def test_invVR_sqrd_scale():
invVRs = np.random.rand(4, 3, 3).astype(np.float64)
out1 = ktool.get_invVR_mats_sqrd_scale(invVRs)
out2 = ktool.get_invVR_mats_sqrd_scale_cython(invVRs)
print("out1 = %r" % (out1,))
print("out2 = %r" % (out2,))
assert np.allclose(out1, out2), "cython invV det version has diverged"
return locals()
def test_invVR_sqrd_scale():
invVRs = np.random.rand(4, 3, 3).astype(np.float64)
out1 = ktool.get_invVR_mats_sqrd_scale(invVRs)
out2 = ktool.get_invVR_mats_sqrd_scale_cython(invVRs)
print('out1 = %r' % (out1, ))
print('out2 = %r' % (out2, ))
assert np.allclose(out1, out2), 'cython invV det version has diverged'
return locals()
def _test_hypothesis_inliers(Aff, invVR1s_m, xy2_m, det2_m, ori2_m,
xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh):
"""
Critical section code. Inner loop of _test_hypothesis_inliers
CommandLine:
python -m vtool.spatial_verification --test-_test_hypothesis_inliers
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> from vtool.spatial_verification import _test_hypothesis_inliers # NOQA
>>> import vtool.tests.dummy as dummy
>>> import vtool.keypoint as ktool
>>> _kw1 = dict(seed=12, damping=1.2, wh_stride=(30, 30))
>>> _kw2 = dict(seed=24, damping=1.6, wh_stride=(30, 30))
>>> kpts1 = dummy.perterbed_grid_kpts(**_kw1).astype(np.float64)
>>> kpts2 = dummy.perterbed_grid_kpts(**_kw2).astype(np.float64)
>>> fm = dummy.make_dummy_fm(len(kpts1)).astype(np.int32)
>>> kpts1_m = kpts1[fm.T[0]]
>>> kpts2_m = kpts2[fm.T[1]]
>>> xy_thresh_sqrd = np.float64(.009) ** 2
>>> scale_thresh_sqrd = np.float64(2)
>>> ori_thresh = np.float64(TAU / 4)
>>> # Get keypoints to project in matrix form
>>> #invVR1s_m = ktool.get_invV_mats(kpts1_m, with_trans=True, with_ori=True)
>>> #print(invVR1s_m[0])
>>> invVR1s_m = ktool.get_invVR_mats3x3(kpts1_m)
>>> RV1s_m = ktool.get_RV_mats_3x3(kpts1_m)
>>> invVR2s_m = ktool.get_invVR_mats3x3(kpts2_m)
>>> # The transform from kp1 to kp2 is given as:
>>> Aff_mats = matrix_multiply(invVR2s_m, RV1s_m)
>>> Aff = Aff_mats[0]
>>> # Get components to test projects against
>>> xy2_m = ktool.get_invVR_mats_xys(invVR2s_m)
>>> det2_m = ktool.get_sqrd_scales(kpts2_m)
>>> ori2_m = ktool.get_invVR_mats_oris(invVR2s_m)
>>> output = _test_hypothesis_inliers(Aff, invVR1s_m, xy2_m, det2_m, ori2_m, xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh)
>>> result = ut.hashstr(output)
>>> print(result)
+%q&%je52nlyli5&
Timeit:
%timeit xy_err < xy_thresh_sqrd
%timeit np.less(xy_err, xy_thresh_sqrd)
"""
# Map keypoints from image 1 onto image 2
invVR1s_mt = matrix_multiply(Aff, invVR1s_m)
# Get projection components
_xy1_mt = ktool.get_invVR_mats_xys(invVR1s_mt)
_det1_mt = ktool.get_invVR_mats_sqrd_scale(invVR1s_mt)
_ori1_mt = ktool.get_invVR_mats_oris(invVR1s_mt)
## Check for projection errors
xy_err = dtool.L2_sqrd(xy2_m.T, _xy1_mt.T, dtype=SV_DTYPE)
scale_err = dtool.det_distance(_det1_mt, det2_m)
ori_err = dtool.ori_distance(_ori1_mt, ori2_m)
# Mark keypoints which are inliers to this hypothosis
xy_inliers_flag = np.less(xy_err, xy_thresh_sqrd)
scale_inliers_flag = np.less(scale_err, scale_thresh_sqrd)
ori_inliers_flag = np.less(ori_err, ori_thresh)
#np.logical_and(xy_inliers_flag, scale_inliers_flag)
# TODO Add uniqueness of matches constraint
#hypo_inliers_flag = np.empty(xy_inliers_flag.size, dtype=np.bool)
