def _annotate_kpts(kpts, sel_fx, draw_ell, draw_pts, color=None, nRandKpts=None, rect=False):
#print('[viz] _annotate_kpts()')
if color is None:
color = 'distinct' if sel_fx is None else df2.ORANGE
ell_args = {
'ell': draw_ell,
'pts': draw_pts,
'rect': rect,
'ell_alpha': .4,
'ell_linewidth': 2,
'ell_color': color,
}
if draw_ell and nRandKpts is not None:
# show a random sample of kpts
nkpts1 = len(kpts)
fxs1 = np.arange(nkpts1)
size = nRandKpts
replace = False
p = np.ones(nkpts1)
p = p / p.sum()
fxs_randsamp = np.random.choice(fxs1, size, replace, p)
kpts = kpts[fxs_randsamp]
# TODO Fix this. This should not set the xlabel
df2.set_xlabel('displaying %r/%r keypoints' % (nRandKpts, nkpts1))
elif draw_ell or draw_pts:
# draw all keypoints
df2.draw_kpts2(kpts, **ell_args)
if sel_fx is not None:
# Draw selected keypoint
sel_kpts = kpts[sel_fx:sel_fx + 1]
df2.draw_kpts2(sel_kpts, ell_color=df2.BLUE, arrow=True, rect=True)
def plot_rank_histogram(allres, orgres_type):
print('[viz] plotting %r rank histogram' % orgres_type)
ranks = allres.__dict__[orgres_type].ranks
label = 'P(rank | ' + orgres_type + ' match)'
title = orgres_type + ' match rankings histogram\n' + allres.title_suffix
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_histpdf(ranks, label=label) # FIXME
df2.set_xlabel('ground truth ranks')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.hs, 'rankviz')
def plot_score_pdf(allres, orgres_type, colorx=0.0, variation_truncate=False):
print('[viz] plotting ' + orgres_type + ' score pdf')
title = orgres_type + ' match score frequencies\n' + allres.title_suffix
scores = allres.__dict__[orgres_type].scores
print('[viz] len(scores) = %r ' % (len(scores), ))
label = 'P(score | %r)' % orgres_type
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_pdf(scores, label=label, colorx=colorx)
if variation_truncate:
df2.variation_trunctate(scores)
#df2.variation_trunctate(false.scores)
df2.set_xlabel('score')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.hs, 'scoreviz')
def plot_rank_stem(allres, orgres_type='true'):
print('[viz] plotting rank stem')
# Visualize rankings with the stem plot
hs = allres.hs
title = orgres_type + 'rankings stem plot\n' + allres.title_suffix
orgres = allres.__dict__[orgres_type]
df2.figure(fnum=FIGNUM, doclf=True, title=title)
x_data = orgres.qcxs
y_data = orgres.ranks
df2.draw_stems(x_data, y_data)
slice_num = int(np.ceil(np.log10(len(orgres.qcxs))))
df2.set_xticks(hs.test_sample_cx[::slice_num])
df2.set_xlabel('query chip indeX (qcx)')
df2.set_ylabel('groundtruth chip ranks')
#df2.set_yticks(list(seen_ranks))
__dump_or_browse(allres.hs, 'rankviz')
def draw_feat_row(rchip,
fx,
kp,
sift,
fnum,
nRows,
nCols,
px,
prevsift=None,
cx=None,
info='',
type_=None):
pnum_ = lambda px: (nRows, nCols, px)
def _draw_patch(**kwargs):
return extract_patch.draw_keypoint_patch(rchip, kp, sift, **kwargs)
# Feature strings
xy_str, acd_str, scale = kp_info(kp)
# Draw the unwarped selected feature
ax = _draw_patch(fnum=fnum, pnum=pnum_(px + 1))
