def testdata_matching_affine_inliers_normalized():
tup = testdata_matching_affine_inliers()
kpts1, kpts2, fm, aff_inliers, rchip1, rchip2, xy_thresh_sqrd = tup
kpts1_ma = kpts1.take(fm.T[0].take(aff_inliers), axis=0)
kpts2_ma = kpts2.take(fm.T[1].take(aff_inliers), axis=0)
#kpts1_ma, kpts2_ma, rchip1, rchip2, xy_thresh_sqrd = testdata_matching_affine_inliers()
# Matching affine inliers
xy1_ma = ktool.get_xys(kpts1_ma)
xy2_ma = ktool.get_xys(kpts2_ma)
# Matching affine inliers normalized
xy1_man, T1 = ltool.whiten_xy_points(xy1_ma)
xy2_man, T2 = ltool.whiten_xy_points(xy2_ma)
return xy1_man, xy2_man, rchip1, rchip2, T1, T2
def testdata_matching_affine_inliers_normalized():
tup = testdata_matching_affine_inliers()
kpts1, kpts2, fm, aff_inliers, rchip1, rchip2, xy_thresh_sqrd = tup
kpts1_ma = kpts1.take(fm.T[0].take(aff_inliers), axis=0)
kpts2_ma = kpts2.take(fm.T[1].take(aff_inliers), axis=0)
#kpts1_ma, kpts2_ma, rchip1, rchip2, xy_thresh_sqrd = testdata_matching_affine_inliers()
# Matching affine inliers
xy1_ma = ktool.get_xys(kpts1_ma)
xy2_ma = ktool.get_xys(kpts2_ma)
# Matching affine inliers normalized
xy1_man, T1 = ltool.whiten_xy_points(xy1_ma)
xy2_man, T2 = ltool.whiten_xy_points(xy2_ma)
return xy1_man, xy2_man, rchip1, rchip2, T1, T2
def kpts_dlen_sqrd(tx):
kpts2 = topx2_kpts[tx]
aid = topx2_aid[tx]
fm = aid2_fm[aid]
x_m, y_m = ktool.get_xys(kpts2[fm[:, 1]])
dlensqrd = (x_m.max() - x_m.min()) ** 2 + (y_m.max() - y_m.min()) ** 2
return dlensqrd
def kpts_dlen_sqrd(tx):
kpts2 = topx2_kpts[tx]
aid = topx2_aid[tx]
fm = aid2_fm[aid]
x_m, y_m = ktool.get_xys(kpts2[fm[:, 1]])
dlensqrd = (x_m.max() - x_m.min())**2 + (y_m.max() - y_m.min())**2
return dlensqrd
def perterb_kpts(kpts, xy_std=None, invV_std=None, ori_std=None, damping=None,
seed=None, **kwargs):
""" Adds normally distributed pertibations to keypoints """
# TODO: Move to ktool
# Get standard deviations of pertibations
if xy_std is None:
xy_std = ktool.get_xys(kpts).std(1) + mtool.eps
if invV_std is None:
invV_std = ktool.get_invVs(kpts).std(1) + mtool.eps
if ori_std is None:
ori_std = ktool.get_oris(kpts).std() + mtool.eps
xy_std = np.array(xy_std, dtype=ktool.KPTS_DTYPE)
invV_std = np.array(invV_std, dtype=ktool.KPTS_DTYPE)
if damping is not None:
xy_std /= damping
invV_std /= damping
ori_std /= damping
if seed is not None:
np.random.seed(seed)
# Create normally distributed pertibations
xy_aug = np.random.normal(0, scale=xy_std, size=(len(kpts), 2)).astype(ktool.KPTS_DTYPE)
try:
invV_aug = np.random.normal(0, scale=invV_std, size=(len(kpts), 3)).astype(ktool.KPTS_DTYPE)
except ValueError as ex:
ut.printex(ex, key_list=[(type, 'invV_std')])
raise
ori_aug = np.random.normal(0, scale=ori_std, size=(len(kpts), 1)).astype(ktool.KPTS_DTYPE)
# Augment keypoints
aug = np.hstack((xy_aug, invV_aug, ori_aug))
kpts_ = kpts + aug
# Ensure keypoint feasibility
kpts_ = force_kpts_feasibility(kpts_)
#print(ut.dict_str({key: type(val) if not isinstance(val, np.ndarray) else val.dtype for key, val in locals().items()}))
#assert kpts_.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere kpts_.dtype=%r' % (kpts_.dtype)
return kpts_
def get_normalized_affine_inliers(kpts1, kpts2, fm, aff_inliers):
"""
returns xy-inliers that are normalized to have a mean of 0 and std of 1 as
well as the transformations so the inverse can be taken
"""
fm_affine = fm.take(aff_inliers, axis=0)
# Get corresponding points and shapes
kpts1_ma = kpts1.take(fm_affine.T[0], axis=0)
kpts2_ma = kpts2.take(fm_affine.T[1], axis=0)
