def show_nearest_descriptors(hs, qcx, qfx, fnum=None):
if fnum is None:
fnum = df2.next_fnum()
# Inspect the nearest neighbors of a descriptor
dx2_cx = hs.qdat._data_index.ax2_cx
dx2_fx = hs.qdat._data_index.ax2_fx
K = hs.qdat.cfg.nn_cfg.K
Knorm = hs.qdat.cfg.nn_cfg.Knorm
checks = hs.qdat.cfg.nn_cfg.checks
flann = hs.qdat._data_index.flann
qfx2_desc = hs.get_desc(qcx)[qfx:qfx + 1]
try:
(qfx2_dx, qfx2_dist) = flann.nn_index(qfx2_desc, K + Knorm, checks=checks)
qfx2_cx = dx2_cx[qfx2_dx]
qfx2_fx = dx2_fx[qfx2_dx]
def get_extract_tuple(cx, fx, k=-1):
rchip = hs.get_chip(cx)
kp = hs.get_kpts(cx)[fx]
sift = hs.get_desc(cx)[fx]
if k == -1:
info = '\nquery %s, fx=%r' % (hs.cidstr(cx), fx)
type_ = 'query'
elif k < K:
type_ = 'match'
info = '\nmatch %s, fx=%r k=%r, dist=%r' % (hs.cidstr(cx), fx, k, qfx2_dist[0, k])
elif k < Knorm + K:
type_ = 'norm'
info = '\nnorm %s, fx=%r k=%r, dist=%r' % (hs.cidstr(cx), fx, k, qfx2_dist[0, k])
else:
raise Exception('[viz] problem k=%r')
return (rchip, kp, sift, fx, cx, info, type_)
extracted_list = []
extracted_list.append(get_extract_tuple(qcx, qfx, -1))
for k in xrange(K + Knorm):
tup = get_extract_tuple(qfx2_cx[0, k], qfx2_fx[0, k], k)
extracted_list.append(tup)
#print('[viz] K + Knorm = %r' % (K + Knorm))
# Draw the _select_ith_match plot
nRows, nCols = len(extracted_list), 3
# Draw selected feature matches
prevsift = None
df2.figure(fnum=fnum, docla=True, doclf=True)
px = 0 # plot offset
for (rchip, kp, sift, fx, cx, info, type_) in extracted_list:
print('[viz] ' + info.replace('\n', ''))
px = draw_feat_row(rchip, fx, kp, sift, fnum, nRows, nCols, px,
prevsift=prevsift, cx=cx, info=info, type_=type_)
prevsift = sift
df2.adjust_subplots_safe(hspace=1)
except Exception as ex:
print('[viz] Error in show nearest descriptors')
print(ex)
raise
def _show_res(hs, res, **kwargs):
''' Displays query chip, groundtruth matches, and top 5 matches'''
#printDBG('[viz._show_res()] %s ' % helpers.printableVal(locals()))
#printDBG = print
in_image = hs.prefs.display_cfg.show_results_in_image
annote = kwargs.pop('annote', 2) # this is toggled
fnum = kwargs.get('fnum', 3)
figtitle = kwargs.get('figtitle', '')
topN_cxs = kwargs.get('topN_cxs', [])
gt_cxs = kwargs.get('gt_cxs', [])
all_kpts = kwargs.get('all_kpts', False)
interact = kwargs.get('interact', True)
show_query = kwargs.get('show_query', False)
dosquare = kwargs.get('dosquare', False)
if SHOW_QUERY_OVERRIDE is not None:
show_query = SHOW_QUERY_OVERRIDE
max_nCols = 5
if len(topN_cxs) in [6, 7]:
max_nCols = 3
if len(topN_cxs) in [8]:
max_nCols = 4
printDBG('========================')
printDBG('[viz._show_res()]----------------')
all_gts = hs.get_other_indexed_cxs(res.qcx)
_tup = tuple(map(len, (topN_cxs, gt_cxs, all_gts)))
printDBG('[viz._show_res()] #topN=%r #missed_gts=%r/%r' % _tup)
