def show_name(ibs, nid, nid2_aids=None, in_image=True, fnum=0, sel_aids=[], subtitle='',
annote=False, **kwargs):
print('[viz] show_name nid=%r' % nid)
aid_list = ibs.get_name_aids(nid)
name = ibs.get_name_text((nid,))
ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote)
print('[viz] show_name=%r aid_list=%r' % (name, aid_list))
nAids = len(aid_list)
if nAids > 0:
nRows, nCols = ph.get_square_row_cols(nAids)
print('[viz*] r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = df2.get_pnum_func(nRows, nCols)
fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
# Trigger computation of all chips in parallel
for px, aid in enumerate(aid_list):
show_chip(ibs, aid=aid, pnum=pnum_(px), annote=annote, in_image=in_image)
if aid in sel_aids:
ax = df2.gca()
df2.draw_border(ax, df2.GREEN, 4)
#plot_aid3(ibs, aid)
if isinstance(nid, np.ndarray):
nid = nid[0]
if isinstance(name, np.ndarray):
name = name[0]
else:
df2.imshow_null(fnum=fnum, **kwargs)
figtitle = 'Name View nid=%r name=%r' % (nid, name)
df2.set_figtitle(figtitle)
def annotate_matches(ibs, qres, aid2,
offset1=(0, 0),
offset2=(0, 0),
xywh2=(0, 0, 0, 0),
xywh1=(0, 0, 0, 0),
**kwargs):
# TODO Use this function when you clean show_matches
in_image = kwargs.get('in_image', False)
show_query = kwargs.get('show_query', True)
draw_border = kwargs.get('draw_border', True)
draw_lbl = kwargs.get('draw_lbl', True)
printDBG('[viz] annotate_matches()')
aid1 = qres.qaid
truth = ibs.get_match_truth(aid1, aid2)
truth_color = vh.get_truth_color(truth)
# Build title
title = vh.get_query_text(ibs, qres, aid2, truth, **kwargs)
# Build xlbl
ax = df2.gca()
ph.set_plotdat(ax, 'viztype', 'matches')
ph.set_plotdat(ax, 'qaid', aid1)
ph.set_plotdat(ax, 'aid1', aid1)
ph.set_plotdat(ax, 'aid2', aid2)
if draw_lbl:
name1, name2 = ibs.get_annot_names([aid1, aid2])
lbl1 = repr(name1) + ' : ' + 'q' + vh.get_aidstrs(aid1)
lbl2 = repr(name2) + ' : ' + vh.get_aidstrs(aid2)
else:
lbl1, lbl2 = None, None
if vh.NO_LBL_OVERRIDE:
title = ''
df2.set_title(title, ax)
# Plot annotations over images
if in_image:
bbox1, bbox2 = vh.get_bboxes(ibs, [aid1, aid2], [offset1, offset2])
theta1, theta2 = ibs.get_annot_thetas([aid1, aid2])
# HACK!
if show_query:
df2.draw_bbox(bbox1, bbox_color=df2.ORANGE, lbl=lbl1, theta=theta1)
bbox_color2 = truth_color if draw_border else df2.ORANGE
df2.draw_bbox(bbox2, bbox_color=bbox_color2, lbl=lbl2, theta=theta2)
else:
xy, w, h = df2._axis_xy_width_height(ax)
bbox2 = (xy[0], xy[1], w, h)
theta2 = 0
if draw_border:
df2.draw_border(ax, truth_color, 4, offset=offset2)
if draw_lbl:
# Custom user lbl for chips 1 and 2
(x1, y1, w1, h1) = xywh1
(x2, y2, w2, h2) = xywh2
df2.absolute_lbl(x1 + w1, y1, lbl1)
df2.absolute_lbl(x2 + w2, y2, lbl2)
# No matches draw a red box
if aid2 not in qres.aid2_fm or len(qres.aid2_fm[aid2]) == 0:
if draw_border:
df2.draw_boxedX(bbox2, theta=theta2)
def show_multiple_chips(ibs,
aid_list,
in_image=True,
fnum=0,
sel_aids=[],
subtitle='',
annote=False,
**kwargs):
"""
CommandLine:
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6435,9861,137,6563,9167,12547,9332,12598,13285 --no-inimage --notitle
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=137,6563,12547,9332,12598,13285 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6563,9332,13285,12598 --no-inimage --notitle --adjust=.05 --rc=1,4