#kpts1_ma = kpts1.take(fm_affine.T[0], axis=0)
#kpts2_ma = kpts2.take(fm_affine.T[1], axis=0)
# Normalize affine inliers xy locations
xy1_ma = ktool.get_xys(kpts1_ma)
xy2_ma = ktool.get_xys(kpts2_ma)
xy1_man, T1 = ltool.whiten_xy_points(xy1_ma)
xy2_man, T2 = ltool.whiten_xy_points(xy2_ma)
return xy1_man, xy2_man, T1, T2
def get_normalized_affine_inliers(kpts1, kpts2, fm, aff_inliers):
"""
returns xy-inliers that are normalized to have a mean of 0 and std of 1 as
well as the transformations so the inverse can be taken
"""
fm_affine = fm.take(aff_inliers, axis=0)
# Get corresponding points and shapes
kpts1_ma = kpts1.take(fm_affine.T[0], axis=0)
kpts2_ma = kpts2.take(fm_affine.T[1], axis=0)
#kpts1_ma = kpts1.take(fm_affine.T[0], axis=0)
#kpts2_ma = kpts2.take(fm_affine.T[1], axis=0)
# Normalize affine inliers xy locations
xy1_ma = ktool.get_xys(kpts1_ma)
xy2_ma = ktool.get_xys(kpts2_ma)
xy1_man, T1 = ltool.whiten_xy_points(xy1_ma)
xy2_man, T2 = ltool.whiten_xy_points(xy2_ma)
return xy1_man, xy2_man, T1, T2
def orientation_actors(kpts, H=None):
""" creates orientation actors w.r.t. the gravity vector """
import vtool.keypoint as ktool
try:
# Get xy diretion of the keypoint orientations
_xs, _ys = ktool.get_xys(kpts)
_iv11s, _iv21s, _iv22s = ktool.get_invVs(kpts)
_oris = ktool.get_oris(kpts)
# mpl's 0 ori == (-tau / 4) w.r.t GRAVITY_THETA
abs_oris = _oris + ktool.GRAVITY_THETA
_sins = np.sin(abs_oris)
_coss = np.cos(abs_oris)
# The following is essentially
# invV.dot(R)
_dxs = _coss * _iv11s
_dys = _coss * _iv21s + _sins * _iv22s
# ut.embed()
# if H is not None:
# # adjust for homogrpahy
# import vtool as vt
# _xs, _ys = vt.transform_points_with_homography(H, np.vstack((_xs, _ys)))
# _dxs, _dys = vt.transform_points_with_homography(H, np.vstack((_dxs, _dys)))
# head_width_list = np.log(_iv11s * _iv22s) / 5
head_width_list = np.ones(len(_iv11s)) / 10
kwargs = {
'length_includes_head': True,
'shape': 'full',
'overhang': 0,
'head_starts_at_zero': False,
}
if H is not None:
kwargs['transform'] = HomographyTransform(H)
ori_actors = [
mpl.patches.FancyArrow(x, y, dx, dy, head_width=hw, **kwargs)
for (x, y, dx, dy,
hw) in zip(_xs, _ys, _dxs, _dys, head_width_list)
]
except ValueError as ex:
print('\n[mplkp.2] !!! ERROR %s: ' % str(ex))
print('_oris.shape = %r' % (_oris.shape, ))
# print('x, y, dx, dy = %r' % ((x, y, dx, dy),))
print('_dxs = %r' % (_dxs, ))
print('_dys = %r' % (_dys, ))
print('_xs = %r' % (_xs, ))
print('_ys = %r' % (_ys, ))
raise
return ori_actors
def orientation_actors(kpts, H=None):
""" creates orientation actors w.r.t. the gravity vector """
try:
# Get xy diretion of the keypoint orientations
_xs, _ys = ktool.get_xys(kpts)
_iv11s, _iv21s, _iv22s = ktool.get_invVs(kpts)
_oris = ktool.get_oris(kpts)
# mpl's 0 ori == (-tau / 4) w.r.t GRAVITY_THETA
abs_oris = _oris + ktool.GRAVITY_THETA
_sins = np.sin(abs_oris)
_coss = np.cos(abs_oris)
# The following is essentially
# invV.dot(R)
_dxs = _coss * _iv11s
_dys = _coss * _iv21s + _sins * _iv22s
#ut.embed()
#if H is not None:
# # adjust for homogrpahy
# import vtool as vt
# _xs, _ys = vt.transform_points_with_homography(H, np.vstack((_xs, _ys)))
# _dxs, _dys = vt.transform_points_with_homography(H, np.vstack((_dxs, _dys)))
#head_width_list = np.log(_iv11s * _iv22s) / 5
head_width_list = np.ones(len(_iv11s)) / 10
kwargs = {
'length_includes_head': True,
'shape': 'full',
'overhang': 0,
'head_starts_at_zero': False,
}
if H is not None:
kwargs['transform'] = HomographyTransform(H)