printDBG('[viz._show_res()] * fnum=%r' % (fnum, ))
printDBG('[viz._show_res()] * figtitle=%r' % (figtitle, ))
printDBG('[viz._show_res()] * max_nCols=%r' % (max_nCols, ))
printDBG('[viz._show_res()] * show_query=%r' % (show_query, ))
ranked_cxs = res.topN_cxs(hs, N='all')
# Build a subplot grid
nQuerySubplts = 1 if show_query else 0
nGtSubplts = nQuerySubplts + (0 if gt_cxs is None else len(gt_cxs))
nTopNSubplts = 0 if topN_cxs is None else len(topN_cxs)
nTopNCols = min(max_nCols, nTopNSubplts)
nGTCols = min(max_nCols, nGtSubplts)
nGTCols = max(nGTCols, nTopNCols)
nTopNCols = nGTCols
nGtRows = 0 if nGTCols == 0 else int(np.ceil(nGtSubplts / nGTCols))
nTopNRows = 0 if nTopNCols == 0 else int(np.ceil(nTopNSubplts / nTopNCols))
nGtCells = nGtRows * nGTCols
nRows = nTopNRows + nGtRows
# HACK:
_color_list = df2.distinct_colors(len(topN_cxs))
cx2_color = {cx: _color_list[ox] for ox, cx in enumerate(topN_cxs)}
if IN_IMAGE_OVERRIDE is not None:
in_image = IN_IMAGE_OVERRIDE
# Helpers
def _show_query_fn(plotx_shift, rowcols):
'helper for viz._show_res'
plotx = plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
#print('[viz] Plotting Query: pnum=%r' % (pnum,))
_kwshow = dict(draw_kpts=annote)
_kwshow.update(kwargs)
_kwshow['prefix'] = 'q'
_kwshow['res'] = res
_kwshow['pnum'] = pnum
_kwshow['cx2_color'] = cx2_color
_kwshow['draw_ell'] = annote >= 1
#_kwshow['in_image'] = in_image
show_chip(hs, **_kwshow)
#if in_image:
#roi1 = hs.cx2_roi(res.qcx)
#df2.draw_roi(roi1, bbox_color=df2.ORANGE, label='q' + hs.cidstr(res.qcx))
def _plot_matches_cxs(cx_list, plotx_shift, rowcols):
def _show_matches_fn(cx, orank, pnum):
'Helper function for drawing matches to one cx'
aug = 'rank=%r\n' % orank
#printDBG('[viz._show_res()] plotting: %r' % (pnum,))
_kwshow = dict(draw_ell=annote,
draw_pts=False,
draw_lines=annote,
ell_alpha=.5,
all_kpts=all_kpts)
_kwshow.update(kwargs)
_kwshow['fnum'] = fnum
_kwshow['pnum'] = pnum
_kwshow['title_aug'] = aug
# If we already are showing the query dont show it here
if not show_query:
_kwshow['draw_ell'] = annote == 1
_kwshow['draw_lines'] = annote >= 1
res_show_chipres(res, hs, cx, in_image=in_image, **_kwshow)
else:
_kwshow['draw_ell'] = annote >= 1
if annote == 2:
# TODO Find a better name
_kwshow['color'] = cx2_color[cx]
_kwshow['sel_fx2'] = res.cx2_fm[cx][:, 1]
show_chip(hs, cx, in_image=in_image, **_kwshow)
annotate_chipres(hs,
res,
cx,
in_image=in_image,
show_query=not show_query)
'helper for viz._show_res to draw many cxs'
#printDBG('[viz._show_res()] Plotting Chips %s:' % hs.cidstr(cx_list))
if cx_list is None:
return
for ox, cx in enumerate(cx_list):
plotx = ox + plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
oranks = np.where(ranked_cxs == cx)[0]
if len(oranks) == 0:
orank = -1
continue
orank = oranks[0] + 1
