def plot_keypoint_scales(hs, fnum=1):
print('[dev] plot_keypoint_scales()')
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(pytex.latex_scalar(r'\# keypoints, ', len(kpts)))
print(pytex.latex_mystats(r'\# keypoints per image', cx2_nFeats))
acd = kpts[:, 2:5].T
scales = np.sqrt(acd[0] * acd[2])
scales = np.array(sorted(scales))
print(pytex.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 plot_keypoint_scales(hs, fnum=1):
print('[dev] plot_keypoint_scales()')
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(pytex.latex_scalar(r'\# keypoints, ', len(kpts)))
print(pytex.latex_mystats(r'\# keypoints per image', cx2_nFeats))
acd = kpts[:, 2:5].T
scales = np.sqrt(acd[0] * acd[2])
scales = np.array(sorted(scales))
print(pytex.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 show_image(back, gx, sel_cxs=[], figtitle='Image View', **kwargs):
fnum = FNUMS['image']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
interact.interact_image(back.hs, gx, sel_cxs, back.select_cx,
fnum=fnum, figtitle=figtitle)
if not did_exist:
back.layout_figures()
def show_single_query(back, res, cx, **kwargs):
# Define callback for show_analysis
fnum = FNUMS['inspect']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
interact.interact_chipres(back.hs, res, cx=cx, fnum=fnum)
if not did_exist:
back.layout_figures()
def show_single_query(back, res, cx, **kwargs):
# Define callback for show_analysis
fnum = FNUMS['inspect']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
interact.interact_chipres(back.hs, res, cx=cx, fnum=fnum)
if not did_exist:
back.layout_figures()
def viz_localmax(signal1d):
#signal1d = np.array(hist)
from hsviz import draw_func2 as df2
signal1d = np.array(signal1d)
maxpos = np.array(localmax(signal1d))
x_data = range(len(signal1d))
y_data = signal1d
df2.figure('localmax vizualization')
df2.plot(x_data, y_data)
df2.plot(maxpos, signal1d[maxpos], 'ro')
df2.update()
def viz_localmax(signal1d):
#signal1d = np.array(hist)
from hsviz import draw_func2 as df2
signal1d = np.array(signal1d)
maxpos = np.array(localmax(signal1d))
x_data = range(len(signal1d))
y_data = signal1d
df2.figure('localmax vizualization')
df2.plot(x_data, y_data)
df2.plot(maxpos, signal1d[maxpos], 'ro')
df2.update()
def show_chip(back, cx, **kwargs):
fnum = FNUMS['chip']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
INTERACTIVE_CHIPS = True # This should always be True
if INTERACTIVE_CHIPS:
interact_fn = interact.interact_chip
interact_fn(back.hs, cx, fnum=fnum, figtitle='Chip View')
else:
viz.show_chip(back.hs, cx, fnum=fnum, figtitle='Chip View')
if not did_exist:
back.layout_figures()
def show_chip(back, cx, **kwargs):
fnum = FNUMS['chip']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
INTERACTIVE_CHIPS = True # This should always be True
if INTERACTIVE_CHIPS:
interact_fn = interact.interact_chip
interact_fn(back.hs, cx, fnum=fnum, figtitle='Chip View')
else:
viz.show_chip(back.hs, cx, fnum=fnum, figtitle='Chip View')
if not did_exist:
back.layout_figures()
def show_image(back, gx, sel_cxs=[], figtitle='Image View', **kwargs):
fnum = FNUMS['image']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
interact.interact_image(back.hs,
gx,
sel_cxs,
back.select_cx,
fnum=fnum,
figtitle=figtitle)
if not did_exist:
back.layout_figures()
def top_matching_features(res, axnum=None, match_type=''):
cx2_fs = res.cx2_fs_V
cx_fx_fs_list = []