ori_actors = [mpl.patches.FancyArrow(x, y, dx, dy, head_width=hw, **kwargs)
for (x, y, dx, dy, hw) in
zip(_xs, _ys, _dxs, _dys, head_width_list)]
except ValueError as ex:
print('\n[mplkp.2] !!! ERROR %s: ' % str(ex))
print('_oris.shape = %r' % (_oris.shape,))
print('x, y, dx, dy = %r' % ((x, y, dx, dy),))
print('_dxs = %r' % (_dxs,))
print('_dys = %r' % (_dys,))
print('_xs = %r' % (_xs,))
print('_ys = %r' % (_ys,))
raise
return ori_actors
def test_affine_errors(H, kpts1, kpts2, fm, xy_thresh_sqrd, scale_thresh_sqrd,
ori_thresh):
"""
used for refinement as opposed to initial estimation
"""
kpts1_m = kpts1.take(fm.T[0], axis=0)
kpts2_m = kpts2.take(fm.T[1], axis=0)
invVR1s_m = ktool.get_invVR_mats3x3(kpts1_m)
xy2_m = ktool.get_xys(kpts2_m)
det2_m = ktool.get_sqrd_scales(kpts2_m)
ori2_m = ktool.get_oris(kpts2_m)
refined_inliers, refined_errors = _test_hypothesis_inliers(
H, invVR1s_m, xy2_m, det2_m, ori2_m, xy_thresh_sqrd, scale_thresh_sqrd,
ori_thresh)
refined_tup1 = (refined_inliers, refined_errors, H)
return refined_tup1
def test_affine_errors(H, kpts1, kpts2, fm, xy_thresh_sqrd, scale_thresh_sqrd,
ori_thresh):
"""
used for refinement as opposed to initial estimation
"""
kpts1_m = kpts1.take(fm.T[0], axis=0)
kpts2_m = kpts2.take(fm.T[1], axis=0)
invVR1s_m = ktool.get_invVR_mats3x3(kpts1_m)
xy2_m = ktool.get_xys(kpts2_m)
det2_m = ktool.get_sqrd_scales(kpts2_m)
ori2_m = ktool.get_oris(kpts2_m)
refined_inliers, refined_errors = _test_hypothesis_inliers(
H, invVR1s_m, xy2_m, det2_m, ori2_m, xy_thresh_sqrd, scale_thresh_sqrd,
ori_thresh)
refined_tup1 = (refined_inliers, refined_errors, H)
return refined_tup1
def perterb_kpts(kpts,
xy_std=None,
invV_std=None,
ori_std=None,
damping=None,
seed=None,
**kwargs):
""" Adds normally distributed pertibations to keypoints """
# TODO: Move to ktool
# Get standard deviations of pertibations
if xy_std is None:
xy_std = ktool.get_xys(kpts).std(1) + mtool.eps
if invV_std is None:
invV_std = ktool.get_invVs(kpts).std(1) + mtool.eps
if ori_std is None:
ori_std = ktool.get_oris(kpts).std() + mtool.eps
xy_std = np.array(xy_std, dtype=ktool.KPTS_DTYPE)
invV_std = np.array(invV_std, dtype=ktool.KPTS_DTYPE)
if damping is not None:
xy_std /= damping
invV_std /= damping
ori_std /= damping
if seed is not None:
np.random.seed(seed)
# Create normally distributed pertibations
xy_aug = np.random.normal(0, scale=xy_std,
size=(len(kpts), 2)).astype(ktool.KPTS_DTYPE)
try:
invV_aug = np.random.normal(0, scale=invV_std,
size=(len(kpts),
3)).astype(ktool.KPTS_DTYPE)
except ValueError as ex:
ut.printex(ex, key_list=[(type, 'invV_std')])
raise
ori_aug = np.random.normal(0, scale=ori_std,
size=(len(kpts), 1)).astype(ktool.KPTS_DTYPE)
# Augment keypoints
aug = np.hstack((xy_aug, invV_aug, ori_aug))
kpts_ = kpts + aug
# Ensure keypoint feasibility
kpts_ = force_kpts_feasibility(kpts_)
#print(ut.dict_str({key: type(val) if not isinstance(val, np.ndarray) else val.dtype for key, val in locals().items()}))
#assert kpts_.dtype == ktool.KPTS_DTYPE, 'bad cast somewhere kpts_.dtype=%r' % (kpts_.dtype)
return kpts_
def draw_keypoints(ax,
kpts_,
scale_factor=1.0,
offset=(0.0, 0.0),
rotation=0.0,
ell=True,
pts=False,
rect=False,
eig=False,
ori=False,
sifts=None,
siftkw={},
H=None,
**kwargs):
"""
draws keypoints extracted by pyhesaff onto a matplotlib axis
FIXME: There is probably a matplotlib bug here. If you specify two different
alphas in a collection, whatever the last alpha was gets applied to
everything
Args:
ax (mpl.Axes):
kpts (ndarray): keypoints [[x, y, a, c, d, theta], ...]