_show_matches_fn(cx, orank, pnum)
if dosquare:
# HACK
nSubplots = nGtSubplts + nTopNSubplts
nRows, nCols = get_square_row_cols(nSubplots, 3)
nTopNCols = nGTCols = nCols
shift_topN = 1
printDBG('nRows, nCols = (%r, %r)' % (nRows, nCols))
else:
shift_topN = nGtCells
if nGtSubplts == 1:
nGTCols = 1
fig = df2.figure(fnum=fnum,
pnum=(nRows, nGTCols, 1),
docla=True,
doclf=True)
df2.disconnect_callback(fig, 'button_press_event')
df2.plt.subplot(nRows, nGTCols, 1)
# Plot Query
if show_query:
_show_query_fn(0, (nRows, nGTCols))
# Plot Ground Truth
_plot_matches_cxs(gt_cxs, nQuerySubplts, (nRows, nGTCols))
_plot_matches_cxs(topN_cxs, shift_topN, (nRows, nTopNCols))
figtitle += ' q%s name=%s' % (hs.cidstr(res.qcx), hs.cx2_name(res.qcx))
df2.set_figtitle(figtitle, incanvas=not NO_LABEL_OVERRIDE)
# Result Interaction
if interact:
printDBG('[viz._show_res()] starting interaction')
def _ctrlclicked_cx(cx):
printDBG('ctrl+clicked cx=%r' % cx)
fnum = FNUMS['special']
fig = df2.figure(fnum=fnum, docla=True, doclf=True)
df2.disconnect_callback(fig, 'button_press_event')
viz_spatial_verification(hs, res.qcx, cx2=cx, fnum=fnum)
fig.canvas.draw()
df2.bring_to_front(fig)
def _clicked_cx(cx):
printDBG('clicked cx=%r' % cx)
fnum = FNUMS['inspect']
res.interact_chipres(hs, cx, fnum=fnum)
fig = df2.gcf()
fig.canvas.draw()
df2.bring_to_front(fig)
def _clicked_none():
# Toggle if the click is not in any axis
printDBG('clicked none')
#print(kwargs)
_show_res(hs, res, annote=(annote + 1) % 3, **kwargs)
fig.canvas.draw()
def _on_res_click(event):
'result interaction mpl event callback slot'
print('[viz] clicked result')
if event.xdata is None or event.inaxes is None:
#print('clicked outside axes')
return _clicked_none()
ax = event.inaxes
hs_viewtype = ax.__dict__.get('_hs_viewtype', '')
printDBG(event.__dict__)
printDBG('hs_viewtype=%r' % hs_viewtype)
# Clicked a specific chipres
if hs_viewtype.find('chipres') == 0:
cx = ax.__dict__.get('_hs_cx')
# Ctrl-Click
key = '' if event.key is None else event.key
print('key = %r' % key)
if key.find('control') == 0:
print('[viz] result control clicked')
return _ctrlclicked_cx(cx)
# Left-Click
else:
print('[viz] result clicked')
return _clicked_cx(cx)
df2.connect_callback(fig, 'button_press_event', _on_res_click)
df2.adjust_subplots_safe()
printDBG('[viz._show_res()] Finished')
return fig
def show_descriptors_match_distances(orgres2_distance,
fnum=1,
db_name='',
**kwargs):
disttype_list = orgres2_distance.itervalues().next().keys()
orgtype_list = orgres2_distance.keys()
(nRow, nCol) = len(orgtype_list), len(disttype_list)
nColors = nRow * nCol
color_list = df2.distinct_colors(nColors)
df2.figure(fnum=fnum, docla=True, doclf=True)
pnum_ = lambda px: (nRow, nCol, px + 1)
plot_type = helpers.get_arg('--plot-type', default='plot')