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=1288 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=4020,4839 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=6524,6540,6571,6751 --no-inimage --notitle --adjust=.05 --diskshow
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST -a default:index=0:4 --show
--aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2 --doboth --show --notitle --trydrawline
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2,3,4,5 --doboth --rc=2,5 --show --chrlbl --trydrawline --qualtitle --no-figtitle --notitle
--doboth
--doboth --show
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=15419 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_name import * # NOQA
>>> import ibeis
>>> ibs, aid_list, in_image = testdata_multichips()
>>> if True:
>>> import matplotlib as mpl
>>> from ibeis.scripts.thesis import TMP_RC
>>> mpl.rcParams.update(TMP_RC)
>>> fnum = 0
>>> sel_aids = []
>>> subtitle = ''
>>> annote = False
>>> fig = show_multiple_chips(ibs, aid_list, in_image, fnum, sel_aids, subtitle, annote)
>>> ut.quit_if_noshow()
>>> fig.canvas.draw()
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
nAids = len(aid_list)
if nAids == 0:
fig = df2.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs)
df2.imshow_null(fnum=fnum, **kwargs)
return fig
# Trigger computation of all chips in parallel
ibsfuncs.ensure_annotation_data(ibs,
aid_list,
chips=(not in_image or annote),
feats=annote)
print('[viz_name] * annot_vuuid=%r' %
((ibs.get_annot_visual_uuids(aid_list), )))
print('[viz_name] * aid_list=%r' % ((aid_list, )))
DOBOTH = ut.get_argflag('--doboth')
rc = ut.get_argval('--rc', type_=list, default=None)
if rc is None:
nRows, nCols = ph.get_square_row_cols(nAids * (2 if DOBOTH else 1))
else:
nRows, nCols = rc
notitle = ut.get_argflag('--notitle')
draw_lbls = not ut.get_argflag('--no-draw_lbls')
show_chip_kw = dict(annote=annote,
in_image=in_image,
notitle=notitle,
draw_lbls=draw_lbls)
#print('[viz_name] * r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = df2.get_pnum_func(nRows, nCols)
fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
ax_list1 = []
for px, aid in enumerate(aid_list):
print('px = %r' % (px, ))
_fig, _ax1 = viz_chip.show_chip(ibs,
aid=aid,
pnum=pnum_(px),
**show_chip_kw)
print('other_aids = %r' % (ibs.get_annot_contact_aids(aid), ))
ax = df2.gca()
ax_list1.append(_ax1)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if ut.get_argflag('--chrlbl') and not DOBOTH:
ax.set_xlabel('(' + chr(ord('a') - 1 + px) + ')')
elif ut.get_argflag('--numlbl') and not DOBOTH:
ax.set_xlabel('(' + str(px + 1) + ')')
#plot_aid3(ibs, aid)
# HACK to show in image and not in image
if DOBOTH:
#ut.embed()
#ph.get_plotdat_dict(ax_list1[1])
#ph.get_plotdat_dict(ax_list2[1])
ax_list2 = []
show_chip_kw['in_image'] = not show_chip_kw['in_image']
start = px + 1
for px, aid in enumerate(aid_list, start=start):
_fig, _ax2 = viz_chip.show_chip(ibs,
aid=aid,
pnum=pnum_(px),
**show_chip_kw)
ax = df2.gca()
ax_list2.append(_ax2)
if ut.get_argflag('--chrlbl'):
ax.set_xlabel('(' + chr(ord('a') - start + px) + ')')
elif ut.get_argflag('--numlbl'):
ax.set_xlabel('(' + str(px - start + 1) + ')')
if ut.get_argflag('--qualtitle'):
qualtext = ibs.get_annot_quality_texts(aid)
ax.set_title(qualtext)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if in_image:
ax_list1, ax_list2 = ax_list2, ax_list1
if ut.get_argflag('--trydrawline'):
# Unfinished
#ut.embed()