scale_factor (float):
offset (tuple):
rotation (float):
ell (bool):
pts (bool):
rect (bool):
eig (bool):
ori (bool):
sifts (None):
References:
http://stackoverflow.com/questions/28401788/transforms-non-affine-patch
CommandLine:
python -m wbia.plottool.mpl_keypoint draw_keypoints --show
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.plottool.mpl_keypoint import * # NOQA
>>> from wbia.plottool.mpl_keypoint import _draw_patches, _draw_pts # NOQA
>>> import wbia.plottool as pt
>>> import vtool as vt
>>> imgBGR = vt.get_star_patch(jitter=True)
>>> H = np.array([[1, 0, 0], [.5, 2, 0], [0, 0, 1]])
>>> H = np.array([[.8, 0, 0], [0, .8, 0], [0, 0, 1]])
>>> H = None
>>> TAU = 2 * np.pi
>>> kpts_ = vt.make_test_image_keypoints(imgBGR, scale=.5, skew=2, theta=TAU / 8.0)
>>> scale_factor=1.0
>>> #offset=(0.0, -4.0)
>>> offset=(0.0, 0.0)
>>> rotation=0.0
>>> ell=True
>>> pts=True
>>> rect=True
>>> eig=True
>>> ori=True
>>> # make random sifts
>>> sifts = mpl_sift.testdata_sifts()
>>> siftkw = {}
>>> kwargs = dict(ori_color=[0, 1, 0], rect_color=[0, 0, 1],
>>> eig_color=[1, 1, 0], pts_size=.1)
>>> w, h = imgBGR.shape[0:2][::-1]
>>> imgBGR_ = imgBGR if H is None else vt.warpAffine(
>>> imgBGR, H, (int(w * .8), int(h * .8)))
>>> fig, ax = pt.imshow(imgBGR_ * 255)
>>> draw_keypoints(ax, kpts_, scale_factor, offset, rotation, ell, pts,
... rect, eig, ori, sifts, siftkw, H=H, **kwargs)
>>> pt.iup()
>>> pt.show_if_requested()
"""
import vtool.keypoint as ktool
if kpts_.shape[1] == 2:
# pad out structure if only xy given
kpts = np.zeros((len(kpts_), 6))
kpts[:, 0:2] = kpts_
kpts[:, 2] = 1
kpts[:, 4] = 1
kpts_ = kpts
if scale_factor is None:
scale_factor = 1.0
# print('[mpl_keypoint.draw_keypoints] kwargs = ' + ut.repr2(kwargs))
# ellipse and point properties
pts_size = kwargs.get('pts_size', 2)
pts_alpha = kwargs.get('pts_alpha', 1.0)
ell_alpha = kwargs.get('ell_alpha', 1.0)
ell_linewidth = kwargs.get('ell_linewidth', 2)
ell_color = kwargs.get('ell_color', None)
if ell_color is None:
ell_color = [1, 0, 0]
# colors
pts_color = kwargs.get('pts_color', ell_color)
rect_color = kwargs.get('rect_color', ell_color)
eig_color = kwargs.get('eig_color', ell_color)
ori_color = kwargs.get('ori_color', ell_color)
# linewidths
eig_linewidth = kwargs.get('eig_linewidth', ell_linewidth)
rect_linewidth = kwargs.get('rect_linewidth', ell_linewidth)
ori_linewidth = kwargs.get('ori_linewidth', ell_linewidth)
# Offset keypoints
assert len(kpts_) > 0, 'cannot draw no keypoints1'
kpts = ktool.offset_kpts(kpts_, offset, scale_factor)
assert len(kpts) > 0, 'cannot draw no keypoints2'
# Build list of keypoint shape transforms from unit circles to ellipes
invVR_aff2Ds = get_invVR_aff2Ds(kpts, H=H)
try:
