def _draw_kpts(*args, **kwargs):
if not show_kpts:
return
df2.draw_kpts2(*args, **kwargs)
def _draw_kpts(*args, **kwargs):
if not show_kpts:
return
df2.draw_kpts2(*args, **kwargs)
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
def _annotate_kpts(kpts_, sel_fx=None, **kwargs):
r"""
Args:
kpts_ (ndarray): keypoints
sel_fx (None):
Keywords:
color: 3/4-tuple, ndarray, or str
Returns:
None
Example:
>>> from plottool.viz_keypoints import * # NOQA
>>> sel_fx = None
>>> kpts = np.array([[ 92.9246, 17.5453, 7.8103, -3.4594, 10.8566, 0. ],
... [ 76.8585, 24.7918, 11.4412, -3.2634, 9.6287, 0. ],
... [ 140.6303, 24.9027, 10.4051, -10.9452, 10.5991, 0. ],])
"""
if len(kpts_) == 0:
print('len(kpts_) == 0...')
return
#color = kwargs.get('color', 'distinct' if sel_fx is None else df2.ORANGE)
color = kwargs.get('color', 'scale' if sel_fx is None else df2.ORANGE)
if color == 'distinct':
# hack for distinct colors
color = df2.distinct_colors(len(kpts_)) # , randomize=True)
elif color == 'scale':
# hack for distinct colors
import vtool as vt
#color = df2.scores_to_color(vt.get_scales(kpts_), cmap_='inferno', score_range=(0, 50))
color = df2.scores_to_color(vt.get_scales(kpts_), cmap_='viridis', score_range=(5, 30), cmap_range=None)
#df2.distinct_colors(len(kpts_)) # , randomize=True)
# Keypoint drawing kwargs
drawkpts_kw = {
'ell': True,
'pts': False,
'ell_alpha': .4,
'ell_linewidth': 2,
'ell_color': color,
}
drawkpts_kw.update(kwargs)
# draw all keypoints
if sel_fx is None:
df2.draw_kpts2(kpts_, **drawkpts_kw)
else:
# dont draw the selected keypoint in this batch
nonsel_kpts_ = np.vstack((kpts_[0:sel_fx], kpts_[sel_fx + 1:]))
# Draw selected keypoint
sel_kpts = kpts_[sel_fx:sel_fx + 1]
import utool as ut
if ut.isiterable(color) and ut.isiterable(color[0]):
# hack for distinct colors
drawkpts_kw['ell_color'] = color[0:sel_fx] + color[sel_fx + 1:]
drawkpts_kw
drawkpts_kw2 = drawkpts_kw.copy()
drawkpts_kw2.update({
'ell_color': df2.BLUE,
'eig': True,
'rect': True,
'ori': True,
})
df2.draw_kpts2(nonsel_kpts_, **drawkpts_kw)
df2.draw_kpts2(sel_kpts, **drawkpts_kw2)