# Draw lines between corresponding axes
# References:
# http://stackoverflow.com/questions/17543359/drawing-lines-between-two-plots-in-matplotlib
import matplotlib as mpl
import vtool as vt
# !!!
#http://matplotlib.org/users/transforms_tutorial.html
#invTransFigure_fn1 = fig.transFigure.inverted().transform
#invTransFigure_fn2 = fig.transFigure.inverted().transform
#print(ax_list1)
#print(ax_list2)
assert len(ax_list1) == len(ax_list2)
for ax1, ax2 in zip(ax_list1, ax_list2):
#_ = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _.width * fig.dpi, _.height * fig.dpi)
# returns in figure coordinates
#bbox1 = df2.get_axis_bbox(ax=ax1)
#if bbox1[-1] < 0:
# # Weird bug
# bbox1 = bbox1[1]
print('--')
print('ax1 = %r' % (ax1, ))
print('ax2 = %r' % (ax2, ))
chipshape = ph.get_plotdat(ax1, 'chipshape')
#_bbox1 = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _bbox1.width * fig.dpi, _bbox1.height * fig.dpi)
bbox1 = (0, 0, chipshape[1], chipshape[0])
aid_ = ph.get_plotdat(ax2, 'aid')
aid_list_ = ph.get_plotdat(ax2, 'aid_list')
index = aid_list_.index(aid_)
annotation_bbox_list = ph.get_plotdat(ax2,
'annotation_bbox_list')
bbox2 = annotation_bbox_list[index]
print('bbox1 = %r' % (bbox1, ))
print('bbox2 = %r' % (bbox2, ))
vert_list1 = np.array(vt.verts_from_bbox(bbox1))
vert_list2 = np.array(vt.verts_from_bbox(bbox2))
print('vert_list1 = %r' % (vert_list1, ))
print('vert_list2 = %r' % (vert_list2, ))
#for vx in [0, 1, 2, 3]:
for vx in [0, 1]:
vert1 = vert_list1[vx].tolist()
vert2 = vert_list2[vx].tolist()
print(' ***')
print(' * vert1 = %r' % (vert1, ))
print(' * vert2 = %r' % (vert2, ))
coordsA = coordsB = 'data'
#coords = 'axes points'
#'axes fraction'
#'axes pixels'
#coordsA = 'axes pixels'
#coordsB = 'data'
#'figure fraction'
#'figure pixels'
#'figure pixels'
#'figure points'
#'polar'
#'offset points'
con = mpl.patches.ConnectionPatch(xyA=vert1,
xyB=vert2,
coordsA=coordsA,
coordsB=coordsB,
axesA=ax1,
axesB=ax2,
linewidth=1,
color='k')
#, arrowstyle="-")
#ut.embed()
#con.set_zorder(None)
ax1.add_artist(con)
#ax2.add_artist(con)
#ut.embed()
#verts2.T[1] -= bbox2[-1]
#bottom_left1, bottom_right1 = verts1[1:3].tolist()
#bottom_left2, bottom_right2 = verts2[1:3].tolist()
##transAxes1 = ax1.transData.inverted()
#transAxes1_fn = ax1.transData.transform
#transAxes2_fn = ax2.transData.transform
#transAxes1_fn = ut.identity
#transAxes2_fn = ut.identity
#coord_bl1 = transFigure.transform(transAxes1.transform(bottom_left1))
#coord_br1 = transFigure.transform(transAxes1.transform(bottom_right1))
#coord_bl1 = invTransFigure_fn1(transAxes1_fn(bottom_left1))
#print('bottom_left2 = %r' % (bottom_left2,))
#coord_bl1 = (5, 5)
#coord_bl2 = invTransFigure_fn2(transAxes2_fn(bottom_left2))
#print('coord_bl2 = %r' % (coord_bl2,))
#coord_br1 = invTransFigure_fn1(transAxes1_fn(bottom_right1))
#coord_br2 = invTransFigure_fn2(transAxes2_fn(bottom_right2))
##print('coord_bl1 = %r' % (coord_bl1,))
#line_coords1 = np.vstack([coord_bl1, coord_bl2])
#line_coords2 = np.vstack([coord_br1, coord_br2])
#print('line_coords1 = %r' % (line_coords1,))
#line1 = mpl.lines.Line2D((line_coords1[0]), (line_coords1[1]), transform=fig.transFigure)
#line2 = mpl.lines.Line2D((line_coords2[0]), (line_coords2[1]), transform=fig.transFigure)
#xs1, ys1 = line_coords1.T
#xs2, ys2 = line_coords2.T
#linekw = dict(transform=fig.transFigure)
#linekw = dict()
#print('xs1 = %r' % (xs1,))
#print('ys1 = %r' % (ys1,))
#line1 = mpl.lines.Line2D(xs1, ys1, **linekw)
#line2 = mpl.lines.Line2D(xs2, ys2, **linekw) # NOQA
#shrinkA=5, shrinkB=5, mutation_scale=20, fc="w")
#ax2.add_artist(con)
#fig.lines.append(line1)
#fig.lines.append(line2)
pass
return fig
def show_multiple_chips(ibs, aid_list, in_image=True, fnum=0, sel_aids=[],
subtitle='', annote=False, **kwargs):
"""
CommandLine:
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6435,9861,137,6563,9167,12547,9332,12598,13285 --no-inimage --notitle