if sifts is not None:
# SIFT descriptors
pass_props(
kwargs,
siftkw,
'bin_color',
'arm1_color',
'arm2_color',
'arm1_lw',
'arm2_lw',
'stroke',
'arm_alpha',
'arm_alpha',
'multicolored_arms',
)
mpl_sift.draw_sifts(ax, sifts, invVR_aff2Ds, **siftkw)
if rect:
# Bounding Rectangles
rect_patches = rectangle_actors(invVR_aff2Ds)
_draw_patches(ax, rect_patches, rect_color, ell_alpha,
rect_linewidth)
if ell:
# Keypoint shape
ell_patches = ellipse_actors(invVR_aff2Ds)
_draw_patches(ax, ell_patches, ell_color, ell_alpha, ell_linewidth)
if eig:
# Shape eigenvectors
eig_patches = eigenvector_actors(invVR_aff2Ds)
_draw_patches(ax, eig_patches, eig_color, ell_alpha, eig_linewidth)
if ori:
# Keypoint orientation
ori_patches = orientation_actors(kpts, H=H)
_draw_patches(ax, ori_patches, ori_color, ell_alpha, ori_linewidth,
ori_color)
if pts:
# Keypoint locations
_xs, _ys = ktool.get_xys(kpts)
if H is not None:
# adjust for homogrpahy
import vtool as vt
_xs, _ys = vt.transform_points_with_homography(
H, np.vstack((_xs, _ys)))
pts_patches = _draw_pts(ax, _xs, _ys, pts_size, pts_color,
pts_alpha)
if pts_patches is not None:
_draw_patches(ax, pts_patches, 'none', pts_alpha, pts_size,
pts_color)
except ValueError as ex:
ut.printex(ex, '\n[mplkp] !!! ERROR')
# print('_oris.shape = %r' % (_oris.shape,))
# print('_xs.shape = %r' % (_xs.shape,))
# print('_iv11s.shape = %r' % (_iv11s.shape,))
raise
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 get_affine_inliers(kpts1, kpts2, fm, fs, xy_thresh_sqrd, scale_thresh_sqrd,
ori_thresh):
"""
Estimates inliers deterministically using elliptical shapes
Compute all transforms from kpts1 to kpts2 (enumerate all hypothesis)
We transform from chip1 -> chip2
The determinants are squared keypoint scales
FROM PERDOCH 2009::
H = inv(Aj).dot(Rj.T).dot(Ri).dot(Ai)
H = inv(Aj).dot(Ai)
The input invVs = perdoch.invA's
CommandLine:
python -m vtool.spatial_verification --test-get_affine_inliers
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> import vtool.tests.dummy as dummy
>>> import vtool.keypoint as ktool
>>> kpts1, kpts2 = dummy.get_dummy_kpts_pair((100, 100))
>>> fm = dummy.make_dummy_fm(len(kpts1)).astype(np.int32)
>>> fs = np.ones(len(fm), dtype=np.float64)
>>> xy_thresh_sqrd = ktool.KPTS_DTYPE(.009) ** 2
>>> scale_thresh_sqrd = ktool.KPTS_DTYPE(2)
>>> ori_thresh = ktool.KPTS_DTYPE(TAU / 4)
>>> output = get_affine_inliers(kpts1, kpts2, fm, fs, xy_thresh_sqrd,
>>> scale_thresh_sqrd, ori_thresh)
>>> result = ut.hashstr(output)
>>> print(result)
89kz8nh6p+66t!+u
Ignore::
from vtool.spatial_verification import * # NOQA
import vtool.tests.dummy as dummy
import vtool.keypoint as ktool
kpts1, kpts2 = dummy.get_dummy_kpts_pair((100, 100))
a = kpts1[fm.T[0]]
