def plot_keypoint_scales(hs, fnum=1):
print('[dev] plot_keypoint_scales()')
cx2_kpts = hs.feats.cx2_kpts
if len(cx2_kpts) == 0:
hs.refresh_features()
cx2_kpts = hs.feats.cx2_kpts
cx2_nFeats = map(len, cx2_kpts)
kpts = np.vstack(cx2_kpts)
print('[dev] --- LaTeX --- ')
_printopts = np.get_printoptions()
np.set_printoptions(precision=3)
print(util_latex.latex_scalar(r'\# keypoints, ', len(kpts)))
print(util_latex.latex_mystats(r'\# keypoints per image', cx2_nFeats))
scales = ktool.get_scales(kpts)
scales = np.array(sorted(scales))
print(util_latex.latex_mystats(r'keypoint scale', scales))
np.set_printoptions(**_printopts)
print('[dev] ---/LaTeX --- ')
#
df2.figure(fnum=fnum, docla=True, title='sorted scales')
df2.plot(scales)
df2.adjust_subplots_safe()
#ax = df2.gca()
#ax.set_yscale('log')
#ax.set_xscale('log')
#
fnum += 1
df2.figure(fnum=fnum, docla=True, title='hist scales')
df2.show_histogram(scales, bins=20)
df2.adjust_subplots_safe()
#ax = df2.gca()
#ax.set_yscale('log')
#ax.set_xscale('log')
return fnum
def get_keypoint_stats(ibs):
from utool import util_latex as pytex
#from hsdev import dev_consistency
#dev_consistency.check_keypoint_consistency(ibs)
# Keypoint stats
#ibs.refresh_features()
from ibeis.control.IBEISControl import IBEISController
assert(isinstance(ibs, IBEISController))
valid_aids = np.array(ibs.get_valid_aids())
cx2_kpts = ibs.get_annot_kpts(valid_aids)
#cx2_kpts = ibs.feats.cx2_kpts
# Check cx2_kpts
cx2_nFeats = list(map(len, cx2_kpts))
kpts = np.vstack(cx2_kpts)
print('[dbinfo] --- LaTeX --- ')
_printopts = np.get_printoptions()
np.set_printoptions(precision=3)
scales = ktool.get_scales(kpts)
scales = np.array(sorted(scales))
tex_scale_stats = pytex.latex_mystats(r'kpt scale', scales)
tex_nKpts = pytex.latex_scalar(r'\# kpts', len(kpts))
tex_kpts_stats = pytex.latex_mystats(r'\# kpts/chip', cx2_nFeats)
print(tex_nKpts)
print(tex_kpts_stats)
print(tex_scale_stats)
np.set_printoptions(**_printopts)
print('[dbinfo] ---/LaTeX --- ')
return (tex_nKpts, tex_kpts_stats, tex_scale_stats)
def kp_info(kp):
kpts = np.array([kp])
xy_str = ktool.get_xy_strs(kpts)[0]
shape_str = ktool.get_shape_strs(kpts)[0]
ori_ = ktool.get_oris(kpts)[0]
ori_str = 'ori=%.2f' % ori_
scale = ktool.get_scales(kpts)[0]
return xy_str, shape_str, scale, ori_str
def kp_info(kp):
kpts = np.array([kp])
xy_str = ktool.get_xy_strs(kpts)[0]
shape_str = ktool.get_shape_strs(kpts)[0]
ori_ = ktool.get_oris(kpts)[0]
ori_str = 'ori=%.2f' % ori_
scale = ktool.get_scales(kpts)[0]
return xy_str, shape_str, scale, ori_str
def show_kpts_(fnum, pnum, kpts, vecs, title):
print('--------')
print('show_kpts: %r.%r' % (fnum, pnum))
print('kpts = %r' % (kpts,))
print('scales = %r' % ktool.get_scales(kpts))
# FIXME: this exists in ibeis. move to vtool
#dev_consistency.check_vecs(vecs3)
show_keypoints(imgL, kpts, sifts=vecs, pnum=pnum, rect=True,
ori=True, fnum=fnum, title=title, ell_alpha=1)
def show_kpts_(fnum, pnum, kpts, vecs, title):
print('--------')
print('show_kpts: %r.%r' % (fnum, pnum))
print('kpts = %r' % (kpts, ))
print('scales = %r' % ktool.get_scales(kpts))
# FIXME: this exists in ibeis. move to vtool
#dev_consistency.check_vecs(vecs3)
