:
ibeis.viz.viz_nearest_descriptors
~/code/ibeis/ibeis/viz/viz_nearest_descriptors.py
CommandLine:
# Use this to find the fx you want to visualize
python -m plottool.interact_keypoints --test-ishow_keypoints --show --fname zebra.png
# Use this to visualize the featrow
python -m plottool.viz_featrow --test-draw_feat_row --show
python -m plottool.viz_featrow --test-draw_feat_row --show --fname zebra.png --fx=121 --feat-all --no-sift
python -m plottool.viz_featrow --test-draw_feat_row --dpath figures --save ~/latex/crall-candidacy-2015/figures/viz_featrow.jpg
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.viz_featrow import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> kpts, vecs, imgBGR = pt.viz_keypoints.testdata_kpts()
>>> chip = imgBGR
>>> print('There are %d features' % (len(vecs)))
>>> fx = ut.get_argval('--fx', type_=int, default=0)
>>> kp = kpts[fx]
>>> sift = vecs[fx]
>>> fnum = 1
>>> nRows = 1
>>> nCols = 2
>>> px = 0
>>> hack = ut.get_argflag('--feat-all')
>>> sift = sift if not ut.get_argflag('--no-sift') else None
>>> draw_desc = sift is not None
>>> kw = dict(
>>> prevsift=None, origsift=None, aid=None, info='', type_=None,
>>> shape_labels=False, vecfield=False, multicolored_arms=True,
>>> draw_chip=hack, draw_unwarped=hack, draw_warped=True, draw_desc=draw_desc
>>> )
>>> # execute function
>>> result = draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px,
>>> rect=False, ori=False, pts=False, **kw)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import numpy as np
import vtool as vt
# should not need ncols here
if nCols is not None:
if ut.VERBOSE:
print("Warning nCols is no longer needed")
# assert nCols_ == nCols
nCols = draw_chip + draw_unwarped + draw_warped + draw_desc
pnum_ = df2.make_pnum_nextgen(nRows, nCols, start=px)
# pnum_ = df2.get_pnum_func(nRows, nCols, base=1)
# countgen = itertools.count(1)
# pnumgen_ = df2.make_pnum_nextgen(nRows, nCols, base=1)
def _draw_patch(**kwargs):
return df2.draw_keypoint_patch(
chip,
kp,
sift,
rect=rect,
ori=ori,
pts=pts,
ori_color=custom_constants.DEEP_PINK,
multicolored_arms=multicolored_arms,
**kwargs
)
# Feature strings
xy_str, shape_str, scale, ori_str = ph.kp_info(kp)
if draw_chip:
pnum = pnum_()
df2.imshow(chip, fnum=fnum, pnum=pnum)
kpts_kw = dict(ell_linewidth=5, ell_alpha=1.0)
kpts_kw.update(kwargs)
df2.draw_kpts2([kp], **kpts_kw)
if draw_unwarped:
# Draw the unwarped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)))
# pnum = pnum_(px + six.next(countgen)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum)
ph.set_plotdat(ax, "viztype", "unwarped")
ph.set_plotdat(ax, "aid", aid)
ph.set_plotdat(ax, "fx", fx)
if shape_labels:
unwarped_lbl = "affine feature invV =\n" + shape_str + "\n" + ori_str
custom_figure.set_xlabel(unwarped_lbl, ax)
if draw_warped:
# Draw the warped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)), warped=True)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum, warped=True, **kwargs)
ph.set_plotdat(ax, "viztype", "warped")
ph.set_plotdat(ax, "aid", aid)
ph.set_plotdat(ax, "fx", fx)
if shape_labels:
warped_lbl = ("warped feature\n" + "fx=%r scale=%.1f\n" + "%s") % (fx, scale, xy_str)
else:
warped_lbl = ""
warped_lbl += info
custom_figure.set_xlabel(warped_lbl, ax)
if draw_desc:
border_color = {
"None": None,
"query": None,
"match": custom_constants.BLUE,
"norm": custom_constants.ORANGE,
}.get(str(type_).lower(), None)
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