b = kpts1.take(fm.T[0])
align = fm.dtype.itemsize * fm.shape[1]
align2 = [fm.dtype.itemsize, fm.dtype.itemsize]
viewtype1 = np.dtype(np.void, align)
viewtype2 = np.dtype(np.int32, align2)
c = np.ascontiguousarray(fm).view(viewtype1)
fm_view = np.ascontiguousarray(fm).view(viewtype1)
qfx = fm.view(np.dtype(np.int32 np.int32.itemsize))
dfx = fm.view(np.dtype(np.int32, np.int32.itemsize))
d = np.ascontiguousarray(c).view(viewtype2)
fm.view(np.dtype(np.void, align))
np.ascontiguousarray(fm).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))
"""
#http://ipython-books.github.io/featured-01/
kpts1_m = kpts1.take(fm.T[0], axis=0)
kpts2_m = kpts2.take(fm.T[1], axis=0)
# Get keypoints to project in matrix form
#invVR2s_m = ktool.get_invV_mats(kpts2_m, with_trans=True, with_ori=True)
#invVR1s_m = ktool.get_invV_mats(kpts1_m, with_trans=True, with_ori=True)
invVR2s_m = ktool.get_invVR_mats3x3(kpts2_m)
invVR1s_m = ktool.get_invVR_mats3x3(kpts1_m)
RV1s_m = ktool.invert_invV_mats(invVR1s_m) # 539 us
# BUILD ALL HYPOTHESIS TRANSFORMS: The transform from kp1 to kp2 is:
Aff_mats = matrix_multiply(invVR2s_m, RV1s_m)
# Get components to test projects against
xy2_m = ktool.get_xys(kpts2_m)
det2_m = ktool.get_sqrd_scales(kpts2_m)
ori2_m = ktool.get_oris(kpts2_m)
# SLOWER EQUIVALENT
# RV1s_m = ktool.get_V_mats(kpts1_m, with_trans=True, with_ori=True) # 5.2 ms
# xy2_m = ktool.get_invVR_mats_xys(invVR2s_m)
# ori2_m = ktool.get_invVR_mats_oris(invVR2s_m)
# assert np.all(ktool.get_oris(kpts2_m) == ktool.get_invVR_mats_oris(invVR2s_m))
# assert np.all(ktool.get_xys(kpts2_m) == ktool.get_invVR_mats_xys(invVR2s_m))
# The previous versions of this function were all roughly comparable.
# The for loop one was the slowest. I'm deciding to go with the one
# where there is no internal function definition. It was moderately faster,
# and it gives us access to profile that function
inliers_and_errors_list = [
_test_hypothesis_inliers(Aff, invVR1s_m, xy2_m, det2_m, ori2_m,
xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh)
for Aff in Aff_mats
]
aff_inliers_list = [tup[0] for tup in inliers_and_errors_list]
aff_errors_list = [tup[1] for tup in inliers_and_errors_list]
return aff_inliers_list, aff_errors_list, Aff_mats
def draw_keypoints(ax, kpts_, scale_factor=1.0, offset=(0.0, 0.0), rotation=0.0,
ell=True, pts=False, rect=False, eig=False, ori=False,
sifts=None, siftkw={}, H=None, **kwargs):
"""
draws keypoints extracted by pyhesaff onto a matplotlib axis
FIXME: There is probably a matplotlib bug here. If you specify two different
alphas in a collection, whatever the last alpha was gets applied to
everything
Args:
ax (mpl.Axes):
kpts (ndarray): keypoints [[x, y, a, c, d, theta], ...]