show_keypoints(imgL,
kpts,
sifts=vecs,
pnum=pnum,
rect=True,
ori=True,
fnum=fnum,
title=title,
ell_alpha=1)
def measure_feat_pairs(allres, orgtype='top_true'):
print('Measure ' + orgtype + ' pairs')
orgres = allres.__dict__[orgtype]
entropy_list = []
scale_list = []
score_list = []
lbl = 'Measuring ' + orgtype + ' pair '
fmt_str = utool.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qrid, rid, score, rank) in enumerate(orgres.iter()):
utool.print_(fmt_str % (ix + 1, ))
# Skip low ranks
if rank > 5:
rank_skips.append(qrid)
continue
other_rids = ibs.get_other_indexed_rids(qrid)
# Skip no groundtruth
if len(other_rids) == 0:
gt_skips.append(qrid)
continue
qres = qrid2_qres[qrid]
# Get matching feature indexes
fm = qres.cx2_fm[rid]
# Get their scores
fs = qres.cx2_fs[rid]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape, ))
desc1 = cx2_desc[qrid][fm[:, 0]]
desc2 = cx2_desc[rid][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qrid][fm[:, 0]]
kpts2 = cx2_kpts[rid][fm[:, 1]]
# Get their scale
scale1_m = ktool.get_scales(kpts1)
scale2_m = ktool.get_scales(kpts2)
# Get their entropy
entropy1 = descriptor_entropy(desc1, bw_factor=1)
entropy2 = descriptor_entropy(desc2, bw_factor=1)
# Append to results
entropy_tup = np.array(zip(entropy1, entropy2))
scale_tup = np.array(zip(scale1_m, scale2_m))
entropy_tup = entropy_tup.reshape(len(entropy_tup), 2)
scale_tup = scale_tup.reshape(len(scale_tup), 2)
entropy_list.append(entropy_tup)
scale_list.append(scale_tup)
score_list.append(fs)
print('Skipped %d total.' % (len(rank_skips) + len(gt_skips), ))
print('Skipped %d for rank > 5, %d for no gt' % (
len(rank_skips),
len(gt_skips),
))
print(np.unique(map(len, entropy_list)))
def evstack(tup):
return np.vstack(tup) if len(tup) > 0 else np.empty((0, 2))
def ehstack(tup):
return np.hstack(tup) if len(tup) > 0 else np.empty((0, 2))
entropy_pairs = evstack(entropy_list)
scale_pairs = evstack(scale_list)
scores = ehstack(score_list)
print('\n * Measured %d pairs' % len(entropy_pairs))
return entropy_pairs, scale_pairs, scores
def measure_feat_pairs(allres, orgtype='top_true'):
print('Measure ' + orgtype + ' pairs')
orgres = allres.__dict__[orgtype]
entropy_list = []
scale_list = []
score_list = []
lbl = 'Measuring ' + orgtype + ' pair '
fmt_str = utool.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qrid, rid, score, rank) in enumerate(orgres.iter()):
utool.print_(fmt_str % (ix + 1,))
# Skip low ranks
if rank > 5:
rank_skips.append(qrid)
continue
other_rids = ibs.get_other_indexed_rids(qrid)
# Skip no groundtruth
if len(other_rids) == 0:
gt_skips.append(qrid)
continue
qres = qrid2_qres[qrid]
# Get matching feature indexes
fm = qres.cx2_fm[rid]
# Get their scores
fs = qres.cx2_fs[rid]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape,))
desc1 = cx2_desc[qrid][fm[:, 0]]
desc2 = cx2_desc[rid][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qrid][fm[:, 0]]
kpts2 = cx2_kpts[rid][fm[:, 1]]
# Get their scale
scale1_m = ktool.get_scales(kpts1)
scale2_m = ktool.get_scales(kpts2)
# Get their entropy
entropy1 = descriptor_entropy(desc1, bw_factor=1)