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=137,6563,12547,9332,12598,13285 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db NNP_Master3 --aids=6563,9332,13285,12598 --no-inimage --notitle --adjust=.05 --rc=1,4
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=1288 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --show --db PZ_Master0 --aids=4020,4839 --no-inimage --notitle --adjust=.05
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=6524,6540,6571,6751 --no-inimage --notitle --adjust=.05 --diskshow
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST -a default:index=0:4 --show
--aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --no-inimage
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2 --doboth --show --notitle --trydrawline
python -m ibeis.viz.viz_name --test-show_multiple_chips --db PZ_MTEST --aids=1,2,3,4,5 --doboth --rc=2,5 --show --chrlbl --trydrawline --qualtitle --no-figtitle --notitle
--doboth
--doboth --show
python -m ibeis.viz.viz_name --test-show_multiple_chips --db NNP_Master3 --aids=15419 --doboth --rc=2,1 --show --notitle --trydrawline --no-draw_lbls
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_name import * # NOQA
>>> import ibeis
>>> ibs, aid_list, in_image = testdata_multichips()
>>> fnum = 0
>>> sel_aids = []
>>> subtitle = ''
>>> annote = False
>>> fig = show_multiple_chips(ibs, aid_list, in_image, fnum, sel_aids, subtitle, annote)
>>> ut.quit_if_noshow()
>>> fig.canvas.draw()
>>> ut.show_if_requested()
"""
fnum = pt.ensure_fnum(fnum)
nAids = len(aid_list)
if nAids == 0:
fig = df2.figure(fnum=fnum, pnum=(1, 1, 1), **kwargs)
df2.imshow_null(fnum=fnum, **kwargs)
return fig
# Trigger computation of all chips in parallel
ibsfuncs.ensure_annotation_data(ibs, aid_list, chips=(not in_image or annote), feats=annote)
print('[viz_name] * annot_vuuid=%r' % ((ibs.get_annot_visual_uuids(aid_list),)))
print('[viz_name] * aid_list=%r' % ((aid_list,)))
DOBOTH = ut.get_argflag('--doboth')
rc = ut.get_argval('--rc', type_=list, default=None)
if rc is None:
nRows, nCols = ph.get_square_row_cols(nAids * (2 if DOBOTH else 1))
else:
nRows, nCols = rc
notitle = ut.get_argflag('--notitle')
draw_lbls = not ut.get_argflag('--no-draw_lbls')
show_chip_kw = dict(annote=annote, in_image=in_image, notitle=notitle, draw_lbls=draw_lbls)
#print('[viz_name] * r=%r, c=%r' % (nRows, nCols))
#gs2 = gridspec.GridSpec(nRows, nCols)
pnum_ = df2.get_pnum_func(nRows, nCols)
fig = df2.figure(fnum=fnum, pnum=pnum_(0), **kwargs)
fig.clf()
ax_list1 = []
for px, aid in enumerate(aid_list):
print('px = %r' % (px,))
_fig, _ax1 = viz_chip.show_chip(ibs, aid=aid, pnum=pnum_(px), **show_chip_kw)
print('other_aids = %r' % (ibs.get_annot_contact_aids(aid),))
ax = df2.gca()
ax_list1.append(_ax1)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if ut.get_argflag('--chrlbl') and not DOBOTH:
ax.set_xlabel('(' + chr(ord('a') - 1 + px) + ')')
elif ut.get_argflag('--numlbl') and not DOBOTH:
ax.set_xlabel('(' + str(px + 1) + ')')
#plot_aid3(ibs, aid)
# HACK to show in image and not in image
if DOBOTH:
#ut.embed()
#ph.get_plotdat_dict(ax_list1[1])
#ph.get_plotdat_dict(ax_list2[1])
ax_list2 = []
show_chip_kw['in_image'] = not show_chip_kw['in_image']
start = px + 1
for px, aid in enumerate(aid_list, start=start):
_fig, _ax2 = viz_chip.show_chip(ibs, aid=aid, pnum=pnum_(px), **show_chip_kw)
ax = df2.gca()
ax_list2.append(_ax2)
if ut.get_argflag('--chrlbl'):
ax.set_xlabel('(' + chr(ord('a') - start + px) + ')')
elif ut.get_argflag('--numlbl'):
ax.set_xlabel('(' + str(px - start + 1) + ')')
if ut.get_argflag('--qualtitle'):
qualtext = ibs.get_annot_quality_texts(aid)
ax.set_title(qualtext)
if aid in sel_aids:
df2.draw_border(ax, df2.GREEN, 4)
if in_image:
ax_list1, ax_list2 = ax_list2, ax_list1
if ut.get_argflag('--trydrawline'):
# Unfinished
#ut.embed()
