def test_non_orthogonal_input_non_mult_4():
Y = colifilt(mandrill, (1,0,0,1), (1,0,0,1))
assert Y.shape == (mandrill.shape[0]*2, mandrill.shape[1])
Z = colifilt_gold(mandrill, (1,0,0,1), (1,0,0,1))
assert_almost_equal(Y,Z)
def test_qshift():
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
y = colifilt(mandrill, h1b, h1b)
z = colifilt_gold(mandrill, h1b, h1a)
assert_almost_equal(y, z)
def test_good_input_size():
Y = colifilt(mandrill[:,:511], (-1,1), (1,-1))
Z = colifilt_gold(mandrill[:,:511], (-1,1), (1,-1))
assert_almost_equal(Y,Z)
def test_output_size_non_mult_4():
Y = colifilt(mandrill, (-1,0,0,1), (1,0,0,-1))
assert Y.shape == (mandrill.shape[0]*2, mandrill.shape[1])
Z = colifilt_gold(mandrill, (-1,0,0,1), (1,0,0,-1))
assert_almost_equal(Y,Z)
def test_zero_input():
Y = colifilt(np.zeros_like(mandrill), (-1,1), (1,-1))
assert np.all(Y[:0] == 0)
def test_bad_input_size():
with raises(ValueError):
colifilt(mandrill[:511,:], (-1,1), (1,-1))
def test_odd_filter():
with raises(ValueError):
colifilt(mandrill, (-1,2,-1), (-1,2,1))
def test_different_size_h():
colifilt(lena, (-1,2,1), (-0.5,-1,2,-1,0.5))
def _level2_ifm(Yl, Yh, g0a, g0b, g1a, g1b, ext_mode, prev_level_size):
"""Perform level 2 or greater of the 3d inverse transform.
"""
# Create work area
work = np.zeros(np.asanyarray(Yl.shape) * 2, dtype=Yl.dtype)
# Form some useful slices
s0a = slice(None, work.shape[0] >> 1)
s1a = slice(None, work.shape[1] >> 1)
s2a = slice(None, work.shape[2] >> 1)
s0b = slice(work.shape[0] >> 1, None)
s1b = slice(work.shape[1] >> 1, None)
s2b = slice(work.shape[2] >> 1, None)
# Assign regions of work area
work[s0a, s1a, s2a] = Yl
work[s0a, s1b, s2a] = c2cube(Yh[:, :, :, 0:4])
work[s0b, s1a, s2a] = c2cube(Yh[:, :, :, 4:8])
work[s0b, s1b, s2a] = c2cube(Yh[:, :, :, 8:12])
work[s0a, s1a, s2b] = c2cube(Yh[:, :, :, 12:16])
work[s0a, s1b, s2b] = c2cube(Yh[:, :, :, 16:20])
work[s0b, s1a, s2b] = c2cube(Yh[:, :, :, 20:24])
work[s0b, s1b, s2b] = c2cube(Yh[:, :, :, 24:28])
for f in xrange(work.shape[2]):
# Do even Qshift filters on rows.
y = colifilt(work[:, s1a, f].T, g0b, g0a) + colifilt(
work[:, s1b, f].T, g1b, g1a)
# Do even Qshift filters on columns.
work[:, :, f] = colifilt(y[:, s0a].T, g0b, g0a) + colifilt(
y[:, s0b].T, g1b, g1a)
for f in xrange(work.shape[1]):