entropy2 = descriptor_entropy(desc2, bw_factor=1)
# Append to results
entropy_tup = np.array(zip(entropy1, entropy2))
scale_tup = np.array(zip(scale1_m, scale2_m))
entropy_tup = entropy_tup.reshape(len(entropy_tup), 2)
scale_tup = scale_tup.reshape(len(scale_tup), 2)
entropy_list.append(entropy_tup)
scale_list.append(scale_tup)
score_list.append(fs)
print('Skipped %d total.' % (len(rank_skips) + len(gt_skips),))
print('Skipped %d for rank > 5, %d for no gt' % (len(rank_skips), len(gt_skips),))
print(np.unique(map(len, entropy_list)))
def evstack(tup):
return np.vstack(tup) if len(tup) > 0 else np.empty((0, 2))
def ehstack(tup):
return np.hstack(tup) if len(tup) > 0 else np.empty((0, 2))
entropy_pairs = evstack(entropy_list)
scale_pairs = evstack(scale_list)
scores = ehstack(score_list)
print('\n * Measured %d pairs' % len(entropy_pairs))
return entropy_pairs, scale_pairs, scores
def test_homog_errors(H,
kpts1,
kpts2,
fm,
xy_thresh_sqrd,
scale_thresh,
ori_thresh,
full_homog_checks=True):
r"""
Test to see which keypoints the homography correctly maps
Args:
H (ndarray[float64_t, ndim=2]): homography/perspective matrix
kpts1 (ndarray[float32_t, ndim=2]): keypoints
kpts2 (ndarray[float32_t, ndim=2]): keypoints
fm (list): list of feature matches as tuples (qfx, dfx)
xy_thresh_sqrd (float):
scale_thresh (float):
ori_thresh (float): angle in radians
full_homog_checks (bool):
Returns:
tuple: homog_tup1
CommandLine:
python -m vtool.spatial_verification --test-test_homog_errors:0 --show
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance --no-affine-invariance --xy-thresh=.001
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance --no-affine-invariance --xy-thresh=.001 --no-full-homog-checks
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --no-full-homog-checks
# --------------
# Shows (sorta) how inliers are computed
python -m vtool.spatial_verification --test-test_homog_errors:1 --show
python -m vtool.spatial_verification --test-test_homog_errors:1 --show --rotation_invariance
python -m vtool.spatial_verification --test-test_homog_errors:1 --show --rotation_invariance --no-affine-invariance --xy-thresh=.001
python -m vtool.spatial_verification --test-test_homog_errors:1 --show --rotation_invariance --xy-thresh=.001
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance --xy-thresh=.001
Example0:
>>> # DISABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> import plottool as pt
>>> kpts1, kpts2, fm, aff_inliers, rchip1, rchip2, xy_thresh_sqrd = testdata_matching_affine_inliers()
>>> H = estimate_refined_transform(kpts1, kpts2, fm, aff_inliers)
>>> scale_thresh, ori_thresh = 2.0, 1.57
>>> full_homog_checks = not ut.get_argflag('--no-full-homog-checks')
>>> homog_tup1 = test_homog_errors(H, kpts1, kpts2, fm, xy_thresh_sqrd, scale_thresh, ori_thresh, full_homog_checks)
>>> homog_tup = (homog_tup1[0], homog_tup1[2])
>>> ut.quit_if_noshow()
>>> pt.draw_sv.show_sv(rchip1, rchip2, kpts1, kpts2, fm, homog_tup=homog_tup)
>>> ut.show_if_requested()
Example1:
>>> # DISABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> import plottool as pt