# pnum = pnum_(px + six.next(countgen))
pnum = pnum_()
sift_as_vecfield = ph.SIFT_OR_VECFIELD or vecfield
if sift_as_vecfield:
custom_figure.figure(fnum=fnum, pnum=pnum)
df2.draw_keypoint_gradient_orientations(chip, kp, sift=sift)
else:
if sift.dtype.type == np.uint8:
sigtitle = "sift histogram" if (px % 3) == 0 else ""
ax = df2.plot_sift_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
else:
sigtitle = "descriptor vector" if (px % 3) == 0 else ""
ax = df2.plot_descriptor_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
ax._hs_viztype = "histogram"
# dist_list = ['L1', 'L2', 'hist_isect', 'emd']
# dist_list = ['L2', 'hist_isect']
# dist_list = ['L2']
# dist_list = ['bar_L2_sift', 'cos_sift']
# dist_list = ['L2_sift', 'bar_cos_sift']
dist_list = ["L2_sift"]
dist_str_list = []
if origsift is not None:
distmap_orig = vt.compute_distances(sift, origsift, dist_list)
dist_str_list.append(
"query_dist: "
+ ", ".join(["(%s, %s)" % (key, formatdist(val)) for key, val in six.iteritems(distmap_orig)])
)
if prevsift is not None:
distmap_prev = vt.compute_distances(sift, prevsift, dist_list)
dist_str_list.append(
"prev_dist: "
+ ", ".join(["(%s, %s)" % (key, formatdist(val)) for key, val in six.iteritems(distmap_prev)])
)
dist_str = "\n".join(dist_str_list)
custom_figure.set_xlabel(dist_str)
return px + nCols
def get_orgres_desc_match_dists(allres, orgtype_list=['false', 'true'],
distkey_list=['L2'],
verbose=True):
r"""
computes distances between matching descriptors of orgtypes in allres
Args:
allres (AllResults): AllResults object
orgtype_list (list): of strings denoting the type of results to compare
distkey_list (list): list of requested distance types
Returns:
dict: orgres2_descmatch_dists mapping from orgtype to dicts of distances (ndarrays)
Notes:
Just SIFT distance seems to have a very interesting property
CommandLine:
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --distkeys=fs,lnbnn,bar_L2_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=fs,lnbnn,bar_L2_sift,cos_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --distkeys=fs,lnbnn,bar_L2_sift,cos_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=cos_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --distkeys=fs,lnbnn,bar_L2_sift,cos_sift --show --nosupport
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+siam128
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift --num-top-fs=2
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift --num-top-fs=10
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift --num-top-fs=1000
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+siam128 --num-top-fs=1
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.expt.results_analyzer import * # NOQA
>>> from ibeis.expt import results_all
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='PZ_MTEST')
>>> qaid_list = ibs.get_valid_aids(hasgt=True)
>>> from ibeis.model import Config
>>> cfgdict = ut.argparse_dict(dict(Config.parse_config_items(Config.QueryConfig())), only_specified=True)
>>> allres = results_all.get_allres(ibs, qaid_list, cfgdict=cfgdict)
>>> # {'feat_type': 'hesaff+siam128'})
>>> orgtype_list = ['false', 'top_true']
>>> verbose = True
>>> distkey_list = ut.get_argval('--distkeys', type_=list, default=['fs', 'lnbnn', 'bar_L2_sift'])
>>> #distkey_list = ['hist_isect']
>>> #distkey_list = ['L2_sift', 'bar_L2_sift']
>>> # execute function
>>> orgres2_descmatch_dists = get_orgres_desc_match_dists(allres, orgtype_list, distkey_list, verbose)
