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
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