>>> kpts1, kpts2, fm_, aff_inliers, rchip1, rchip2, xy_thresh_sqrd = testdata_matching_affine_inliers()
>>> H = estimate_refined_transform(kpts1, kpts2, fm_, aff_inliers)
>>> scale_thresh, ori_thresh = 2.0, 1.57
>>> full_homog_checks = not ut.get_argflag('--no-full-homog-checks')
>>> # ----------------
>>> # Take subset of feature matches
>>> fm = fm_
>>> scale_err, xy_err, ori_err = \
... ut.exec_func_src(test_homog_errors, globals(), locals(),
... 'scale_err, xy_err, ori_err'.split(', '))
>>> # we only care about checking out scale and orientation here. ignore bad xy points
>>> xy_inliers_flag = np.less(xy_err, xy_thresh_sqrd)
>>> scale_err[~xy_inliers_flag] = 0
>>> # filter
>>> fm = fm_[np.array(scale_err).argsort()[::-1][:10]]
>>> fm = fm_[np.array(scale_err).argsort()[::-1][:10]]
>>> # Exec sourcecode
>>> kpts1_m, kpts2_m, off_xy1_m, off_xy1_mt, dxy1_m, dxy1_mt, xy2_m, xy1_m, xy1_mt, scale_err, xy_err, ori_err = \
... ut.exec_func_src(test_homog_errors, globals(), locals(),
... 'kpts1_m, kpts2_m, off_xy1_m, off_xy1_mt, dxy1_m, dxy1_mt, xy2_m, xy1_m, xy1_mt, scale_err, xy_err, ori_err'.split(', '))
>>> #---------------
>>> ut.quit_if_noshow()
>>> pt.figure(fnum=1, pnum=(1, 2, 1), title='orig points and offset point')
>>> segments_list1 = np.array(list(zip(xy1_m.T.tolist(), off_xy1_m.T.tolist())))
>>> pt.draw_line_segments(segments_list1, color=pt.LIGHT_BLUE)
>>> pt.dark_background()
>>> #---------------
>>> pt.figure(fnum=1, pnum=(1, 2, 2), title='transformed points and matching points')
>>> #---------------
>>> # first have to make corresponding offset points
>>> # Use reference point for scale and orientation tests
>>> oris2_m = ktool.get_oris(kpts2_m)
>>> scales2_m = ktool.get_scales(kpts2_m)
>>> dxy2_m = np.vstack((np.sin(oris2_m), -np.cos(oris2_m)))
>>> scaled_dxy2_m = dxy2_m * scales2_m[None, :]
>>> off_xy2_m = xy2_m + scaled_dxy2_m
>>> # Draw transformed semgents
>>> segments_list2 = np.array(list(zip(xy2_m.T.tolist(), off_xy2_m.T.tolist())))
>>> pt.draw_line_segments(segments_list2, color=pt.GREEN)
>>> # Draw corresponding matches semgents
>>> segments_list3 = np.array(list(zip(xy1_mt.T.tolist(), off_xy1_mt.T.tolist())))
>>> pt.draw_line_segments(segments_list3, color=pt.RED)
>>> # Draw matches between correspondences
>>> segments_list4 = np.array(list(zip(xy1_mt.T.tolist(), xy2_m.T.tolist())))
>>> pt.draw_line_segments(segments_list4, color=pt.ORANGE)
>>> pt.dark_background()
>>> #---------------
>>> #vt.get _xy_axis_extents(kpts1_m)
>>> #pt.draw_sv.show_sv(rchip1, rchip2, kpts1, kpts2, fm, homog_tup=homog_tup)
>>> ut.show_if_requested()
"""
kpts1_m = kpts1.take(fm.T[0], axis=0)
kpts2_m = kpts2.take(fm.T[1], axis=0)
# Transform all xy1 matches to xy2 space
xy1_m = ktool.get_xys(kpts1_m)
#with ut.embed_on_exception_context:
xy1_mt = ltool.transform_points_with_homography(H, xy1_m)
#xy1_mt = ktool.transform_kpts_xys(H, kpts1_m)
xy2_m = ktool.get_xys(kpts2_m)
# --- Find (Squared) Homography Distance Error ---
# You cannot test for scale or orientation easily here because
# you no longer have an ellipse? (maybe, probably have a conic) when using a
# projective transformation
xy_err = dtool.L2_sqrd(xy1_mt.T, xy2_m.T)