# Remember min and max val for each distance type (l1, emd...)
distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list}
distkey2_max = {distkey: 0 for distkey in disttype_list}
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label)
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper right')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
px = 0
for orgkey in orgtype_list:
for distkey in disttype_list:
dists = orgres2_distance[orgkey][distkey]
if len(dists) == 0:
continue
min_ = dists.min()
max_ = dists.max()
distkey2_min[distkey] = min(distkey2_min[distkey], min_)
distkey2_max[distkey] = max(distkey2_max[distkey], max_)
for orgkey in orgtype_list:
for distkey in disttype_list:
print(((orgkey, distkey)))
dists = orgres2_distance[orgkey][distkey]
df2.figure(fnum=fnum, pnum=pnum_(px))
color = color_list[px]
title = distkey + ' ' + orgkey
label = 'P(%s | %s)' % (distkey, orgkey)
_distplot(dists, color, label, distkey, **kwargs)
#ax = df2.gca()
#ax.set_title(title)
px += 1
subtitle = 'the matching distances between sift descriptors'
title = '(sift) matching distances'
if db_name != '':
title = db_name + ' ' + title
df2.set_figtitle(title, subtitle)
df2.adjust_subplots_safe()
def draw():
df2.adjust_subplots_safe()
df2.draw()
def show_nearest_descriptors(hs, qcx, qfx, fnum=None):
if fnum is None:
fnum = df2.next_fnum()
# Inspect the nearest neighbors of a descriptor
dx2_cx = hs.qdat._data_index.ax2_cx
dx2_fx = hs.qdat._data_index.ax2_fx
K = hs.qdat.cfg.nn_cfg.K
Knorm = hs.qdat.cfg.nn_cfg.Knorm
checks = hs.qdat.cfg.nn_cfg.checks
flann = hs.qdat._data_index.flann
qfx2_desc = hs.get_desc(qcx)[qfx:qfx + 1]
try:
(qfx2_dx, qfx2_dist) = flann.nn_index(qfx2_desc,
K + Knorm,
checks=checks)
qfx2_cx = dx2_cx[qfx2_dx]
qfx2_fx = dx2_fx[qfx2_dx]
def get_extract_tuple(cx, fx, k=-1):
rchip = hs.get_chip(cx)
kp = hs.get_kpts(cx)[fx]
sift = hs.get_desc(cx)[fx]
if k == -1:
info = '\nquery %s, fx=%r' % (hs.cidstr(cx), fx)
type_ = 'query'
elif k < K:
type_ = 'match'
info = '\nmatch %s, fx=%r k=%r, dist=%r' % (hs.cidstr(cx), fx,
k, qfx2_dist[0, k])
elif k < Knorm + K:
type_ = 'norm'
info = '\nnorm %s, fx=%r k=%r, dist=%r' % (hs.cidstr(cx), fx,
k, qfx2_dist[0, k])
else:
raise Exception('[viz] problem k=%r')
return (rchip, kp, sift, fx, cx, info, type_)
extracted_list = []
extracted_list.append(get_extract_tuple(qcx, qfx, -1))
for k in xrange(K + Knorm):
tup = get_extract_tuple(qfx2_cx[0, k], qfx2_fx[0, k], k)
extracted_list.append(tup)
#print('[viz] K + Knorm = %r' % (K + Knorm))
# Draw the _select_ith_match plot
nRows, nCols = len(extracted_list), 3
# Draw selected feature matches
prevsift = None
df2.figure(fnum=fnum, docla=True, doclf=True)
px = 0 # plot offset
for (rchip, kp, sift, fx, cx, info, type_) in extracted_list:
print('[viz] ' + info.replace('\n', ''))
px = draw_feat_row(rchip,
fx,
kp,
sift,
fnum,
nRows,
nCols,
px,
prevsift=prevsift,
cx=cx,
info=info,
type_=type_)
prevsift = sift
df2.adjust_subplots_safe(hspace=1)
except Exception as ex:
print('[viz] Error in show nearest descriptors')
print(ex)
raise
def _show_res(hs, res, **kwargs):
''' Displays query chip, groundtruth matches, and top 5 matches'''
#printDBG('[viz._show_res()] %s ' % helpers.printableVal(locals()))
#printDBG = print
in_image = hs.prefs.display_cfg.show_results_in_image
annote = kwargs.pop('annote', 2) # this is toggled
fnum = kwargs.get('fnum', 3)
figtitle = kwargs.get('figtitle', '')
topN_cxs = kwargs.get('topN_cxs', [])
gt_cxs = kwargs.get('gt_cxs', [])
all_kpts = kwargs.get('all_kpts', False)
interact = kwargs.get('interact', True)
show_query = kwargs.get('show_query', False)
