def test_sver_wrapper():
"""
Test to ensure cpp and python agree and that cpp is faster
CommandLine:
python -m vtool.sver_c_wrapper --test-test_sver_wrapper
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --rebuild-sver
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --dummy
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=easy2.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=hard3.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=carl.jpg --fname2=hard3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.sver_c_wrapper import * # NOQA
>>> test_sver_wrapper()
Ignore:
%timeit call_python_version(*args)
%timeit get_affine_inliers_cpp(*args)
"""
import vtool.spatial_verification as sver
import vtool.tests.dummy as dummy
xy_thresh_sqrd = ktool.KPTS_DTYPE(.4)
scale_thresh_sqrd = ktool.KPTS_DTYPE(2.0)
ori_thresh = ktool.KPTS_DTYPE(TAU / 4.0)
keys = 'xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh'.split(', ')
print(ut.dict_str(ut.dict_subset(locals(), keys)))
def report_errors():
pass
if ut.get_argflag('--dummy'):
testtup = dummy.testdata_dummy_matches()
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
fm_input = fm_input.astype(fm_dtype)
#fm_input = fm_input[0:10].astype(fm_dtype)
#fs_input = fs_input[0:10].astype(np.float32)
else:
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
testtup = dummy.testdata_ratio_matches(fname1, fname2)
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
# pack up call to aff hypothesis
import vtool as vt
import scipy.stats.mstats
scales1 = vt.get_scales(kpts1.take(fm_input.T[0], axis=0))
scales2 = vt.get_scales(kpts2.take(fm_input.T[1], axis=0))
#fs_input = 1 / scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
fs_input = scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
print('fs_input = ' + ut.numpy_str(fs_input))
#fs_input[0:-9] = 0
#fs_input = np.ones(len(fm_input), dtype=fs_dtype)
#ut.embed()
#fs_input = scales1 * scales2
args = (kpts1, kpts2, fm_input, fs_input, xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh)
ex_list = []
try:
with ut.Indenter('[TEST1] '):
inlier_tup = vt.compare_implementations(
sver.get_affine_inliers,
get_affine_inliers_cpp,
args, lbl1='py', lbl2='c',
output_lbl=('aff_inliers_list', 'aff_errors_list', 'Aff_mats')
)
out_inliers, out_errors, out_mats = inlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
try:
import functools
with ut.Indenter('[TEST2] '):
bestinlier_tup = vt.compare_implementations(
functools.partial(sver.get_best_affine_inliers, forcepy=True),
get_best_affine_inliers_cpp,
args, show_output=True, lbl1='py', lbl2='c',
output_lbl=('bestinliers', 'besterror', 'bestmat')
)
bestinliers, besterror, bestmat = bestinlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
if len(ex_list) > 0:
raise AssertionError('some tests failed. see previous stdout')
#num_inliers_list = np.array(map(len, out_inliers_c))
#best_argx = num_inliers_list.argmax()
##best_inliers_py = out_inliers_py[best_argx]
#best_inliers_c = out_inliers_c[best_argx]
if ut.show_was_requested():
import plottool as pt
fm_output = fm_input.take(bestinliers, axis=0)
fnum = pt.next_fnum()
pt.figure(fnum=fnum, doclf=True, docla=True)
pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_input, ell_linewidth=5, fnum=fnum, pnum=(2, 1, 1))
pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_output, ell_linewidth=5, fnum=fnum, pnum=(2, 1, 2))
pt.show_if_requested()
def test_sver_wrapper():
"""
Test to ensure cpp and python agree and that cpp is faster
CommandLine:
python -m vtool.sver_c_wrapper --test-test_sver_wrapper
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --rebuild-sver
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --dummy
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=easy2.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=hard3.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=carl.jpg --fname2=hard3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.sver_c_wrapper import * # NOQA
>>> test_sver_wrapper()
Ignore:
%timeit call_python_version(*args)