# Estimate final inliers
#ut.embed()
if full_homog_checks:
# TODO: may need to use more than one reference point
# Use reference point for scale and orientation tests
oris1_m = ktool.get_oris(kpts1_m)
scales1_m = ktool.get_scales(kpts1_m)
# Get point offsets with unit length
dxy1_m = np.vstack((np.sin(oris1_m), -np.cos(oris1_m)))
scaled_dxy1_m = dxy1_m * scales1_m[None, :]
off_xy1_m = xy1_m + scaled_dxy1_m
# transform reference point
off_xy1_mt = ltool.transform_points_with_homography(H, off_xy1_m)
scaled_dxy1_mt = xy1_mt - off_xy1_mt
scales1_mt = npl.norm(scaled_dxy1_mt, axis=0)
#with warnings.catch_warnings():
# warnings.simplefilter("ignore")
dxy1_mt = scaled_dxy1_mt / scales1_mt
# adjust for gravity vector being 0
oris1_mt = np.arctan2(dxy1_mt[1], dxy1_mt[0]) - ktool.GRAVITY_THETA
_det1_mt = scales1_mt**2
det2_m = ktool.get_sqrd_scales(kpts2_m)
ori2_m = ktool.get_oris(kpts2_m)
#xy_err = dtool.L2_sqrd(xy2_m.T, _xy1_mt.T)
scale_err = dtool.det_distance(_det1_mt, det2_m)
ori_err = dtool.ori_distance(oris1_mt, ori2_m)
###
xy_inliers_flag = np.less(xy_err, xy_thresh_sqrd)
scale_inliers_flag = np.less(scale_err, scale_thresh)
ori_inliers_flag = np.less(ori_err, ori_thresh)
hypo_inliers_flag = xy_inliers_flag # Try to re-use memory
np.logical_and(hypo_inliers_flag,
ori_inliers_flag,
out=hypo_inliers_flag)
np.logical_and(hypo_inliers_flag,
scale_inliers_flag,
out=hypo_inliers_flag)
# Seems slower due to memory
#hypo_inliers_flag = np.logical_and.reduce(
# [xy_inliers_flag, ori_inliers_flag, scale_inliers_flag])
# this is also slower
#hypo_inliers_flag = np.logical_and.reduce((xy_inliers_flag,
#ori_inliers_flag, scale_inliers_flag), out=xy_inliers_flag)
refined_inliers = np.where(hypo_inliers_flag)[0].astype(INDEX_DTYPE)
refined_errors = (xy_err, ori_err, scale_err)
else:
refined_inliers = np.where(
xy_err < xy_thresh_sqrd)[0].astype(INDEX_DTYPE)
refined_errors = (xy_err, None, None)
homog_tup1 = (refined_inliers, refined_errors, H)
return homog_tup1
def test_homog_errors(H, kpts1, kpts2, fm, xy_thresh_sqrd, scale_thresh,
ori_thresh, full_homog_checks=True):
r"""
Test to see which keypoints the homography correctly maps
Args:
H (ndarray[float64_t, ndim=2]): homography/perspective matrix
kpts1 (ndarray[float32_t, ndim=2]): keypoints
kpts2 (ndarray[float32_t, ndim=2]): keypoints
fm (list): list of feature matches as tuples (qfx, dfx)
xy_thresh_sqrd (float):
scale_thresh (float):
ori_thresh (float): angle in radians
full_homog_checks (bool):
Returns:
tuple: homog_tup1
CommandLine:
python -m vtool.spatial_verification --test-test_homog_errors:0 --show
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance --no-affine-invariance --xy-thresh=.001
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance --no-affine-invariance --xy-thresh=.001 --no-full-homog-checks
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --no-full-homog-checks
# --------------
# Shows (sorta) how inliers are computed
python -m vtool.spatial_verification --test-test_homog_errors:1 --show
python -m vtool.spatial_verification --test-test_homog_errors:1 --show --rotation_invariance