>>> #print('orgres2_descmatch_dists = ' + ut.dict_str(orgres2_descmatch_dists, truncate=-1, precision=3))
>>> stats_ = {key: ut.dict_val_map(val, ut.get_stats) for key, val in orgres2_descmatch_dists.items()}
>>> print('orgres2_descmatch_dists = ' + ut.dict_str(stats_, truncate=2, precision=3, nl=4))
>>> # ------ VISUALIZE ------------
>>> ut.quit_if_noshow()
>>> import vtool as vt
>>> # If viewing a large amount of data this might help on OverFlowError
>>> #ut.embed()
>>> # http://stackoverflow.com/questions/20330475/matplotlib-overflowerror-allocated-too-many-blocks
>>> # http://matplotlib.org/1.3.1/users/customizing.html
>>> limit_ = len(qaid_list) > 100
>>> if limit_ or True:
>>> import matplotlib as mpl
>>> mpl.rcParams['agg.path.chunksize'] = 100000
>>> # visualize the descriptor scores
>>> for fnum, distkey in enumerate(distkey_list, start=1):
>>> encoder = vt.ScoreNormalizer()
>>> tn_scores, tp_scores = ut.get_list_column(ut.dict_take(orgres2_descmatch_dists, orgtype_list), distkey)
>>> encoder.fit_partitioned(tp_scores, tn_scores, verbose=False)
>>> figtitle = 'Descriptor Distance: %r. db=%r\norgtype_list=%r' % (distkey, ibs.get_dbname(), orgtype_list)
>>> use_support = not ut.get_argflag('--nosupport')
>>> encoder.visualize(figtitle=figtitle, use_stems=not limit_, fnum=fnum, with_normscore=use_support, with_scores=use_support)
>>> ut.show_if_requested()
"""
import vtool as vt
orgres2_descmatch_dists = {}
desc_dist_xs, other_xs = vt.index_partition(distkey_list, vt.VALID_DISTS)
distkey_list1 = ut.list_take(distkey_list, desc_dist_xs)
distkey_list2 = ut.list_take(distkey_list, other_xs)
for orgtype in orgtype_list:
if verbose:
print('[rr2] getting orgtype=%r distances between vecs' % orgtype)
orgres = allres.get_orgtype(orgtype)
qaids = orgres.qaids
aids = orgres.aids
# DO distance that need real computation
if len(desc_dist_xs) > 0:
try:
stacked_qvecs, stacked_dvecs = get_matching_descriptors(allres, qaids, aids)
except Exception as ex:
orgres.printme3()
ut.printex(ex)
raise
if verbose:
print('[rr2] * stacked_qvecs.shape = %r' % (stacked_qvecs.shape,))
print('[rr2] * stacked_dvecs.shape = %r' % (stacked_dvecs.shape,))
#distkey_list = ['L1', 'L2', 'hist_isect', 'emd']
#distkey_list = ['L1', 'L2', 'hist_isect']
#distkey_list = ['L2', 'hist_isect']
hist1 = np.asarray(stacked_qvecs, dtype=np.float32)
hist2 = np.asarray(stacked_dvecs, dtype=np.float32)
# returns an ordered dictionary
distances1 = vt.compute_distances(hist1, hist2, distkey_list1)
else:
distances1 = {}
# DO precomputed distances like fs (true weights) or lnbnn
if len(other_xs) > 0:
distances2 = ut.odict([(disttype, []) for disttype in distkey_list2])
for qaid, daid in zip(qaids, aids):
try:
qres = allres.qaid2_qres[qaid]
for disttype in distkey_list2:
if disttype == 'fs':
# hack in full fs
assert disttype == 'fs', 'unimplemented'
vals = qres.aid2_fs[daid]
else:
assert disttype in qres.filtkey_list, 'no score labeled %' % (disttype,)
index = qres.filtkey_list.index(disttype)
vals = qres.aid2_fsv[daid].T[index]
if len(vals) == 0:
continue
else:
# individual score component
pass
#num_top_vec_scores = None
num_top_vec_scores = ut.get_argval('--num-top-fs', type_=int, default=None)
if num_top_vec_scores is not None:
# Take only the best matching descriptor scores for each pair in this analysis
# This tries to see how deperable the BEST descriptor score is for each match
vals = vals[vals.argsort()[::-1][0:num_top_vec_scores]]