%timeit get_affine_inliers_cpp(*args)
"""
import vtool.spatial_verification as sver
import vtool.tests.dummy as dummy
xy_thresh_sqrd = ktool.KPTS_DTYPE(.4)
scale_thresh_sqrd = ktool.KPTS_DTYPE(2.0)
ori_thresh = ktool.KPTS_DTYPE(TAU / 4.0)
keys = 'xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh'.split(', ')
print(ut.dict_str(ut.dict_subset(locals(), keys)))
def report_errors():
pass
if ut.get_argflag('--dummy'):
testtup = dummy.testdata_dummy_matches()
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
fm_input = fm_input.astype(fm_dtype)
#fm_input = fm_input[0:10].astype(fm_dtype)
#fs_input = fs_input[0:10].astype(np.float32)
else:
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
testtup = dummy.testdata_ratio_matches(fname1, fname2)
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
# pack up call to aff hypothesis
import vtool as vt
import scipy.stats.mstats
scales1 = vt.get_scales(kpts1.take(fm_input.T[0], axis=0))
scales2 = vt.get_scales(kpts2.take(fm_input.T[1], axis=0))
#fs_input = 1 / scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
fs_input = scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
print('fs_input = ' + ut.numpy_str(fs_input))
#fs_input[0:-9] = 0
#fs_input = np.ones(len(fm_input), dtype=fs_dtype)
#ut.embed()
#fs_input = scales1 * scales2
args = (kpts1, kpts2, fm_input, fs_input, xy_thresh_sqrd,
scale_thresh_sqrd, ori_thresh)
ex_list = []
try:
with ut.Indenter('[TEST1] '):
inlier_tup = vt.compare_implementations(
sver.get_affine_inliers,
get_affine_inliers_cpp,
args,
lbl1='py',
lbl2='c',
output_lbl=('aff_inliers_list', 'aff_errors_list', 'Aff_mats'))
out_inliers, out_errors, out_mats = inlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
try:
import functools
with ut.Indenter('[TEST2] '):
bestinlier_tup = vt.compare_implementations(
functools.partial(sver.get_best_affine_inliers, forcepy=True),
get_best_affine_inliers_cpp,
args,
show_output=True,
lbl1='py',
lbl2='c',
output_lbl=('bestinliers', 'besterror', 'bestmat'))
bestinliers, besterror, bestmat = bestinlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
if len(ex_list) > 0:
raise AssertionError('some tests failed. see previous stdout')
#num_inliers_list = np.array(map(len, out_inliers_c))
#best_argx = num_inliers_list.argmax()
##best_inliers_py = out_inliers_py[best_argx]
#best_inliers_c = out_inliers_c[best_argx]
if ut.show_was_requested():
import plottool as pt
fm_output = fm_input.take(bestinliers, axis=0)
fnum = pt.next_fnum()
pt.figure(fnum=fnum, doclf=True, docla=True)
pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm_input,
ell_linewidth=5,
fnum=fnum,
pnum=(2, 1, 1))
pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm_output,
ell_linewidth=5,
fnum=fnum,
pnum=(2, 1, 2))
pt.show_if_requested()
def understanding_pseudomax_props(mode=2):
"""
Function showing some properties of distances between normalized pseudomax vectors
CommandLine:
python -m vtool.distance --test-understanding_pseudomax_props
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.distance import * # NOQA
>>> for mode in [0, 1, 2, 3]:
... print('+---')
... print('mode = %r' % (mode,))
... result = understanding_pseudomax_props(mode)
... print('L___')
>>> print(result)
"""
import vtool as vt
pseudo_max = 512
rng = np.random.RandomState(0)
num = 10
if mode == 0:
dim = 2
p1_01 = (vt.normalize_rows(rng.rand(num, dim)))
p2_01 = (vt.normalize_rows(rng.rand(num, dim)))
elif mode == 1:
p1_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
p2_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
elif mode == 2:
# Build theoretically maximally distant normalized vectors (type 1)
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0::2] = 1
p1_01[:, 1::2] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
elif mode == 3:
# Build theoretically maximally distant vectors (type 2)
# This mode will clip if cast to uint8, thus failing the test
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0] = 1
p1_01[:, 1:] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