scale_factor (float):
offset (tuple):
rotation (float):
ell (bool):
pts (bool):
rect (bool):
eig (bool):
ori (bool):
sifts (None):
References:
http://stackoverflow.com/questions/28401788/using-homogeneous-transforms-non-affine-with-matplotlib-patches
CommandLine:
python -m plottool.mpl_keypoint --test-draw_keypoints --show
Example:
>>> # ENABLE_DOCTEST
>>> from plottool.mpl_keypoint import * # NOQA
>>> from plottool.mpl_keypoint import _draw_patches, _draw_pts # NOQA
>>> import plottool as pt
>>> import vtool as vt
>>> imgBGR = vt.get_star_patch(jitter=True)
>>> H = np.array([[1, 0, 0], [.5, 2, 0], [0, 0, 1]])
>>> H = None
>>> TAU = 2 * np.pi
>>> kpts_ = vt.make_test_image_keypoints(imgBGR, scale=.5, skew=2, theta=TAU / 8.0)
>>> scale_factor=1.0
>>> offset=(0.0, 0.0)
>>> rotation=0.0
>>> ell=True
>>> pts=True
>>> rect=True
>>> eig=True
>>> ori=True
>>> # make random sifts
>>> sifts = mpl_sift.testdata_sifts()
>>> siftkw = {}
>>> kwargs = dict(ori_color=[0, 1, 0], rect_color=[0, 0, 1], eig_color=[1, 1, 0], pts_size=.1)
>>> w, h = imgBGR.shape[0:2][::-1]
>>> imgBGR_ = imgBGR if H is None else vt.warpAffine(imgBGR, H, (w * 2, h * 2))
>>> fig, ax = pt.imshow(imgBGR_ * 255)
>>> draw_keypoints(ax, kpts_, scale_factor, offset, rotation, ell, pts,
... rect, eig, ori, sifts, siftkw, H=H, **kwargs)
>>> pt.iup()
>>> pt.show_if_requested()
"""
if kpts_.shape[1] == 2:
# pad out structure if only xy given
kpts = np.zeros((len(kpts_), 6))
kpts[:, 0:2] = kpts_
kpts[:, 2] = 1
kpts[:, 4] = 1
kpts_ = kpts
if scale_factor is None:
scale_factor = 1.0
#print('[mpl_keypoint.draw_keypoints] kwargs = ' + ut.dict_str(kwargs))
# ellipse and point properties
pts_size = kwargs.get('pts_size', 2)
pts_alpha = kwargs.get('pts_alpha', 1.0)
ell_alpha = kwargs.get('ell_alpha', 1.0)
ell_linewidth = kwargs.get('ell_linewidth', 2)
ell_color = kwargs.get('ell_color', None)
if ell_color is None:
ell_color = [1, 0, 0]
# colors
pts_color = kwargs.get('pts_color', ell_color)
rect_color = kwargs.get('rect_color', ell_color)
eig_color = kwargs.get('eig_color', ell_color)
ori_color = kwargs.get('ori_color', ell_color)
# linewidths
eig_linewidth = kwargs.get('eig_linewidth', ell_linewidth)
rect_linewidth = kwargs.get('rect_linewidth', ell_linewidth)
ori_linewidth = kwargs.get('ori_linewidth', ell_linewidth)
# Offset keypoints
assert len(kpts_) > 0, 'cannot draw no keypoints1'
kpts = ktool.offset_kpts(kpts_, offset, scale_factor)
assert len(kpts) > 0, 'cannot draw no keypoints2'
# Build list of keypoint shape transforms from unit circles to ellipes
invVR_aff2Ds = get_invVR_aff2Ds(kpts, H=H)
try:
if sifts is not None:
# SIFT descriptors
pass_props(kwargs, siftkw, 'bin_color', 'arm1_color', 'arm2_color',
'arm1_lw', 'arm2_lw', 'arm_alpha', 'arm_alpha', 'multicolored_arms')
mpl_sift.draw_sifts(ax, sifts, invVR_aff2Ds, **siftkw)
if rect:
# Bounding Rectangles
rect_patches = rectangle_actors(invVR_aff2Ds)
_draw_patches(ax, rect_patches, rect_color, ell_alpha, rect_linewidth)
if ell:
# Keypoint shape
ell_patches = ellipse_actors(invVR_aff2Ds)
_draw_patches(ax, ell_patches, ell_color, ell_alpha, ell_linewidth)
if eig:
# Shape eigenvectors
eig_patches = eigenvector_actors(invVR_aff2Ds)
_draw_patches(ax, eig_patches, eig_color, ell_alpha, eig_linewidth)
if ori:
# Keypoint orientation
ori_patches = orientation_actors(kpts, H=H)
_draw_patches(ax, ori_patches, ori_color, ell_alpha, ori_linewidth, ori_color)
if pts:
# Keypoint locations
_xs, _ys = ktool.get_xys(kpts)
if H is not None:
# adjust for homogrpahy
import vtool as vt
_xs, _ys = vt.transform_points_with_homography(H, np.vstack((_xs, _ys)))
pts_patches = _draw_pts(ax, _xs, _ys, pts_size, pts_color, pts_alpha)
if pts_patches is not None:
_draw_patches(ax, pts_patches, 'none', pts_alpha, pts_size, pts_color)