# Draw lines between corresponding axes
# References:
# http://stackoverflow.com/questions/17543359/drawing-lines-between-two-plots-in-matplotlib
import matplotlib as mpl
import vtool as vt
# !!!
#http://matplotlib.org/users/transforms_tutorial.html
#invTransFigure_fn1 = fig.transFigure.inverted().transform
#invTransFigure_fn2 = fig.transFigure.inverted().transform
#print(ax_list1)
#print(ax_list2)
assert len(ax_list1) == len(ax_list2)
for ax1, ax2 in zip(ax_list1, ax_list2):
#_ = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _.width * fig.dpi, _.height * fig.dpi)
# returns in figure coordinates
#bbox1 = df2.get_axis_bbox(ax=ax1)
#if bbox1[-1] < 0:
# # Weird bug
# bbox1 = bbox1[1]
print('--')
print('ax1 = %r' % (ax1,))
print('ax2 = %r' % (ax2,))
chipshape = ph.get_plotdat(ax1, 'chipshape')
#_bbox1 = ax1.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
#bbox1 = (0, 0, _bbox1.width * fig.dpi, _bbox1.height * fig.dpi)
bbox1 = (0, 0, chipshape[1], chipshape[0])
aid_ = ph.get_plotdat(ax2, 'aid')
aid_list_ = ph.get_plotdat(ax2, 'aid_list')
index = aid_list_.index(aid_)
annotation_bbox_list = ph.get_plotdat(ax2, 'annotation_bbox_list')
bbox2 = annotation_bbox_list[index]
print('bbox1 = %r' % (bbox1,))
print('bbox2 = %r' % (bbox2,))
vert_list1 = np.array(vt.verts_from_bbox(bbox1))
vert_list2 = np.array(vt.verts_from_bbox(bbox2))
print('vert_list1 = %r' % (vert_list1,))
print('vert_list2 = %r' % (vert_list2,))
#for vx in [0, 1, 2, 3]:
for vx in [0, 1]:
vert1 = vert_list1[vx].tolist()
vert2 = vert_list2[vx].tolist()
print(' ***')
print(' * vert1 = %r' % (vert1,))
print(' * vert2 = %r' % (vert2,))
coordsA = coordsB = 'data'
#coords = 'axes points'
#'axes fraction'
#'axes pixels'
#coordsA = 'axes pixels'
#coordsB = 'data'
#'figure fraction'
#'figure pixels'
#'figure pixels'
#'figure points'
#'polar'
#'offset points'
con = mpl.patches.ConnectionPatch(
xyA=vert1, xyB=vert2, coordsA=coordsA,
coordsB=coordsB,
axesA=ax1, axesB=ax2,
linewidth=1, color='k')
#, arrowstyle="-")
#ut.embed()
#con.set_zorder(None)
ax1.add_artist(con)
#ax2.add_artist(con)
#ut.embed()
#verts2.T[1] -= bbox2[-1]
#bottom_left1, bottom_right1 = verts1[1:3].tolist()
#bottom_left2, bottom_right2 = verts2[1:3].tolist()
##transAxes1 = ax1.transData.inverted()
#transAxes1_fn = ax1.transData.transform
#transAxes2_fn = ax2.transData.transform
#transAxes1_fn = ut.identity
#transAxes2_fn = ut.identity
#coord_bl1 = transFigure.transform(transAxes1.transform(bottom_left1))
#coord_br1 = transFigure.transform(transAxes1.transform(bottom_right1))
#coord_bl1 = invTransFigure_fn1(transAxes1_fn(bottom_left1))
#print('bottom_left2 = %r' % (bottom_left2,))
#coord_bl1 = (5, 5)
#coord_bl2 = invTransFigure_fn2(transAxes2_fn(bottom_left2))
#print('coord_bl2 = %r' % (coord_bl2,))
#coord_br1 = invTransFigure_fn1(transAxes1_fn(bottom_right1))
#coord_br2 = invTransFigure_fn2(transAxes2_fn(bottom_right2))
##print('coord_bl1 = %r' % (coord_bl1,))
#line_coords1 = np.vstack([coord_bl1, coord_bl2])
#line_coords2 = np.vstack([coord_br1, coord_br2])
#print('line_coords1 = %r' % (line_coords1,))
#line1 = mpl.lines.Line2D((line_coords1[0]), (line_coords1[1]), transform=fig.transFigure)
#line2 = mpl.lines.Line2D((line_coords2[0]), (line_coords2[1]), transform=fig.transFigure)
#xs1, ys1 = line_coords1.T
#xs2, ys2 = line_coords2.T
#linekw = dict(transform=fig.transFigure)
#linekw = dict()
#print('xs1 = %r' % (xs1,))
#print('ys1 = %r' % (ys1,))
#line1 = mpl.lines.Line2D(xs1, ys1, **linekw)