ax._hs_viewtype = 'unwarped'
ax._hs_cx = cx
ax._hs_fx = fx
unwarped_lbl = 'affine feature inv(A) =\n' + acd_str
df2.set_xlabel(unwarped_lbl, ax)
# Draw the warped selected feature
ax = _draw_patch(fnum=fnum, pnum=pnum_(px + 2), warped=True)
ax._hs_viewtype = 'warped'
ax._hs_cx = cx
ax._hs_fx = fx
warped_lbl = ('warped feature\n' + 'fx=%r scale=%.1f\n' + '%s' +
info) % (fx, scale, xy_str)
df2.set_xlabel(warped_lbl, ax)
border_color = {
None: None,
'query': None,
'match': df2.BLUE,
'norm': df2.ORANGE
}[type_]
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
sigtitle = '' if px != 3 else 'sift histogram'
ax = df2.plot_sift_signature(sift, sigtitle, fnum=fnum, pnum=pnum_(px + 3))
ax._hs_viewtype = 'histogram'
if prevsift is not None:
from hotspotter import algos
dist_list = ['L1', 'L2', 'hist_isect', 'emd']
distmap = algos.compute_distances(sift, prevsift, dist_list)
dist_str = ', '.join(
['(%s, %.1E)' % (key, val) for key, val in distmap.iteritems()])
df2.set_xlabel(dist_str)
return px + nCols
def _annotate_kpts(kpts,
sel_fx,
draw_ell,
draw_pts,
color=None,
nRandKpts=None,
rect=False):
#print('[viz] _annotate_kpts()')
if color is None:
color = 'distinct' if sel_fx is None else df2.ORANGE
ell_args = {
'ell': draw_ell,
'pts': draw_pts,
'rect': rect,
'ell_alpha': .4,
'ell_linewidth': 2,
'ell_color': color,
}
if draw_ell and nRandKpts is not None:
# show a random sample of kpts
nkpts1 = len(kpts)
fxs1 = np.arange(nkpts1)
size = nRandKpts
replace = False
p = np.ones(nkpts1)
p = p / p.sum()
fxs_randsamp = np.random.choice(fxs1, size, replace, p)
kpts = kpts[fxs_randsamp]
# TODO Fix this. This should not set the xlabel
df2.set_xlabel('displaying %r/%r keypoints' % (nRandKpts, nkpts1))
elif draw_ell or draw_pts:
# draw all keypoints
df2.draw_kpts2(kpts, **ell_args)
if sel_fx is not None:
# Draw selected keypoint
sel_kpts = kpts[sel_fx:sel_fx + 1]
df2.draw_kpts2(sel_kpts, ell_color=df2.BLUE, arrow=True, rect=True)
def plot_score_matrix(allres):
print('[viz] plotting score matrix')
score_matrix = allres.score_matrix
title = 'Score Matrix\n' + allres.title_suffix
# Find inliers
#inliers = helpers.find_std_inliers(score_matrix)
#max_inlier = score_matrix[inliers].max()
# Trunate above 255
score_img = np.copy(score_matrix)
#score_img[score_img < 0] = 0
#score_img[score_img > 255] = 255
#dim = 0
#score_img = helpers.norm_zero_one(score_img, dim=dim)
# Make colors
scores = score_img.flatten()
colors = df2.scores_to_color(scores, logscale=True)
cmap = df2.scores_to_cmap(scores, colors)
df2.figure(fnum=FIGNUM, doclf=True, title=title)
# Show score matrix
df2.imshow(score_img, fnum=FIGNUM, cmap=cmap)
# Colorbar
df2.colorbar(scores, colors)
df2.set_xlabel('database')
df2.set_ylabel('queries')
def draw_feat_row(rchip, fx, kp, sift, fnum, nRows, nCols, px, prevsift=None,
cx=None, info='', type_=None):
pnum_ = lambda px: (nRows, nCols, px)
def _draw_patch(**kwargs):
return extract_patch.draw_keypoint_patch(rchip, kp, sift, **kwargs)