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)
#hypo_inliers_flag = np.logical_and.reduce(
# [xy_inliers_flag, ori_inliers_flag, scale_inliers_flag])
# this is also slower
hypo_inliers = np.where(hypo_inliers_flag)[0]
hypo_errors = (xy_err, ori_err, scale_err)
return hypo_inliers, hypo_errors
def _test_hypothesis_inliers(Aff, invVR1s_m, xy2_m, det2_m, ori2_m,
xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh):
"""
Critical section code. Inner loop of _test_hypothesis_inliers
CommandLine:
python -m vtool.spatial_verification --test-_test_hypothesis_inliers
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.spatial_verification import * # NOQA
>>> from vtool.spatial_verification import _test_hypothesis_inliers # NOQA
>>> import vtool.tests.dummy as dummy
>>> import vtool.keypoint as ktool
>>> _kw1 = dict(seed=12, damping=1.2, wh_stride=(30, 30))
>>> _kw2 = dict(seed=24, damping=1.6, wh_stride=(30, 30))
>>> kpts1 = dummy.perterbed_grid_kpts(**_kw1).astype(np.float64)
>>> kpts2 = dummy.perterbed_grid_kpts(**_kw2).astype(np.float64)
>>> fm = dummy.make_dummy_fm(len(kpts1)).astype(np.int32)
>>> kpts1_m = kpts1[fm.T[0]]
>>> kpts2_m = kpts2[fm.T[1]]
>>> xy_thresh_sqrd = np.float64(.009) ** 2
>>> scale_thresh_sqrd = np.float64(2)
>>> ori_thresh = np.float64(TAU / 4)
>>> # Get keypoints to project in matrix form
>>> #invVR1s_m = ktool.get_invV_mats(kpts1_m, with_trans=True, with_ori=True)
>>> #print(invVR1s_m[0])
>>> invVR1s_m = ktool.get_invVR_mats3x3(kpts1_m)
>>> RV1s_m = ktool.get_RV_mats_3x3(kpts1_m)
>>> invVR2s_m = ktool.get_invVR_mats3x3(kpts2_m)
>>> # The transform from kp1 to kp2 is given as:
>>> Aff_mats = matrix_multiply(invVR2s_m, RV1s_m)
>>> Aff = Aff_mats[0]
>>> # Get components to test projects against
>>> xy2_m = ktool.get_invVR_mats_xys(invVR2s_m)
>>> det2_m = ktool.get_sqrd_scales(kpts2_m)
>>> ori2_m = ktool.get_invVR_mats_oris(invVR2s_m)
>>> output = _test_hypothesis_inliers(Aff, invVR1s_m, xy2_m, det2_m, ori2_m, xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh)
>>> result = ut.hashstr(output)
>>> print(result)
+%q&%je52nlyli5&
Timeit:
%timeit xy_err < xy_thresh_sqrd
%timeit np.less(xy_err, xy_thresh_sqrd)
"""
# Map keypoints from image 1 onto image 2
invVR1s_mt = matrix_multiply(Aff, invVR1s_m)
# Get projection components
_xy1_mt = ktool.get_invVR_mats_xys(invVR1s_mt)
_det1_mt = ktool.get_invVR_mats_sqrd_scale(invVR1s_mt)
_ori1_mt = ktool.get_invVR_mats_oris(invVR1s_mt)
## Check for projection errors
xy_err = dtool.L2_sqrd(xy2_m.T, _xy1_mt.T, dtype=SV_DTYPE)
scale_err = dtool.det_distance(_det1_mt, det2_m)
ori_err = dtool.ori_distance(_ori1_mt, ori2_m)
# Mark keypoints which are inliers to this hypothosis
xy_inliers_flag = np.less(xy_err, xy_thresh_sqrd)
scale_inliers_flag = np.less(scale_err, scale_thresh_sqrd)
ori_inliers_flag = np.less(ori_err, ori_thresh)
#np.logical_and(xy_inliers_flag, scale_inliers_flag)
# TODO Add uniqueness of matches constraint
#hypo_inliers_flag = np.empty(xy_inliers_flag.size, dtype=np.bool)
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)
#hypo_inliers_flag = np.logical_and.reduce(
# [xy_inliers_flag, ori_inliers_flag, scale_inliers_flag])
# this is also slower
hypo_inliers = np.where(hypo_inliers_flag)[0]
hypo_errors = (xy_err, ori_err, scale_err)
return hypo_inliers, hypo_errors