python -m vtool.spatial_verification --test-test_homog_errors:1 --show --rotation_invariance --no-affine-invariance --xy-thresh=.001
python -m vtool.spatial_verification --test-test_homog_errors:1 --show --rotation_invariance --xy-thresh=.001
python -m vtool.spatial_verification --test-test_homog_errors:0 --show --rotation_invariance --xy-thresh=.001
Example0:
>>> # DISABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> import plottool as pt
>>> kpts1, kpts2, fm, aff_inliers, rchip1, rchip2, xy_thresh_sqrd = testdata_matching_affine_inliers()
>>> H = estimate_refined_transform(kpts1, kpts2, fm, aff_inliers)
>>> scale_thresh, ori_thresh = 2.0, 1.57
>>> full_homog_checks = not ut.get_argflag('--no-full-homog-checks')
>>> homog_tup1 = test_homog_errors(H, kpts1, kpts2, fm, xy_thresh_sqrd, scale_thresh, ori_thresh, full_homog_checks)
>>> homog_tup = (homog_tup1[0], homog_tup1[2])
>>> ut.quit_if_noshow()
>>> pt.draw_sv.show_sv(rchip1, rchip2, kpts1, kpts2, fm, homog_tup=homog_tup)
>>> ut.show_if_requested()
Example1:
>>> # DISABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> import plottool as pt
>>> kpts1, kpts2, fm_, aff_inliers, rchip1, rchip2, xy_thresh_sqrd = testdata_matching_affine_inliers()
>>> H = estimate_refined_transform(kpts1, kpts2, fm_, aff_inliers)
>>> scale_thresh, ori_thresh = 2.0, 1.57
>>> full_homog_checks = not ut.get_argflag('--no-full-homog-checks')
>>> # ----------------
>>> # Take subset of feature matches
>>> fm = fm_
>>> scale_err, xy_err, ori_err = \
... ut.exec_func_src(test_homog_errors, globals(), locals(),
... 'scale_err, xy_err, ori_err'.split(', '))
>>> # we only care about checking out scale and orientation here. ignore bad xy points
>>> xy_inliers_flag = np.less(xy_err, xy_thresh_sqrd)
>>> scale_err[~xy_inliers_flag] = 0
>>> # filter
>>> fm = fm_[np.array(scale_err).argsort()[::-1][:10]]
>>> fm = fm_[np.array(scale_err).argsort()[::-1][:10]]
>>> # Exec sourcecode
>>> kpts1_m, kpts2_m, off_xy1_m, off_xy1_mt, dxy1_m, dxy1_mt, xy2_m, xy1_m, xy1_mt, scale_err, xy_err, ori_err = \
... ut.exec_func_src(test_homog_errors, globals(), locals(),
... 'kpts1_m, kpts2_m, off_xy1_m, off_xy1_mt, dxy1_m, dxy1_mt, xy2_m, xy1_m, xy1_mt, scale_err, xy_err, ori_err'.split(', '))
>>> #---------------
>>> ut.quit_if_noshow()
>>> pt.figure(fnum=1, pnum=(1, 2, 1), title='orig points and offset point')
>>> segments_list1 = np.array(list(zip(xy1_m.T.tolist(), off_xy1_m.T.tolist())))
>>> pt.draw_line_segments(segments_list1, color=pt.LIGHT_BLUE)
>>> pt.dark_background()
>>> #---------------
>>> pt.figure(fnum=1, pnum=(1, 2, 2), title='transformed points and matching points')
>>> #---------------
>>> # first have to make corresponding offset points
>>> # Use reference point for scale and orientation tests
>>> oris2_m = ktool.get_oris(kpts2_m)
>>> scales2_m = ktool.get_scales(kpts2_m)
>>> dxy2_m = np.vstack((np.sin(oris2_m), -np.cos(oris2_m)))
>>> scaled_dxy2_m = dxy2_m * scales2_m[None, :]
>>> off_xy2_m = xy2_m + scaled_dxy2_m
>>> # Draw transformed semgents
>>> segments_list2 = np.array(list(zip(xy2_m.T.tolist(), off_xy2_m.T.tolist())))
>>> pt.draw_line_segments(segments_list2, color=pt.GREEN)