#line2 = mpl.lines.Line2D(xs2, ys2, **linekw) # NOQA
#shrinkA=5, shrinkB=5, mutation_scale=20, fc="w")
#ax2.add_artist(con)
#fig.lines.append(line1)
#fig.lines.append(line2)
pass
return fig
def draw_feat_row(
chip,
fx,
kp,
sift,
fnum,
nRows,
nCols=None,
px=None,
prevsift=None,
origsift=None,
aid=None,
info="",
type_=None,
shape_labels=False,
vecfield=False,
multicolored_arms=False,
draw_chip=False,
draw_warped=True,
draw_unwarped=True,
draw_desc=True,
rect=True,
ori=True,
pts=False,
**kwargs
):
"""
draw_feat_row
SeeAlso:
ibeis.viz.viz_nearest_descriptors
~/code/ibeis/ibeis/viz/viz_nearest_descriptors.py
CommandLine:
# Use this to find the fx you want to visualize
python -m plottool.interact_keypoints --test-ishow_keypoints --show --fname zebra.png
# Use this to visualize the featrow
python -m plottool.viz_featrow --test-draw_feat_row --show
python -m plottool.viz_featrow --test-draw_feat_row --show --fname zebra.png --fx=121 --feat-all --no-sift
python -m plottool.viz_featrow --test-draw_feat_row --dpath figures --save ~/latex/crall-candidacy-2015/figures/viz_featrow.jpg
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.viz_featrow import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> kpts, vecs, imgBGR = pt.viz_keypoints.testdata_kpts()
>>> chip = imgBGR
>>> print('There are %d features' % (len(vecs)))
>>> fx = ut.get_argval('--fx', type_=int, default=0)
>>> kp = kpts[fx]
>>> sift = vecs[fx]
>>> fnum = 1
>>> nRows = 1
>>> nCols = 2
>>> px = 0
>>> hack = ut.get_argflag('--feat-all')
>>> sift = sift if not ut.get_argflag('--no-sift') else None
>>> draw_desc = sift is not None
>>> kw = dict(
>>> prevsift=None, origsift=None, aid=None, info='', type_=None,
>>> shape_labels=False, vecfield=False, multicolored_arms=True,
>>> draw_chip=hack, draw_unwarped=hack, draw_warped=True, draw_desc=draw_desc
>>> )
>>> # execute function
>>> result = draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px,
>>> rect=False, ori=False, pts=False, **kw)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import numpy as np
import vtool as vt
# should not need ncols here
if nCols is not None:
if ut.VERBOSE:
print("Warning nCols is no longer needed")
# assert nCols_ == nCols
nCols = draw_chip + draw_unwarped + draw_warped + draw_desc
pnum_ = df2.make_pnum_nextgen(nRows, nCols, start=px)
# pnum_ = df2.get_pnum_func(nRows, nCols, base=1)
# countgen = itertools.count(1)
# pnumgen_ = df2.make_pnum_nextgen(nRows, nCols, base=1)
def _draw_patch(**kwargs):
return df2.draw_keypoint_patch(
chip,
kp,
sift,
rect=rect,
ori=ori,
pts=pts,
ori_color=custom_constants.DEEP_PINK,
multicolored_arms=multicolored_arms,
**kwargs
)
# Feature strings
xy_str, shape_str, scale, ori_str = ph.kp_info(kp)
if draw_chip:
pnum = pnum_()
df2.imshow(chip, fnum=fnum, pnum=pnum)
kpts_kw = dict(ell_linewidth=5, ell_alpha=1.0)
kpts_kw.update(kwargs)
df2.draw_kpts2([kp], **kpts_kw)
if draw_unwarped:
# Draw the unwarped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)))
# pnum = pnum_(px + six.next(countgen)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum)
ph.set_plotdat(ax, "viztype", "unwarped")
ph.set_plotdat(ax, "aid", aid)
ph.set_plotdat(ax, "fx", fx)
if shape_labels:
unwarped_lbl = "affine feature invV =\n" + shape_str + "\n" + ori_str
custom_figure.set_xlabel(unwarped_lbl, ax)
if draw_warped:
# Draw the warped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)), warped=True)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum, warped=True, **kwargs)
ph.set_plotdat(ax, "viztype", "warped")
