def vary_two_cfg(hs, qcx, cx, query_cfg, vary_cfg, fnum=1):
if len(vary_cfg) > 2:
raise Exception('can only vary at most two cfgeters')
print('[dev] vary_two_cfg: q' + hs.vs_str(qcx, cx))
cfg_keys = vary_cfg.keys()
cfg_vals = vary_cfg.values()
cfg1_name = cfg_keys[0]
cfg2_name = cfg_keys[1]
cfg1_steps = cfg_vals[0]
cfg2_steps = cfg_vals[1]
nRows = len(cfg1_steps)
nCols = len(cfg2_steps)
print('[dev] Varying configs: nRows=%r, nCols=%r' % (nRows, nCols))
print('[dev] %r = %r ' % (cfg1_name, cfg1_steps))
print('[dev] %r = %r ' % (cfg2_name, cfg2_steps))
ylabel_args = dict(rotation='horizontal',
verticalalignment='bottom',
horizontalalignment='right',
fontproperties=df2.FONTS.medbold)
xlabel_args = dict(fontproperties=df2.FONTS.medbold)
#ax = df2.gca()
# Vary cfg1
#df2..gcf().clf()
print_lock_ = helpers.ModulePrintLock(mc3, df2)
assign_alg = query_cfg.agg_cfg.query_type
vert = not hs.args.horiz
plt_match_args = dict(fnum=fnum, show_gname=False, showTF=False, vert=vert)
for rowx, cfg1_value in enumerate(cfg1_steps):
query_cfg.update_cfg(**{cfg1_name: cfg1_value})
y_title = cfg1_name + '=' + helpers.format(cfg1_value, 3)
# Vary cfg2
for colx, cfg2_value in enumerate(cfg2_steps):
query_cfg.update_cfg(**{cfg2_name: cfg2_value})
pnum = (nRows, nCols, rowx * nCols + colx + 1)
# HACK
#print(pnum)
#print(query_cfg)
# query only the chips of interest (groundtruth) when doing vsone
if assign_alg == 'vsone':
res = hs.query_groundtruth(qcx, query_cfg)
# query the entire database in vsmany (just as fast as vgroundtruth)
elif assign_alg == 'vsmany':
res = hs.query(qcx, query_cfg)
res.plot_single_match(hs, cx, pnum=pnum, **plt_match_args)
x_title = cfg2_name + '=' + helpers.format(cfg2_value, 3) # helpers.commas(cfg2_value, 3)
ax = df2.gca()
if rowx == len(cfg1_steps) - 1:
ax.set_xlabel(x_title, **xlabel_args)
if colx == 0:
ax.set_ylabel(y_title, **ylabel_args)
del print_lock_
vary_title = '%s vary %s and %s' % (assign_alg, cfg1_name, cfg2_name)
figtitle = '%s %s %s' % (vary_title, hs.vs_str(qcx, cx), str(hs.cx2_property(qcx, 'Notes')))
subtitle = mc3.simplify_test_uid(query_cfg.get_uid())
df2.set_figtitle(figtitle, subtitle)
df2.adjust_subplots_xylabels()
fnum += 1
viz.save_if_requested(hs, vary_title)
return fnum
def dump_orgres_matches(allres, orgres_type):
orgres = allres.__dict__[orgres_type]
hs = allres.hs
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(hs.tables.gx2_gname[hs.tables.cx2_gx[qcx]])
result_gname, _ = os.path.splitext(hs.tables.gx2_gname[hs.tables.cx2_gx[cx]])
res = qcx2_res[qcx]
df2.figure(fnum=1, plotnum=121)
df2.show_matches_annote_res(res, hs, cx, SV=False, fnum=1, plotnum=121)
df2.show_matches_annote_res(res, hs, cx, SV=True, fnum=1, plotnum=122)
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 dump_orgres_matches(allres, orgres_type):
orgres = allres.__dict__[orgres_type]
hs = allres.hs
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(
hs.tables.gx2_gname[hs.tables.cx2_gx[qcx]])
result_gname, _ = os.path.splitext(
hs.tables.gx2_gname[hs.tables.cx2_gx[cx]])
res = qcx2_res[qcx]
df2.figure(fnum=1, plotnum=121)
df2.show_matches_annote_res(res, hs, cx, SV=False, fnum=1, plotnum=121)
df2.show_matches_annote_res(res, hs, cx, SV=True, fnum=1, plotnum=122)