dosquare = kwargs.get('dosquare', False)
if SHOW_QUERY_OVERRIDE is not None:
show_query = SHOW_QUERY_OVERRIDE
max_nCols = 5
if len(topN_cxs) in [6, 7]:
max_nCols = 3
if len(topN_cxs) in [8]:
max_nCols = 4
printDBG('========================')
printDBG('[viz._show_res()]----------------')
all_gts = hs.get_other_indexed_cxs(res.qcx)
_tup = tuple(map(len, (topN_cxs, gt_cxs, all_gts)))
printDBG('[viz._show_res()] #topN=%r #missed_gts=%r/%r' % _tup)
printDBG('[viz._show_res()] * fnum=%r' % (fnum,))
printDBG('[viz._show_res()] * figtitle=%r' % (figtitle,))
printDBG('[viz._show_res()] * max_nCols=%r' % (max_nCols,))
printDBG('[viz._show_res()] * show_query=%r' % (show_query,))
ranked_cxs = res.topN_cxs(hs, N='all')
# Build a subplot grid
nQuerySubplts = 1 if show_query else 0
nGtSubplts = nQuerySubplts + (0 if gt_cxs is None else len(gt_cxs))
nTopNSubplts = 0 if topN_cxs is None else len(topN_cxs)
nTopNCols = min(max_nCols, nTopNSubplts)
nGTCols = min(max_nCols, nGtSubplts)
nGTCols = max(nGTCols, nTopNCols)
nTopNCols = nGTCols
nGtRows = 0 if nGTCols == 0 else int(np.ceil(nGtSubplts / nGTCols))
nTopNRows = 0 if nTopNCols == 0 else int(np.ceil(nTopNSubplts / nTopNCols))
nGtCells = nGtRows * nGTCols
nRows = nTopNRows + nGtRows
# HACK:
_color_list = df2.distinct_colors(len(topN_cxs))
cx2_color = {cx: _color_list[ox] for ox, cx in enumerate(topN_cxs)}
if IN_IMAGE_OVERRIDE is not None:
in_image = IN_IMAGE_OVERRIDE
# Helpers
def _show_query_fn(plotx_shift, rowcols):
'helper for viz._show_res'
plotx = plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
#print('[viz] Plotting Query: pnum=%r' % (pnum,))
_kwshow = dict(draw_kpts=annote)
_kwshow.update(kwargs)
_kwshow['prefix'] = 'q'
_kwshow['res'] = res
_kwshow['pnum'] = pnum
_kwshow['cx2_color'] = cx2_color
_kwshow['draw_ell'] = annote >= 1
#_kwshow['in_image'] = in_image
show_chip(hs, **_kwshow)
#if in_image:
#roi1 = hs.cx2_roi(res.qcx)
#df2.draw_roi(roi1, bbox_color=df2.ORANGE, label='q' + hs.cidstr(res.qcx))
def _plot_matches_cxs(cx_list, plotx_shift, rowcols):
def _show_matches_fn(cx, orank, pnum):
'Helper function for drawing matches to one cx'
aug = 'rank=%r\n' % orank
#printDBG('[viz._show_res()] plotting: %r' % (pnum,))
_kwshow = dict(draw_ell=annote, draw_pts=False, draw_lines=annote,
ell_alpha=.5, all_kpts=all_kpts)
_kwshow.update(kwargs)
_kwshow['fnum'] = fnum
_kwshow['pnum'] = pnum
_kwshow['title_aug'] = aug
# If we already are showing the query dont show it here
if not show_query:
_kwshow['draw_ell'] = annote == 1
_kwshow['draw_lines'] = annote >= 1
res_show_chipres(res, hs, cx, in_image=in_image, **_kwshow)
else:
_kwshow['draw_ell'] = annote >= 1
if annote == 2:
# TODO Find a better name
_kwshow['color'] = cx2_color[cx]
_kwshow['sel_fx2'] = res.cx2_fm[cx][:, 1]
show_chip(hs, cx, in_image=in_image, **_kwshow)
annotate_chipres(hs, res, cx, in_image=in_image, show_query=not show_query)
'helper for viz._show_res to draw many cxs'
#printDBG('[viz._show_res()] Plotting Chips %s:' % hs.cidstr(cx_list))
if cx_list is None:
return
for ox, cx in enumerate(cx_list):
plotx = ox + plotx_shift + 1
pnum = (rowcols[0], rowcols[1], plotx)
oranks = np.where(ranked_cxs == cx)[0]
if len(oranks) == 0:
orank = -1
continue
orank = oranks[0] + 1
_show_matches_fn(cx, orank, pnum)
if dosquare:
# HACK
nSubplots = nGtSubplts + nTopNSubplts
nRows, nCols = get_square_row_cols(nSubplots, 3)
nTopNCols = nGTCols = nCols
shift_topN = 1
printDBG('nRows, nCols = (%r, %r)' % (nRows, nCols))
else:
shift_topN = nGtCells
if nGtSubplts == 1:
nGTCols = 1
fig = df2.figure(fnum=fnum, pnum=(nRows, nGTCols, 1), docla=True, doclf=True)
df2.disconnect_callback(fig, 'button_press_event')