vals = vals[vals.argsort()[::-1][0:num_top_vec_scores]]
distances2[disttype].extend(vals)
except KeyError:
continue
# convert to numpy array
for disttype in distkey_list2:
distances2[disttype] = np.array(distances2[disttype])
else:
distances2 = {}
# Put things back in expected order
dist1_vals = ut.dict_take(distances1, distkey_list1)
dist2_vals = ut.dict_take(distances2, distkey_list2)
dist_vals = vt.rebuild_partition(dist1_vals, dist2_vals, desc_dist_xs, other_xs)
distances = ut.odict(list(zip(distkey_list, dist_vals)))
orgres2_descmatch_dists[orgtype] = distances
return orgres2_descmatch_dists
def get_orgres_desc_match_dists(allres,
orgtype_list=['false', 'true'],
distkey_list=['L2'],
verbose=True):
r"""
computes distances between matching descriptors of orgtypes in allres
Args:
allres (AllResults): AllResults object
orgtype_list (list): of strings denoting the type of results to compare
distkey_list (list): list of requested distance types
Returns:
dict: orgres2_descmatch_dists mapping from orgtype to dicts of distances (ndarrays)
Notes:
Just SIFT distance seems to have a very interesting property
CommandLine:
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --distkeys=fs,lnbnn,bar_L2_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=fs,lnbnn,bar_L2_sift,cos_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --distkeys=fs,lnbnn,bar_L2_sift,cos_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=cos_sift --show
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_Master0 --distkeys=fs,lnbnn,bar_L2_sift,cos_sift --show --nosupport
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+siam128
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift --num-top-fs=2
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift --num-top-fs=10
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+sift --num-top-fs=1000
python -m ibeis.expt.results_analyzer --test-get_orgres_desc_match_dists --db PZ_MTEST --distkeys=lnbnn --show --feat_type=hesaff+siam128 --num-top-fs=1
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.expt.results_analyzer import * # NOQA
>>> from ibeis.expt import results_all
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='PZ_MTEST')
>>> qaid_list = ibs.get_valid_aids(hasgt=True)
>>> from ibeis.model import Config
>>> cfgdict = ut.argparse_dict(dict(Config.parse_config_items(Config.QueryConfig())), only_specified=True)
>>> allres = results_all.get_allres(ibs, qaid_list, cfgdict=cfgdict)
>>> # {'feat_type': 'hesaff+siam128'})
>>> orgtype_list = ['false', 'top_true']
>>> verbose = True
>>> distkey_list = ut.get_argval('--distkeys', type_=list, default=['fs', 'lnbnn', 'bar_L2_sift'])
>>> #distkey_list = ['hist_isect']
>>> #distkey_list = ['L2_sift', 'bar_L2_sift']
>>> # execute function
>>> orgres2_descmatch_dists = get_orgres_desc_match_dists(allres, orgtype_list, distkey_list, verbose)
>>> #print('orgres2_descmatch_dists = ' + ut.dict_str(orgres2_descmatch_dists, truncate=-1, precision=3))
>>> stats_ = {key: ut.dict_val_map(val, ut.get_stats) for key, val in orgres2_descmatch_dists.items()}
>>> print('orgres2_descmatch_dists = ' + ut.dict_str(stats_, truncate=2, precision=3, nl=4))
>>> # ------ VISUALIZE ------------
>>> ut.quit_if_noshow()
>>> import vtool as vt
>>> # If viewing a large amount of data this might help on OverFlowError
>>> #ut.embed()
>>> # http://stackoverflow.com/questions/20330475/matplotlib-overflowerror-allocated-too-many-blocks