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 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
hs = allres.hs
qcx2_res = allres.qcx2_res
import scipy
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 hs.feats.cx2_desc.size == 0:
print(' * forcing load of descriptors')
hs.load_features()
cx2_desc = hs.feats.cx2_desc
cx2_kpts = hs.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 = helpers.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qcx, cx, score, rank) in enumerate(orgres.iter()):
helpers.print_(fmt_str % (ix + 1,))
# Skip low ranks
if rank > 5:
rank_skips.append(qcx)
continue
other_cxs = hs.get_other_indexed_cxs(qcx)
# Skip no groundtruth
if len(other_cxs) == 0:
gt_skips.append(qcx)
continue
res = qcx2_res[qcx]
# Get matching feature indexes
fm = res.cx2_fm[cx]
# Get their scores
fs = res.cx2_fs[cx]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape,))
desc1 = cx2_desc[qcx][fm[:, 0]]
desc2 = cx2_desc[cx][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qcx][fm[:, 0]]
kpts2 = cx2_kpts[cx][fm[:, 1]]
# Get their scale
scale1_m = sv2.keypoint_scale(kpts1)
scale2_m = sv2.keypoint_scale(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 vary_two_cfg(hs, qcx, cx, query_cfg, vary_cfg, fnum=1):
if len(vary_cfg) > 2:
raise Exception('can only vary at most two cfgeters')
print('[dev] vary_two_cfg: q' + hs.vs_str(qcx, cx))
cfg_keys = vary_cfg.keys()
cfg_vals = vary_cfg.values()
cfg1_name = cfg_keys[0]
cfg2_name = cfg_keys[1]
cfg1_steps = cfg_vals[0]
cfg2_steps = cfg_vals[1]
nRows = len(cfg1_steps)
nCols = len(cfg2_steps)
print('[dev] Varying configs: nRows=%r, nCols=%r' % (nRows, nCols))
print('[dev] %r = %r ' % (cfg1_name, cfg1_steps))
print('[dev] %r = %r ' % (cfg2_name, cfg2_steps))
ylabel_args = dict(rotation='horizontal',
verticalalignment='bottom',
horizontalalignment='right',
fontproperties=df2.FONTS.medbold)
xlabel_args = dict(fontproperties=df2.FONTS.medbold)
#ax = df2.gca()
# Vary cfg1
#df2..gcf().clf()
print_lock_ = helpers.ModulePrintLock(mc3, df2)
assign_alg = query_cfg.agg_cfg.query_type
vert = not hs.args.horiz
plt_match_args = dict(fnum=fnum, show_gname=False, showTF=False, vert=vert)
for rowx, cfg1_value in enumerate(cfg1_steps):
query_cfg.update_cfg(**{cfg1_name: cfg1_value})
y_title = cfg1_name + '=' + helpers.format(cfg1_value, 3)
# Vary cfg2
for colx, cfg2_value in enumerate(cfg2_steps):
query_cfg.update_cfg(**{cfg2_name: cfg2_value})
pnum = (nRows, nCols, rowx * nCols + colx + 1)
# HACK
#print(pnum)
#print(query_cfg)
# query only the chips of interest (groundtruth) when doing vsone
if assign_alg == 'vsone':
res = hs.query_groundtruth(qcx, query_cfg)
# query the entire database in vsmany (just as fast as vgroundtruth)
elif assign_alg == 'vsmany':
res = hs.query(qcx, query_cfg)
res.plot_single_match(hs, cx, pnum=pnum, **plt_match_args)
x_title = cfg2_name + '=' + helpers.format(
cfg2_value, 3) # helpers.commas(cfg2_value, 3)
ax = df2.gca()
if rowx == len(cfg1_steps) - 1:
ax.set_xlabel(x_title, **xlabel_args)
if colx == 0:
ax.set_ylabel(y_title, **ylabel_args)
del print_lock_
vary_title = '%s vary %s and %s' % (assign_alg, cfg1_name, cfg2_name)
figtitle = '%s %s %s' % (vary_title, hs.vs_str(
qcx, cx), str(hs.cx2_property(qcx, 'Notes')))
subtitle = mc3.simplify_test_uid(query_cfg.get_uid())