pass
print('ndims = %r' % (p1_01.shape[1]))
p1_01 = p1_01.astype(TEMP_VEC_DTYPE)
p2_01 = p2_01.astype(TEMP_VEC_DTYPE)
p1_256 = p1_01 * pseudo_max
p2_256 = p2_01 * pseudo_max
dist_sqrd_01 = vt.L2_sqrd(p1_01, p2_01)
dist_sqrd_256 = vt.L2_sqrd(p1_256, p2_256)
dist_01 = np.sqrt(dist_sqrd_01)
dist_256 = np.sqrt(dist_sqrd_256)
print('dist_sqrd_01 = %s' % (ut.numpy_str(dist_sqrd_01, precision=2), ))
print('dist_sqrd_256 = %s' % (ut.numpy_str(dist_sqrd_256, precision=2), ))
print('dist_01 = %s' % (ut.numpy_str(dist_01, precision=2), ))
print('dist_256 = %s' % (ut.numpy_str(dist_256, precision=2), ))
print('--')
print('sqrt(2) = %f' % (np.sqrt(2)))
print('--')
assert np.all(dist_01 == vt.L2(p1_01, p2_01))
assert np.all(dist_256 == vt.L2(p1_256, p2_256))
const_sqrd = dist_sqrd_256 / dist_sqrd_01
const = dist_256 / dist_01
print('const = %r' % (const[0], ))
print('const_sqrd = %r' % (const_sqrd[0], ))
print('1 / const = %r' % (1 / const[0], ))
print('1 / const_sqrd = %r' % (1 / const_sqrd[0], ))
assert ut.allsame(const)
assert ut.allsame(const_sqrd)
assert np.all(const == np.sqrt(const_sqrd))
# Assert that distance conversions work
assert np.all(dist_256 / const == dist_01)
assert np.all(dist_sqrd_256 / const_sqrd == dist_sqrd_01)
print('Conversions work')
print('Maximal L2 distance between any two NON-NEGATIVE L2-NORMALIZED'
' vectors should always be sqrt(2)')
def understanding_pseudomax_props(mode=2):
"""
Function showing some properties of distances between normalized pseudomax vectors
CommandLine:
python -m vtool.distance --test-understanding_pseudomax_props
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.distance import * # NOQA
>>> for mode in [0, 1, 2, 3]:
... print('+---')
... print('mode = %r' % (mode,))
... result = understanding_pseudomax_props(mode)
... print('L___')
>>> print(result)
"""
import vtool as vt
pseudo_max = 512
rng = np.random.RandomState(0)
num = 10
if mode == 0:
dim = 2
p1_01 = (vt.normalize_rows(rng.rand(num, dim)))
p2_01 = (vt.normalize_rows(rng.rand(num, dim)))
elif mode == 1:
p1_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
p2_01 = vt.dummy.testdata_dummy_sift(num, rng) / pseudo_max
elif mode == 2:
# Build theoretically maximally distant normalized vectors (type 1)
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0::2] = 1
p1_01[:, 1::2] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
elif mode == 3:
# Build theoretically maximally distant vectors (type 2)
# This mode will clip if cast to uint8, thus failing the test
dim = 128
p1_01 = np.zeros((1, dim))
p2_01 = np.zeros((1, dim))
p2_01[:, 0] = 1
p1_01[:, 1:] = 1
p1_01 = vt.normalize_rows(p1_01)
p2_01 = vt.normalize_rows(p2_01)
pass
print('ndims = %r' % (p1_01.shape[1]))
p1_01 = p1_01.astype(TEMP_VEC_DTYPE)
p2_01 = p2_01.astype(TEMP_VEC_DTYPE)
p1_256 = p1_01 * pseudo_max
p2_256 = p2_01 * pseudo_max
dist_sqrd_01 = vt.L2_sqrd(p1_01, p2_01)
dist_sqrd_256 = vt.L2_sqrd(p1_256, p2_256)
dist_01 = np.sqrt(dist_sqrd_01)
dist_256 = np.sqrt(dist_sqrd_256)
print('dist_sqrd_01 = %s' % (ut.numpy_str(dist_sqrd_01, precision=2),))
print('dist_sqrd_256 = %s' % (ut.numpy_str(dist_sqrd_256, precision=2),))
print('dist_01 = %s' % (ut.numpy_str(dist_01, precision=2),))
print('dist_256 = %s' % (ut.numpy_str(dist_256, precision=2),))
print('--')
print('sqrt(2) = %f' % (np.sqrt(2)))
print('--')
assert np.all(dist_01 == vt.L2(p1_01, p2_01))
assert np.all(dist_256 == vt.L2(p1_256, p2_256))
const_sqrd = dist_sqrd_256 / dist_sqrd_01
const = dist_256 / dist_01
print('const = %r' % (const[0],))
print('const_sqrd = %r' % (const_sqrd[0],))
print('1 / const = %r' % (1 / const[0],))
print('1 / const_sqrd = %r' % (1 / const_sqrd[0],))
assert ut.allsame(const)
assert ut.allsame(const_sqrd)
assert np.all(const == np.sqrt(const_sqrd))
# Assert that distance conversions work
assert np.all(dist_256 / const == dist_01)
assert np.all(dist_sqrd_256 / const_sqrd == dist_sqrd_01)
print('Conversions work')
print('Maximal L2 distance between any two NON-NEGATIVE L2-NORMALIZED'
' vectors should always be sqrt(2)')
