def show_name(hs, nx, nx2_cxs=None, fnum=0, sel_cxs=[], subtitle='',
annote=False, **kwargs):
print('[viz] show_name nx=%r' % nx)
nx2_name = hs.tables.nx2_name
cx2_nx = hs.tables.cx2_nx
name = nx2_name[nx]
if not nx2_cxs is None:
cxs = nx2_cxs[nx]
else:
cxs = np.where(cx2_nx == nx)[0]
print('[viz] show_name %r' % hs.cidstr(cxs))
nRows, nCols = get_square_row_cols(len(cxs))
print('[viz*] r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum = lambda px: (nRows, nCols, px + 1)
fig = df2.figure(fnum=fnum, pnum=pnum(0), **kwargs)
fig.clf()
# Trigger computation of all chips in parallel
hs.refresh_features(cxs)
for px, cx in enumerate(cxs):
show_chip(hs, cx=cx, pnum=pnum(px), draw_ell=annote, kpts_alpha=.2)
if cx in sel_cxs:
ax = df2.gca()
df2.draw_border(ax, df2.GREEN, 4)
#plot_cx3(hs, cx)
if isinstance(nx, np.ndarray):
nx = nx[0]
if isinstance(name, np.ndarray):
name = name[0]
figtitle = 'Name View nx=%r name=%r' % (nx, name)
df2.set_figtitle(figtitle)
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 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 show_name(hs,
nx,
nx2_cxs=None,
fnum=0,
sel_cxs=[],
subtitle='',
annote=False,
**kwargs):
print('[viz] show_name nx=%r' % nx)
nx2_name = hs.tables.nx2_name
cx2_nx = hs.tables.cx2_nx
name = nx2_name[nx]
if not nx2_cxs is None:
cxs = nx2_cxs[nx]
else:
cxs = np.where(cx2_nx == nx)[0]
print('[viz] show_name %r' % hs.cidstr(cxs))
nRows, nCols = get_square_row_cols(len(cxs))
print('[viz*] r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum = lambda px: (nRows, nCols, px + 1)
fig = df2.figure(fnum=fnum, pnum=pnum(0), **kwargs)
fig.clf()
# Trigger computation of all chips in parallel
hs.refresh_features(cxs)
for px, cx in enumerate(cxs):
show_chip(hs, cx=cx, pnum=pnum(px), draw_ell=annote, kpts_alpha=.2)
if cx in sel_cxs:
ax = df2.gca()
df2.draw_border(ax, df2.GREEN, 4)
#plot_cx3(hs, cx)
if isinstance(nx, np.ndarray):
nx = nx[0]
if isinstance(name, np.ndarray):
name = name[0]
figtitle = 'Name View nx=%r name=%r' % (nx, name)
df2.set_figtitle(figtitle)
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)
def draw_one_kp(patch, kp, plotx, color, desc=None):
fig, ax = df2.imshow(patch, plotnum=(num_rows, num_cols, plotx))
df2.draw_kpts2([kp], ell_color=color, pts=True)
if not desc is None and not '--nodesc' in sys.argv:
df2.draw_sift(desc, [kp])
df2.draw_border(ax, color, 1)
def viz_top_features(hs, res, low, high, fignum=0, draw_chips=True):
from collections import defaultdict
qcx = res.qcx
cx2_nx = hs.tables.cx2_nx
top_patches_list = get_top_scoring_patches(hs, res, low, high)
num_rows = high-low
num_cols = 4
if params.__MATCH_TYPE__ == 'vsmany':
num_cols = 6
# Initialize Figure
fig = df2.figure(fignum+1, plotnum=(num_rows, num_cols,1))
cx2_rchip = defaultdict(int)