ph.set_plotdat(ax, "aid", aid)
ph.set_plotdat(ax, "fx", fx)
if shape_labels:
warped_lbl = ("warped feature\n" + "fx=%r scale=%.1f\n" + "%s") % (fx, scale, xy_str)
else:
warped_lbl = ""
warped_lbl += info
custom_figure.set_xlabel(warped_lbl, ax)
if draw_desc:
border_color = {
"None": None,
"query": None,
"match": custom_constants.BLUE,
"norm": custom_constants.ORANGE,
}.get(str(type_).lower(), None)
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
# pnum = pnum_(px + six.next(countgen))
pnum = pnum_()
sift_as_vecfield = ph.SIFT_OR_VECFIELD or vecfield
if sift_as_vecfield:
custom_figure.figure(fnum=fnum, pnum=pnum)
df2.draw_keypoint_gradient_orientations(chip, kp, sift=sift)
else:
if sift.dtype.type == np.uint8:
sigtitle = "sift histogram" if (px % 3) == 0 else ""
ax = df2.plot_sift_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
else:
sigtitle = "descriptor vector" if (px % 3) == 0 else ""
ax = df2.plot_descriptor_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
ax._hs_viztype = "histogram"
# dist_list = ['L1', 'L2', 'hist_isect', 'emd']
# dist_list = ['L2', 'hist_isect']
# dist_list = ['L2']
# dist_list = ['bar_L2_sift', 'cos_sift']
# dist_list = ['L2_sift', 'bar_cos_sift']
dist_list = ["L2_sift"]
dist_str_list = []
if origsift is not None:
distmap_orig = vt.compute_distances(sift, origsift, dist_list)
dist_str_list.append(
"query_dist: "
+ ", ".join(["(%s, %s)" % (key, formatdist(val)) for key, val in six.iteritems(distmap_orig)])
)
if prevsift is not None:
distmap_prev = vt.compute_distances(sift, prevsift, dist_list)
dist_str_list.append(
"prev_dist: "
+ ", ".join(["(%s, %s)" % (key, formatdist(val)) for key, val in six.iteritems(distmap_prev)])
)
dist_str = "\n".join(dist_str_list)
custom_figure.set_xlabel(dist_str)
return px + nCols
def draw_feat_row(chip,
fx,
kp,
sift,
fnum,
nRows,
nCols=None,
px=None,
prevsift=None,
origsift=None,
aid=None,
info='',
type_=None,
shape_labels=False,
vecfield=False,
multicolored_arms=False,
draw_chip=False,
draw_warped=True,
draw_unwarped=True,
draw_desc=True,
rect=True,
ori=True,
pts=False,
**kwargs):
"""
draw_feat_row
SeeAlso:
ibeis.viz.viz_nearest_descriptors
~/code/ibeis/ibeis/viz/viz_nearest_descriptors.py
CommandLine:
# Use this to find the fx you want to visualize
python -m plottool.interact_keypoints --test-ishow_keypoints --show --fname zebra.png
# Use this to visualize the featrow
python -m plottool.viz_featrow --test-draw_feat_row --show
python -m plottool.viz_featrow --test-draw_feat_row --show --fname zebra.png --fx=121 --feat-all --no-sift
python -m plottool.viz_featrow --test-draw_feat_row --dpath figures --save ~/latex/crall-candidacy-2015/figures/viz_featrow.jpg
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.viz_featrow import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> kpts, vecs, imgBGR = pt.viz_keypoints.testdata_kpts()
>>> chip = imgBGR
>>> print('There are %d features' % (len(vecs)))
>>> fx = ut.get_argval('--fx', type_=int, default=0)
>>> kp = kpts[fx]
>>> sift = vecs[fx]
>>> fnum = 1
>>> nRows = 1
>>> nCols = 2
>>> px = 0
>>> hack = ut.get_argflag('--feat-all')
>>> sift = sift if not ut.get_argflag('--no-sift') else None
>>> draw_desc = sift is not None
>>> kw = dict(
>>> prevsift=None, origsift=None, aid=None, info='', type_=None,
>>> shape_labels=False, vecfield=False, multicolored_arms=True,
>>> draw_chip=hack, draw_unwarped=hack, draw_warped=True, draw_desc=draw_desc
>>> )