except ValueError as ex:
ut.printex(ex, '\n[mplkp] !!! ERROR')
#print('_oris.shape = %r' % (_oris.shape,))
#print('_xs.shape = %r' % (_xs.shape,))
#print('_iv11s.shape = %r' % (_iv11s.shape,))
raise
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 get_affine_inliers(kpts1, kpts2, fm, fs,
xy_thresh_sqrd,
scale_thresh_sqrd,
ori_thresh):
"""
Estimates inliers deterministically using elliptical shapes
Compute all transforms from kpts1 to kpts2 (enumerate all hypothesis)
We transform from chip1 -> chip2
The determinants are squared keypoint scales
FROM PERDOCH 2009::
H = inv(Aj).dot(Rj.T).dot(Ri).dot(Ai)
H = inv(Aj).dot(Ai)
The input invVs = perdoch.invA's
CommandLine:
python -m vtool.spatial_verification --test-get_affine_inliers
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> import vtool.tests.dummy as dummy
>>> import vtool.keypoint as ktool
>>> kpts1, kpts2 = dummy.get_dummy_kpts_pair((100, 100))
>>> fm = dummy.make_dummy_fm(len(kpts1)).astype(np.int32)
>>> fs = np.ones(len(fm), dtype=np.float64)
>>> xy_thresh_sqrd = ktool.KPTS_DTYPE(.009) ** 2
>>> scale_thresh_sqrd = ktool.KPTS_DTYPE(2)
>>> ori_thresh = ktool.KPTS_DTYPE(TAU / 4)
>>> output = get_affine_inliers(kpts1, kpts2, fm, fs, xy_thresh_sqrd,
>>> scale_thresh_sqrd, ori_thresh)
>>> result = ut.hashstr(output)
>>> print(result)
89kz8nh6p+66t!+u
Ignore::
from vtool.spatial_verification import * # NOQA
import vtool.tests.dummy as dummy
import vtool.keypoint as ktool
kpts1, kpts2 = dummy.get_dummy_kpts_pair((100, 100))
a = kpts1[fm.T[0]]
b = kpts1.take(fm.T[0])
align = fm.dtype.itemsize * fm.shape[1]
align2 = [fm.dtype.itemsize, fm.dtype.itemsize]
viewtype1 = np.dtype(np.void, align)
viewtype2 = np.dtype(np.int32, align2)
c = np.ascontiguousarray(fm).view(viewtype1)
fm_view = np.ascontiguousarray(fm).view(viewtype1)
qfx = fm.view(np.dtype(np.int32 np.int32.itemsize))
dfx = fm.view(np.dtype(np.int32, np.int32.itemsize))
d = np.ascontiguousarray(c).view(viewtype2)
fm.view(np.dtype(np.void, align))
np.ascontiguousarray(fm).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))
"""
#http://ipython-books.github.io/featured-01/
kpts1_m = kpts1.take(fm.T[0], axis=0)
kpts2_m = kpts2.take(fm.T[1], axis=0)
# Get keypoints to project in matrix form
#invVR2s_m = ktool.get_invV_mats(kpts2_m, with_trans=True, with_ori=True)
#invVR1s_m = ktool.get_invV_mats(kpts1_m, with_trans=True, with_ori=True)
invVR2s_m = ktool.get_invVR_mats3x3(kpts2_m)
invVR1s_m = ktool.get_invVR_mats3x3(kpts1_m)
RV1s_m = ktool.invert_invV_mats(invVR1s_m) # 539 us
# BUILD ALL HYPOTHESIS TRANSFORMS: The transform from kp1 to kp2 is:
Aff_mats = matrix_multiply(invVR2s_m, RV1s_m)
# Get components to test projects against
xy2_m = ktool.get_xys(kpts2_m)
det2_m = ktool.get_sqrd_scales(kpts2_m)
ori2_m = ktool.get_oris(kpts2_m)
# SLOWER EQUIVALENT
# RV1s_m = ktool.get_V_mats(kpts1_m, with_trans=True, with_ori=True) # 5.2 ms
# xy2_m = ktool.get_invVR_mats_xys(invVR2s_m)
# ori2_m = ktool.get_invVR_mats_oris(invVR2s_m)
# assert np.all(ktool.get_oris(kpts2_m) == ktool.get_invVR_mats_oris(invVR2s_m))
# assert np.all(ktool.get_xys(kpts2_m) == ktool.get_invVR_mats_xys(invVR2s_m))
# The previous versions of this function were all roughly comparable.
# The for loop one was the slowest. I'm deciding to go with the one
# where there is no internal function definition. It was moderately faster,
# and it gives us access to profile that function
inliers_and_errors_list = [_test_hypothesis_inliers(Aff, invVR1s_m, xy2_m,
det2_m, ori2_m,
xy_thresh_sqrd,
scale_thresh_sqrd,
ori_thresh)
for Aff in Aff_mats]
aff_inliers_list = [tup[0] for tup in inliers_and_errors_list]
aff_errors_list = [tup[1] for tup in inliers_and_errors_list]
return aff_inliers_list, aff_errors_list, Aff_mats