df2.plt.subplot(nRows, nGTCols, 1)
# Plot Query
if show_query:
_show_query_fn(0, (nRows, nGTCols))
# Plot Ground Truth
_plot_matches_cxs(gt_cxs, nQuerySubplts, (nRows, nGTCols))
_plot_matches_cxs(topN_cxs, shift_topN, (nRows, nTopNCols))
figtitle += ' q%s name=%s' % (hs.cidstr(res.qcx), hs.cx2_name(res.qcx))
df2.set_figtitle(figtitle, incanvas=not NO_LABEL_OVERRIDE)
# Result Interaction
if interact:
printDBG('[viz._show_res()] starting interaction')
def _ctrlclicked_cx(cx):
printDBG('ctrl+clicked cx=%r' % cx)
fnum = FNUMS['special']
fig = df2.figure(fnum=fnum, docla=True, doclf=True)
df2.disconnect_callback(fig, 'button_press_event')
viz_spatial_verification(hs, res.qcx, cx2=cx, fnum=fnum)
fig.canvas.draw()
df2.bring_to_front(fig)
def _clicked_cx(cx):
printDBG('clicked cx=%r' % cx)
fnum = FNUMS['inspect']
res.interact_chipres(hs, cx, fnum=fnum)
fig = df2.gcf()
fig.canvas.draw()
df2.bring_to_front(fig)
def _clicked_none():
# Toggle if the click is not in any axis
printDBG('clicked none')
#print(kwargs)
_show_res(hs, res, annote=(annote + 1) % 3, **kwargs)
fig.canvas.draw()
def _on_res_click(event):
'result interaction mpl event callback slot'
print('[viz] clicked result')
if event.xdata is None or event.inaxes is None:
#print('clicked outside axes')
return _clicked_none()
ax = event.inaxes
hs_viewtype = ax.__dict__.get('_hs_viewtype', '')
printDBG(event.__dict__)
printDBG('hs_viewtype=%r' % hs_viewtype)
# Clicked a specific chipres
if hs_viewtype.find('chipres') == 0:
cx = ax.__dict__.get('_hs_cx')
# Ctrl-Click
key = '' if event.key is None else event.key
print('key = %r' % key)
if key.find('control') == 0:
print('[viz] result control clicked')
return _ctrlclicked_cx(cx)
# Left-Click
else:
print('[viz] result clicked')
return _clicked_cx(cx)
df2.connect_callback(fig, 'button_press_event', _on_res_click)
df2.adjust_subplots_safe()
printDBG('[viz._show_res()] Finished')
return fig
def show_descriptors_match_distances(orgres2_distance, fnum=1, db_name='', **kwargs):
disttype_list = orgres2_distance.itervalues().next().keys()
orgtype_list = orgres2_distance.keys()
(nRow, nCol) = len(orgtype_list), len(disttype_list)
nColors = nRow * nCol
color_list = df2.distinct_colors(nColors)
df2.figure(fnum=fnum, docla=True, doclf=True)
pnum_ = lambda px: (nRow, nCol, px + 1)
plot_type = helpers.get_arg('--plot-type', default='plot')
# Remember min and max val for each distance type (l1, emd...)
distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list}
distkey2_max = {distkey: 0 for distkey in disttype_list}
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label)
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper right')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
px = 0
for orgkey in orgtype_list:
for distkey in disttype_list:
dists = orgres2_distance[orgkey][distkey]
if len(dists) == 0:
continue
min_ = dists.min()
max_ = dists.max()
distkey2_min[distkey] = min(distkey2_min[distkey], min_)
distkey2_max[distkey] = max(distkey2_max[distkey], max_)
for orgkey in orgtype_list:
for distkey in disttype_list:
print(((orgkey, distkey)))
dists = orgres2_distance[orgkey][distkey]
df2.figure(fnum=fnum, pnum=pnum_(px))
color = color_list[px]
title = distkey + ' ' + orgkey
label = 'P(%s | %s)' % (distkey, orgkey)
_distplot(dists, color, label, distkey, **kwargs)
#ax = df2.gca()
#ax.set_title(title)
px += 1
subtitle = 'the matching distances between sift descriptors'
title = '(sift) matching distances'
if db_name != '':
title = db_name + ' ' + title
df2.set_figtitle(title, subtitle)
df2.adjust_subplots_safe()
def draw():
df2.adjust_subplots_safe()
df2.draw()