for cx in xrange(len(cx2_fs)):
fx2_fs = cx2_fs[cx]
for fx in xrange(len(fx2_fs)):
fs = fx2_fs[fx]
cx_fx_fs_list.append((cx, fx, fs))
cx_fx_fs_sorted = np.array(sorted(cx_fx_fs_list, key=lambda x: x[2])[::-1])
sorted_score = cx_fx_fs_sorted[:, 2]
df2.figure(0)
df2.plot(sorted_score)
def top_matching_features(res, axnum=None, match_type=''):
cx2_fs = res.cx2_fs_V
cx_fx_fs_list = []
for cx in xrange(len(cx2_fs)):
fx2_fs = cx2_fs[cx]
for fx in xrange(len(fx2_fs)):
fs = fx2_fs[fx]
cx_fx_fs_list.append((cx, fx, fs))
cx_fx_fs_sorted = np.array(sorted(cx_fx_fs_list, key=lambda x: x[2])[::-1])
sorted_score = cx_fx_fs_sorted[:, 2]
df2.figure(0)
df2.plot(sorted_score)
def viz_chipgraph(hs, graph, fnum=1, with_images=False):
# Adapated from
# https://gist.github.com/shobhit/3236373
print('[encounter] drawing chip graph')
df2.figure(fnum=fnum, pnum=(1, 1, 1))
ax = df2.gca()
#pos = netx.spring_layout(graph)
pos = netx.graphviz_layout(graph)
netx.draw(graph, pos=pos, ax=ax)
if with_images:
cx_list = graph.nodes()
pos_list = [pos[cx] for cx in cx_list]
thumb_list = hs.get_thumb(cx_list, 16, 16)
draw_images_at_positions(thumb_list, pos_list)
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 show_query_result(back, res, tx=None, **kwargs):
if tx is not None:
fnum = FNUMS['inspect']
did_exist = df2.plt.fignum_exists(fnum)
# Interact with the tx\th top index
res.interact_top_chipres(back.hs, tx)
else:
fnum = FNUMS['res']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
if back.hs.prefs.display_cfg.showanalysis:
# Define callback for show_analysis
res.show_analysis(back.hs, fnum=fnum, figtitle=' Analysis View')
else:
res.show_top(back.hs, fnum=fnum, figtitle='Query View ')
if not did_exist:
back.layout_figures()
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 show_query_result(back, res, tx=None, **kwargs):
if tx is not None:
fnum = FNUMS['inspect']
did_exist = df2.plt.fignum_exists(fnum)
# Interact with the tx\th top index
res.interact_top_chipres(back.hs, tx)
else:
fnum = FNUMS['res']
did_exist = df2.plt.fignum_exists(fnum)
df2.figure(fnum=fnum, docla=True, doclf=True)
if back.hs.prefs.display_cfg.showanalysis:
# Define callback for show_analysis
res.show_analysis(back.hs,
fnum=fnum,
figtitle=' Analysis View')
else:
res.show_top(back.hs, fnum=fnum, figtitle='Query View ')
if not did_exist:
back.layout_figures()
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_nx(back, nx, sel_cxs=[], **kwargs):
# Define callback for show_analysis
fnum = FNUMS['name']
df2.figure(fnum=fnum, docla=True, doclf=True)
interact.interact_name(back.hs, nx, sel_cxs, back.select_cx,
fnum=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)
print('[back] database (cid=%r)' % db_cid)
print('[back] database (cx = %r)' % db_cx)
try:
query_res = hs.query(query_cx)
except Exception as ex:
# TODO Catch actually exceptions here
print('[back] ex = %r' % ex)
raise
#-----function: def show_single_query(back, res, cx, **kwargs):
FNUMS = dict(image=1, chip=2, res=3, inspect=4, special=5, name=6)
viz.register_FNUMS(FNUMS)
fnum = FNUMS['inspect']
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,
def show_nx(back, nx, sel_cxs=[], **kwargs):
# Define callback for show_analysis
fnum = FNUMS['name']
df2.figure(fnum=fnum, docla=True, doclf=True)
interact.interact_name(back.hs, nx, sel_cxs, back.select_cx, fnum=fnum)
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)