df2.set_figtitle(figtitle, subtitle)
df2.adjust_subplots_xylabels()
fnum += 1
viz.save_if_requested(hs, vary_title)
return fnum
def show_splash(back, fnum, view='Nice', **kwargs):
if df2.plt.fignum_exists(fnum):
df2.figure(fnum=fnum, docla=True, doclf=True)
viz.show_splash(fnum=fnum)
df2.set_figtitle('%s View' % view)
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
hs = allres.hs
qcx2_res = allres.qcx2_res
import scipy
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 hs.feats.cx2_desc.size == 0:
print(' * forcing load of descriptors')
hs.load_features()
cx2_desc = hs.feats.cx2_desc
cx2_kpts = hs.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 = helpers.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qcx, cx, score, rank) in enumerate(orgres.iter()):
helpers.print_(fmt_str % (ix + 1, ))
# Skip low ranks
if rank > 5:
rank_skips.append(qcx)
continue
other_cxs = hs.get_other_indexed_cxs(qcx)
# Skip no groundtruth
if len(other_cxs) == 0:
gt_skips.append(qcx)
continue
res = qcx2_res[qcx]
# Get matching feature indexes
fm = res.cx2_fm[cx]
# Get their scores
fs = res.cx2_fs[cx]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape, ))
desc1 = cx2_desc[qcx][fm[:, 0]]
desc2 = cx2_desc[cx][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qcx][fm[:, 0]]
kpts2 = cx2_kpts[cx][fm[:, 1]]
# Get their scale
scale1_m = sv2.keypoint_scale(kpts1)
scale2_m = sv2.keypoint_scale(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 show_splash(back, fnum, view='Nice', **kwargs):
if df2.plt.fignum_exists(fnum):
df2.figure(fnum=fnum, docla=True, doclf=True)
viz.show_splash(fnum=fnum)
df2.set_figtitle('%s View' % view)
scale_factor=scale_factor)
dstimg_thresh = dstimg.copy()
dstimg_thresh[dstimg_thresh > 0] = 1
# Get matching coverage
hs.prefs.query_cfg.agg_cfg.score_method = 'coverage'
print(hs.get_cache_uid())
res = hs.query(cx)
nTop = 2
for tx in xrange(nTop):
cx2 = res.topN_cxs(hs)[tx]
dstimg1, dstimg2, args_, kwargs_ = test_result_coverage(
hs, res, cx2, scale_factor)
test_find_coverage_score(hs, res)
res.show_chipres(hs, cx2, fnum=fnum)
df2.set_figtitle('matching viz' + str(tx), incanvas=False)
fnum += 1
df2.show_chipmatch2(dstimg1, dstimg2, *args_, fnum=fnum, **kwargs_)
df2.set_figtitle('matching coverage' + str(tx))
fnum += 1
df2.imshow(srcimg, fnum=fnum, heatmap=True)
df2.set_figtitle('gaussian weights')
fnum += 1
df2.imshow(dstimg, fnum=fnum, heatmap=True)
df2.set_figtitle('chip coverage map')
fnum += 1
df2.imshow(dstimg_thresh, fnum=fnum, heatmap=True)
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
#------- function: interact.interact_chipres(back.hs, res, cx=cx, fnum=fnum)
annote_ptr = [0]
pnum=(1, 1, 1)
xywh2_ptr = [None]
#-------function: def _chipmatch_view(pnum=(1, 1, 1), **kwargs):
mode = annote_ptr[0]
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 res and rectify with show_chipres
tup = res_show_chipres(query_res, hs, db_cx, fnum=fnum, pnum=pnum,
draw_lines=draw_lines, draw_ell=draw_ell,
colorbar_=True)
figtitle='Inspect Query Result'
df2.set_figtitle(figtitle + hs.vs_str(query_cx, db_cx))
#
if Flag_save & 1:
fpath = save_dir
usetitle = 'querycid_' + str(query_cid) + '_db' + str(db_cid)
df2.save_figure(fnum=fnum, fpath=fpath, usetitle=usetitle, overwrite=True)