>>> # execute function
>>> result = draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px,
>>> rect=False, ori=False, pts=False, **kw)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import numpy as np
import vtool as vt
# should not need ncols here
if nCols is not None:
if ut.VERBOSE:
print('Warning nCols is no longer needed')
#assert nCols_ == nCols
nCols = (draw_chip + draw_unwarped + draw_warped + draw_desc)
pnum_ = df2.make_pnum_nextgen(nRows, nCols, start=px)
#pnum_ = df2.get_pnum_func(nRows, nCols, base=1)
#countgen = itertools.count(1)
#pnumgen_ = df2.make_pnum_nextgen(nRows, nCols, base=1)
def _draw_patch(**kwargs):
return df2.draw_keypoint_patch(chip,
kp,
sift,
rect=rect,
ori=ori,
pts=pts,
ori_color=custom_constants.DEEP_PINK,
multicolored_arms=multicolored_arms,
**kwargs)
# Feature strings
xy_str, shape_str, scale, ori_str = ph.kp_info(kp)
if draw_chip:
pnum = pnum_()
df2.imshow(chip, fnum=fnum, pnum=pnum)
kpts_kw = dict(ell_linewidth=5, ell_alpha=1.0)
kpts_kw.update(kwargs)
df2.draw_kpts2([kp], **kpts_kw)
if draw_unwarped:
# Draw the unwarped selected feature
#ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)))
#pnum = pnum_(px + six.next(countgen)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum)
ph.set_plotdat(ax, 'viztype', 'unwarped')
ph.set_plotdat(ax, 'aid', aid)
ph.set_plotdat(ax, 'fx', fx)
if shape_labels:
unwarped_lbl = 'affine feature invV =\n' + shape_str + '\n' + ori_str
custom_figure.set_xlabel(unwarped_lbl, ax)
if draw_warped:
# Draw the warped selected feature
#ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)), warped=True)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum, warped=True, **kwargs)
ph.set_plotdat(ax, 'viztype', 'warped')
ph.set_plotdat(ax, 'aid', aid)
ph.set_plotdat(ax, 'fx', fx)
if shape_labels:
warped_lbl = ('warped feature\n' + 'fx=%r scale=%.1f\n' +
'%s') % (fx, scale, xy_str)
else:
warped_lbl = ''
warped_lbl += info
custom_figure.set_xlabel(warped_lbl, ax)
if draw_desc:
border_color = {
'None': None,
'query': None,
'match': custom_constants.BLUE,
'norm': custom_constants.ORANGE
}.get(str(type_).lower(), None)
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
#pnum = pnum_(px + six.next(countgen))
pnum = pnum_()
sift_as_vecfield = ph.SIFT_OR_VECFIELD or vecfield
if sift_as_vecfield:
custom_figure.figure(fnum=fnum, pnum=pnum)
df2.draw_keypoint_gradient_orientations(chip, kp, sift=sift)
else:
if sift.dtype.type == np.uint8:
sigtitle = 'sift histogram' if (px % 3) == 0 else ''
ax = df2.plot_sift_signature(sift,
sigtitle,
fnum=fnum,
pnum=pnum)
else:
sigtitle = 'descriptor vector' if (px % 3) == 0 else ''
ax = df2.plot_descriptor_signature(sift,
sigtitle,
fnum=fnum,
pnum=pnum)
ax._hs_viztype = 'histogram'
#dist_list = ['L1', 'L2', 'hist_isect', 'emd']
#dist_list = ['L2', 'hist_isect']
#dist_list = ['L2']
#dist_list = ['bar_L2_sift', 'cos_sift']
#dist_list = ['L2_sift', 'bar_cos_sift']
dist_list = ['L2_sift']
dist_str_list = []
if origsift is not None:
distmap_orig = vt.compute_distances(sift, origsift, dist_list)
dist_str_list.append('query_dist: ' + ', '.join([
'(%s, %s)' % (key, formatdist(val))
for key, val in six.iteritems(distmap_orig)
]))
if prevsift is not None:
distmap_prev = vt.compute_distances(sift, prevsift, dist_list)
dist_str_list.append('prev_dist: ' + ', '.join([
'(%s, %s)' % (key, formatdist(val))
for key, val in six.iteritems(distmap_prev)
]))
dist_str = '\n'.join(dist_str_list)
custom_figure.set_xlabel(dist_str)
return px + nCols