cx2_kplist = defaultdict(list)
def draw_one_kp(patch, kp, plotx, color, desc=None):
fig, ax = df2.imshow(patch, plotnum=(num_rows, num_cols, plotx))
df2.draw_kpts2([kp], ell_color=color, pts=True)
if not desc is None and not '--nodesc' in sys.argv:
df2.draw_sift(desc, [kp])
df2.draw_border(ax, color, 1)
for tx, (patches1, patches2, patchesN, cx, feat_score) in enumerate(top_patches_list):
(kp1, subkp1, wkp1, patch1, wpatch1, cx1, desc1) = patches1
(kp2, subkp2, wkp2, patch2, wpatch2, cx2, desc2) = patches2
# draw on table
# Draw Query Keypoint
plotx = (tx*num_cols)
draw_one_kp(patch1, subkp1, plotx+1, df2.GREEN, desc1)
qnx = cx2_nx[qcx]
df2.plt.gca().set_xlabel('qcx=%r; qnx=%r' % (qcx, qnx))
draw_one_kp(wpatch1, wkp1, plotx+2, df2.GREEN, desc1)
# Draw ith < k match
draw_one_kp(patch2, subkp2, plotx+3, df2.BLUE, desc2)
nx = cx2_nx[cx]
df2.plt.gca().set_xlabel('cx=%r; nx=%r' % (cx, nx))
draw_one_kp(wpatch2, wkp2, plotx+4, df2.BLUE, desc2)
df2.plt.gca().set_xlabel('score=%r' % (feat_score))
# Draw k+1th match
if params.__MATCH_TYPE__ == 'vsmany':
(kpN, subkpN, wkpN, patchN, wpatchN, cxN, descN) = patchesN
draw_one_kp(patchN, subkpN, plotx+5, df2.ORANGE, descN)
nxN = cx2_nx[qcx]
df2.plt.gca().set_xlabel('cxN=%r; nxN=%r' % (cxN, nxN))
draw_one_kp(wpatchN, wkpN, plotx+6, df2.ORANGE, descN)
# Get other info
cx2_rchip[cx] = hs.get_chip(cx)
cx2_kplist[qcx].append(kp1)
cx2_kplist[cx].append(kp2)
# Draw annotations on
df2.figure(plotnum=(num_rows, num_cols,1), title='Query Patch')
df2.figure(plotnum=(num_rows, num_cols,2), title='Query Patch')
df2.figure(plotnum=(num_rows, num_cols,3), title='Result Patch')
df2.figure(plotnum=(num_rows, num_cols,4), title='Result Warped')
if params.__MATCH_TYPE__ == 'vsmany':
df2.figure(plotnum=(num_rows, num_cols,5), title='Normalizer Patch')
df2.figure(plotnum=(num_rows, num_cols,6), title='Normalizer Warped')
df2.set_figtitle('Top '+str(low)+' to '+str(high)+' scoring matches')
if not draw_chips:
return
#
# Draw on full images
cx2_rchip[qcx] = hs.get_chip(qcx)
cx_keys = cx2_kplist.keys()
cx_vals = map(len, cx2_kplist.values())
cx_list = [x for (y,x) in sorted(zip(cx_vals, cx_keys))][::-1]
num_chips = len(cx_list)
pltnum_fn = lambda ix: (int(np.ceil(num_chips/2)), 2, ix)
plotnum = pltnum_fn(1)
fig2 = df2.figure(fignum-1, plotnum=plotnum)
for ix, cx in enumerate(cx_list):
plotnum = pltnum_fn(ix+1)
fig2 = df2.figure(fignum-1, plotnum=plotnum)
rchip = cx2_rchip[cx]
fig, ax = df2.imshow(rchip)
title_pref = ''
color = df2.BLUE
if res.qcx == cx:
title_pref = 'q'
color = df2.GREEN
df2.draw_kpts2(cx2_kplist[cx], ell_color=color, ell_alpha=1)
df2.draw_border(ax, color, 2)
nx = cx2_nx[cx]
ax.set_title(title_pref+'cx = %r; nx=%r' % (cx, nx))
gname = hs.get_gname(cx)
ax.set_xlabel(gname)