# Feature strings
xy_str, acd_str, scale = kp_info(kp)
# Draw the unwarped selected feature
ax = _draw_patch(fnum=fnum, pnum=pnum_(px + 1))
ax._hs_viewtype = 'unwarped'
ax._hs_cx = cx
ax._hs_fx = fx
unwarped_lbl = 'affine feature inv(A) =\n' + acd_str
df2.set_xlabel(unwarped_lbl, ax)
# Draw the warped selected feature
ax = _draw_patch(fnum=fnum, pnum=pnum_(px + 2), warped=True)
ax._hs_viewtype = 'warped'
ax._hs_cx = cx
ax._hs_fx = fx
warped_lbl = ('warped feature\n' +
'fx=%r scale=%.1f\n' +
'%s' + info) % (fx, scale, xy_str)
df2.set_xlabel(warped_lbl, ax)
border_color = {None: None,
'query': None,
'match': df2.BLUE,
'norm': df2.ORANGE}[type_]
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
sigtitle = '' if px != 3 else 'sift histogram'
ax = df2.plot_sift_signature(sift, sigtitle, fnum=fnum, pnum=pnum_(px + 3))
ax._hs_viewtype = 'histogram'
if prevsift is not None:
from hotspotter import algos
dist_list = ['L1', 'L2', 'hist_isect', 'emd']
distmap = algos.compute_distances(sift, prevsift, dist_list)
dist_str = ', '.join(['(%s, %.1E)' % (key, val) for key, val in distmap.iteritems()])
df2.set_xlabel(dist_str)
return px + nCols
def annotate_chipres(hs,
res,
cx,
showTF=True,
showScore=True,
title_pref='',
title_suff='',
show_2nd_gname=True,
show_2nd_name=True,
show_1st=True,
time_appart=True,
in_image=False,
offset1=(0, 0),
offset2=(0, 0),
show_query=True,
xywh2=None,
**kwargs):
printDBG('[viz] annotate_chipres()')
#print('Did not expect args: %r' % (kwargs.keys(),))
qcx = res.qcx
score = res.cx2_score[cx]
matched_kpts = np.float32(len(res.cx2_fs[cx]))
# print('matched_kpts= %r'%str(matched_kpts))
# TODO Use this function when you clean show_chipres
(truestr, falsestr, nonamestr) = ('TRUE', 'FALSE', '???')
is_true, is_unknown = hs.is_true_match(qcx, cx)
isgt_str = nonamestr if is_unknown else (truestr if is_true else falsestr)
match_color = {
nonamestr: df2.UNKNOWN_PURP,
truestr: df2.TRUE_GREEN,
falsestr: df2.FALSE_RED
}[isgt_str]
# Build title
title = '*%s*' % isgt_str if showTF else ''
if showScore:
score_str = (' score=' + helpers.num_fmt(score)) % (score)
score_str += (' matched_kpts=' +
helpers.num_fmt(matched_kpts)) % (matched_kpts)
title += score_str
title = title_pref + str(title) + title_suff
# Build xlabel
xlabel_ = []
if 'show_1st':
xlabel_.append('top_gname=%r' % hs.cx2_gname(qcx))
xlabel_.append('top_name=%r' % hs.cx2_name(qcx))
if 'show_2nd_gname':
xlabel_.append('\n below_gname=%r' % hs.cx2_gname(cx))
if 'show_2nd_name':
xlabel_.append('below_name=%r' % hs.cx2_name(cx))
if 'time_appart':
xlabel_.append('\n' + hs.get_timedelta_str(qcx, cx))
xlabel = ', '.join(xlabel_)
ax = df2.gca()
ax._hs_viewtype = 'chipres'
ax._hs_qcx = qcx
ax._hs_cx = cx
if NO_LABEL_OVERRIDE:
title = ''
xlabel = ''
df2.set_title(title, ax)
df2.set_xlabel(xlabel, ax)
if in_image:
roi1 = hs.cx2_roi(qcx) + np.array(list(offset1) + [0, 0])
roi2 = hs.cx2_roi(cx) + np.array(list(offset2) + [0, 0])
theta1 = hs.cx2_theta(qcx)
theta2 = hs.cx2_theta(cx)