>>> # Draw corresponding matches semgents
>>> segments_list3 = np.array(list(zip(xy1_mt.T.tolist(), off_xy1_mt.T.tolist())))
>>> pt.draw_line_segments(segments_list3, color=pt.RED)
>>> # Draw matches between correspondences
>>> segments_list4 = np.array(list(zip(xy1_mt.T.tolist(), xy2_m.T.tolist())))
>>> pt.draw_line_segments(segments_list4, color=pt.ORANGE)
>>> pt.dark_background()
>>> #---------------
>>> #vt.get _xy_axis_extents(kpts1_m)
>>> #pt.draw_sv.show_sv(rchip1, rchip2, kpts1, kpts2, fm, homog_tup=homog_tup)
>>> ut.show_if_requested()
"""
kpts1_m = kpts1.take(fm.T[0], axis=0)
kpts2_m = kpts2.take(fm.T[1], axis=0)
# Transform all xy1 matches to xy2 space
xy1_m = ktool.get_xys(kpts1_m)
#with ut.embed_on_exception_context:
xy1_mt = ltool.transform_points_with_homography(H, xy1_m)
#xy1_mt = ktool.transform_kpts_xys(H, kpts1_m)
xy2_m = ktool.get_xys(kpts2_m)
# --- Find (Squared) Homography Distance Error ---
# You cannot test for scale or orientation easily here because
# you no longer have an ellipse? (maybe, probably have a conic) when using a
# projective transformation
xy_err = dtool.L2_sqrd(xy1_mt.T, xy2_m.T)
# Estimate final inliers
#ut.embed()
if full_homog_checks:
# TODO: may need to use more than one reference point
# Use reference point for scale and orientation tests
oris1_m = ktool.get_oris(kpts1_m)
scales1_m = ktool.get_scales(kpts1_m)
# Get point offsets with unit length
dxy1_m = np.vstack((np.sin(oris1_m), -np.cos(oris1_m)))
scaled_dxy1_m = dxy1_m * scales1_m[None, :]
off_xy1_m = xy1_m + scaled_dxy1_m
# transform reference point
off_xy1_mt = ltool.transform_points_with_homography(H, off_xy1_m)
scaled_dxy1_mt = xy1_mt - off_xy1_mt
scales1_mt = npl.norm(scaled_dxy1_mt, axis=0)
#with warnings.catch_warnings():
# warnings.simplefilter("ignore")
dxy1_mt = scaled_dxy1_mt / scales1_mt
# adjust for gravity vector being 0
oris1_mt = np.arctan2(dxy1_mt[1], dxy1_mt[0]) - ktool.GRAVITY_THETA
_det1_mt = scales1_mt ** 2
det2_m = ktool.get_sqrd_scales(kpts2_m)
ori2_m = ktool.get_oris(kpts2_m)
#xy_err = dtool.L2_sqrd(xy2_m.T, _xy1_mt.T)
scale_err = dtool.det_distance(_det1_mt, det2_m)
ori_err = dtool.ori_distance(oris1_mt, ori2_m)
###
xy_inliers_flag = np.less(xy_err, xy_thresh_sqrd)
scale_inliers_flag = np.less(scale_err, scale_thresh)
ori_inliers_flag = np.less(ori_err, ori_thresh)
hypo_inliers_flag = xy_inliers_flag # Try to re-use memory
np.logical_and(hypo_inliers_flag, ori_inliers_flag, out=hypo_inliers_flag)
np.logical_and(hypo_inliers_flag, scale_inliers_flag, out=hypo_inliers_flag)
# Seems slower due to memory
#hypo_inliers_flag = np.logical_and.reduce(
# [xy_inliers_flag, ori_inliers_flag, scale_inliers_flag])
# this is also slower
#hypo_inliers_flag = np.logical_and.reduce((xy_inliers_flag,
#ori_inliers_flag, scale_inliers_flag), out=xy_inliers_flag)
refined_inliers = np.where(hypo_inliers_flag)[0].astype(INDEX_DTYPE)
refined_errors = (xy_err, ori_err, scale_err)
else:
refined_inliers = np.where(xy_err < xy_thresh_sqrd)[0].astype(INDEX_DTYPE)
refined_errors = (xy_err, None, None)
homog_tup1 = (refined_inliers, refined_errors, H)
return homog_tup1