# Do even Qshift filters on 3rd dim.
y = work[:, f, :].T
work[:, f, :] = (colifilt(y[s2a, :], g0b, g0a) +
colifilt(y[s2b, :], g1b, g1a)).T
# Now check if the size of the previous level is exactly twice the size of
# the current level. If YES, this means we have not done the extension in
# the previous level. If NO, then we have to remove the appended row /
# column / frame from the previous level DTCWT coefs.
prev_level_size = np.asarray(prev_level_size)
curr_level_size = np.asarray(Yh.shape)
if ext_mode == 4:
if curr_level_size[0] * 2 != prev_level_size[0]:
# Discard the top and bottom rows
work = work[1:-1, :, :]
if curr_level_size[1] * 2 != prev_level_size[1]:
# Discard the top and bottom rows
work = work[:, 1:-1, :]
if curr_level_size[2] * 2 != prev_level_size[2]:
# Discard the top and bottom rows
work = work[:, :, 1:-1]
elif ext_mode == 8:
if curr_level_size[0] * 2 != prev_level_size[0]:
# Discard the top and bottom rows
work = work[2:-2, :, :]
if curr_level_size[1] * 2 != prev_level_size[1]:
# Discard the top and bottom rows
work = work[:, 2:-2, :]
if curr_level_size[2] * 2 != prev_level_size[2]:
# Discard the top and bottom rows
work = work[:, :, 2:-2]
return work
def test_qshift_even_input():
h1b = np.array((-0.25, 0.5, 0.5, -0.25))
h1a = h1b[::-1]
y = colifilt(mandrill, h1b, h1a)
z = colifilt_gold(mandrill, h1b, h1a)
assert_almost_equal(y, z)
def test_non_orthogonal_input_non_mult_4():
Y = colifilt(lena, (1,0,0,1), (1,0,0,1))
assert Y.shape == (lena.shape[0]*2, lena.shape[1])
Z = colifilt_gold(lena, (1,0,0,1), (1,0,0,1))
assert_almost_equal(Y,Z)
def test_output_size_non_mult_4():
Y = colifilt(lena, (-1,0,0,1), (1,0,0,-1))
assert Y.shape == (lena.shape[0]*2, lena.shape[1])
Z = colifilt_gold(lena, (-1,0,0,1), (1,0,0,-1))
assert_almost_equal(Y,Z)
def test_bad_input_size():
colifilt(lena[:511,:], (-1,1), (1,-1))
def test_qshift_odd_len_input_2():
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
y = colifilt(mandrill, h1a[1:-1], h1b[1:-1])
z = colifilt_gold(mandrill, h1a[1:-1], h1b[1:-1])
assert_almost_equal(y, z)
def test_different_size_h():
with raises(ValueError):
colifilt(mandrill, (-1,2,1), (-0.5,-1,2,-1,0.5))
def _level2_ifm(Yl, Yh, g0a, g0b, g1a, g1b, ext_mode, prev_level_size):
"""Perform level 2 or greater of the 3d inverse transform.
"""
# Create work area
work = np.zeros(np.asanyarray(Yl.shape)*2, dtype=Yl.dtype)
# Form some useful slices
s0a = slice(None, work.shape[0] >> 1)
s1a = slice(None, work.shape[1] >> 1)
s2a = slice(None, work.shape[2] >> 1)
s0b = slice(work.shape[0] >> 1, None)
s1b = slice(work.shape[1] >> 1, None)
s2b = slice(work.shape[2] >> 1, None)
# Assign regions of work area
work[s0a, s1a, s2a] = Yl
work[s0a, s1b, s2a] = c2cube(Yh[:,:,:, 0:4 ])
work[s0b, s1a, s2a] = c2cube(Yh[:,:,:, 4:8 ])
work[s0b, s1b, s2a] = c2cube(Yh[:,:,:, 8:12])
work[s0a, s1a, s2b] = c2cube(Yh[:,:,:,12:16])
work[s0a, s1b, s2b] = c2cube(Yh[:,:,:,16:20])
work[s0b, s1a, s2b] = c2cube(Yh[:,:,:,20:24])
work[s0b, s1b, s2b] = c2cube(Yh[:,:,:,24:28])
for f in xrange(work.shape[2]):
# Do even Qshift filters on rows.
y = colifilt(work[:, s1a, f].T, g0b, g0a) + colifilt(work[:, s1b, f].T, g1b, g1a)
# Do even Qshift filters on columns.
work[:, :, f] = colifilt(y[:, s0a].T, g0b, g0a) + colifilt(y[:,s0b].T, g1b, g1a)
for f in xrange(work.shape[1]):
# Do even Qshift filters on 3rd dim.
y = work[:, f, :].T
work[:, f, :] = (colifilt(y[s2a, :], g0b, g0a) + colifilt(y[s2b, :], g1b, g1a)).T
# Now check if the size of the previous level is exactly twice the size of
# the current level. If YES, this means we have not done the extension in
# the previous level. If NO, then we have to remove the appended row /
# column / frame from the previous level DTCWT coefs.
prev_level_size = np.asarray(prev_level_size)
curr_level_size = np.asarray(Yh.shape)
if ext_mode == 4:
if curr_level_size[0] * 2 != prev_level_size[0]:
# Discard the top and bottom rows
work = work[1:-1,:,:]
if curr_level_size[1] * 2 != prev_level_size[1]:
# Discard the top and bottom rows
work = work[:,1:-1,:]
if curr_level_size[2] * 2 != prev_level_size[2]:
# Discard the top and bottom rows
work = work[:,:,1:-1]
elif ext_mode == 8:
if curr_level_size[0] * 2 != prev_level_size[0]:
# Discard the top and bottom rows
work = work[2:-2,:,:]
if curr_level_size[1] * 2 != prev_level_size[1]:
# Discard the top and bottom rows
work = work[:,2:-2,:]
if curr_level_size[2] * 2 != prev_level_size[2]:
# Discard the top and bottom rows
work = work[:,:,2:-2]
return work
def test_odd_filter():
colifilt(lena, (-1,2,-1), (-1,2,1))