# HACK!
lbl1 = 'q' + hs.cidstr(qcx)
lbl2 = hs.cidstr(cx)
if show_query:
df2.draw_roi(roi1, bbox_color=df2.ORANGE, label=lbl1, theta=theta1)
df2.draw_roi(roi2, bbox_color=match_color, label=lbl2, theta=theta2)
# No matches draw a red box
if len(res.cx2_fm[cx]) == 0:
df2.draw_boxedX(roi2, theta=theta2)
else:
if xywh2 is None:
xy, w, h = df2._axis_xy_width_height(ax)
xywh2 = (xy[0], xy[1], w, h)
df2.draw_border(ax, match_color, 4, offset=offset2)
# No matches draw a red box
if len(res.cx2_fm[cx]) == 0:
df2.draw_boxedX(xywh2)
def annotate_chipres(hs, res, cx, showTF=True, showScore=True, title_pref='',
title_suff='', show_gname=False, show_name=True,
time_appart=True, in_image=False, offset1=(0, 0),
offset2=(0, 0), show_query=True, xywh2=None, **kwargs):
printDBG('[viz] annotate_chipres()')
#print('Did not expect args: %r' % (kwargs.keys(),))
qcx = res.qcx
score = res.cx2_score[cx]
# TODO Use this function when you clean show_chipres
(truestr, falsestr, nonamestr) = ('TRUE', 'FALSE', '???')
is_true, is_unknown = hs.is_true_match(qcx, cx)
isgt_str = nonamestr if is_unknown else (truestr if is_true else falsestr)
match_color = {nonamestr: df2.UNKNOWN_PURP,
truestr: df2.TRUE_GREEN,
falsestr: df2.FALSE_RED}[isgt_str]
# Build title
title = '*%s*' % isgt_str if showTF else ''
if showScore:
score_str = (' score=' + helpers.num_fmt(score)) % (score)
title += score_str
title = title_pref + str(title) + title_suff
# Build xlabel
xlabel_ = []
if 'show_gname':
xlabel_.append('gname=%r' % hs.cx2_gname(cx))
if 'show_name':
xlabel_.append('name=%r' % hs.cx2_name(cx))
if 'time_appart':
xlabel_.append('\n' + hs.get_timedelta_str(qcx, cx))
xlabel = ', '.join(xlabel_)
ax = df2.gca()
ax._hs_viewtype = 'chipres'
ax._hs_qcx = qcx
ax._hs_cx = cx
if NO_LABEL_OVERRIDE:
title = ''
xlabel = ''
df2.set_title(title, ax)
df2.set_xlabel(xlabel, ax)
if in_image:
roi1 = hs.cx2_roi(qcx) + np.array(list(offset1) + [0, 0])
roi2 = hs.cx2_roi(cx) + np.array(list(offset2) + [0, 0])
theta1 = hs.cx2_theta(qcx)
theta2 = hs.cx2_theta(cx)
# HACK!
lbl1 = 'q' + hs.cidstr(qcx)
lbl2 = hs.cidstr(cx)
if show_query:
df2.draw_roi(roi1, bbox_color=df2.ORANGE, label=lbl1, theta=theta1)
df2.draw_roi(roi2, bbox_color=match_color, label=lbl2, theta=theta2)
# No matches draw a red box
if len(res.cx2_fm[cx]) == 0:
df2.draw_boxedX(roi2, theta=theta2)
else:
if xywh2 is None:
xy, w, h = df2._axis_xy_width_height(ax)
xywh2 = (xy[0], xy[1], w, h)
df2.draw_border(ax, match_color, 4, offset=offset2)
# No matches draw a red box
if len(res.cx2_fm[cx]) == 0:
df2.draw_boxedX(xywh2)