>>> # http://matplotlib.org/1.3.1/users/customizing.html
>>> limit_ = len(qaid_list) > 100
>>> if limit_ or True:
>>> import matplotlib as mpl
>>> mpl.rcParams['agg.path.chunksize'] = 100000
>>> # visualize the descriptor scores
>>> for fnum, distkey in enumerate(distkey_list, start=1):
>>> encoder = vt.ScoreNormalizer()
>>> tn_scores, tp_scores = ut.get_list_column(ut.dict_take(orgres2_descmatch_dists, orgtype_list), distkey)
>>> encoder.fit_partitioned(tp_scores, tn_scores, verbose=False)
>>> figtitle = 'Descriptor Distance: %r. db=%r\norgtype_list=%r' % (distkey, ibs.get_dbname(), orgtype_list)
>>> use_support = not ut.get_argflag('--nosupport')
>>> encoder.visualize(figtitle=figtitle, use_stems=not limit_, fnum=fnum, with_normscore=use_support, with_scores=use_support)
>>> ut.show_if_requested()
"""
import vtool as vt
orgres2_descmatch_dists = {}
desc_dist_xs, other_xs = vt.index_partition(distkey_list, vt.VALID_DISTS)
distkey_list1 = ut.list_take(distkey_list, desc_dist_xs)
distkey_list2 = ut.list_take(distkey_list, other_xs)
for orgtype in orgtype_list:
if verbose:
print('[rr2] getting orgtype=%r distances between vecs' % orgtype)
orgres = allres.get_orgtype(orgtype)
qaids = orgres.qaids
aids = orgres.aids
# DO distance that need real computation
if len(desc_dist_xs) > 0:
try:
stacked_qvecs, stacked_dvecs = get_matching_descriptors(
allres, qaids, aids)
except Exception as ex:
orgres.printme3()
ut.printex(ex)
raise
if verbose:
print('[rr2] * stacked_qvecs.shape = %r' %
(stacked_qvecs.shape, ))
print('[rr2] * stacked_dvecs.shape = %r' %
(stacked_dvecs.shape, ))
#distkey_list = ['L1', 'L2', 'hist_isect', 'emd']
#distkey_list = ['L1', 'L2', 'hist_isect']
#distkey_list = ['L2', 'hist_isect']
hist1 = np.asarray(stacked_qvecs, dtype=np.float32)
hist2 = np.asarray(stacked_dvecs, dtype=np.float32)
# returns an ordered dictionary
distances1 = vt.compute_distances(hist1, hist2, distkey_list1)
else:
distances1 = {}
# DO precomputed distances like fs (true weights) or lnbnn
if len(other_xs) > 0:
distances2 = ut.odict([(disttype, [])
for disttype in distkey_list2])
for qaid, daid in zip(qaids, aids):
try:
qres = allres.qaid2_qres[qaid]
for disttype in distkey_list2:
if disttype == 'fs':
# hack in full fs
assert disttype == 'fs', 'unimplemented'
vals = qres.aid2_fs[daid]
else:
assert disttype in qres.filtkey_list, 'no score labeled %' % (
disttype, )
index = qres.filtkey_list.index(disttype)
vals = qres.aid2_fsv[daid].T[index]
if len(vals) == 0:
continue
else:
# individual score component
pass
#num_top_vec_scores = None
num_top_vec_scores = ut.get_argval('--num-top-fs',
type_=int,
default=None)
if num_top_vec_scores is not None:
# Take only the best matching descriptor scores for each pair in this analysis
# This tries to see how deperable the BEST descriptor score is for each match
vals = vals[vals.argsort()[::-1]
[0:num_top_vec_scores]]
vals = vals[vals.argsort()[::-1]
[0:num_top_vec_scores]]
distances2[disttype].extend(vals)
except KeyError:
continue
# convert to numpy array
for disttype in distkey_list2:
distances2[disttype] = np.array(distances2[disttype])
else:
distances2 = {}
# Put things back in expected order
dist1_vals = ut.dict_take(distances1, distkey_list1)
dist2_vals = ut.dict_take(distances2, distkey_list2)
dist_vals = vt.rebuild_partition(dist1_vals, dist2_vals, desc_dist_xs,
other_xs)
distances = ut.odict(list(zip(distkey_list, dist_vals)))
orgres2_descmatch_dists[orgtype] = distances
return orgres2_descmatch_dists
