def test_specific_wavelet():
a = dtwavexfm2_np(mandrill,
biort=biort('antonini'),
qshift=qshift('qshift_06'))
b = dtwavexfm2_cl(mandrill,
biort=biort('antonini'),
qshift=qshift('qshift_06'))
_compare_transforms(a, b)
def test_equal_numpy_qshift1():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
ref = np_coldfilt(mandrill.T, ha, hb).T
y_op = rowdfilt(mandrill_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_modified():
a = dtwavexfm2_np(mandrill,
biort=biort('near_sym_b_bp'),
qshift=qshift('qshift_b_bp'))
b = dtwavexfm2_cl(mandrill,
biort=biort('near_sym_b_bp'),
qshift=qshift('qshift_b_bp'))
_compare_transforms(a, b)
def test_equal_numpy_qshift1():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
ref = np_colifilt(mandrill, ha, hb)
y_op = colifilt(mandrill_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_small_in():
ha = qshift('qshift_b')[0]
hb = qshift('qshift_b')[1]
im = mandrill[0:4,0:4]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_coldfilt(im, ha, hb)
y_op = coldfilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_small_in():
ha = qshift('qshift_b')[0]
hb = qshift('qshift_b')[1]
im = mandrill[0:4, 0:4]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_colifilt(im, ha, hb)
y_op = colifilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift2():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
im = mandrill[:508, :504]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_coldfilt(im.T, ha, hb).T
y_op = rowdfilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift2():
ha = qshift('qshift_c')[0]
hb = qshift('qshift_c')[1]
im = mandrill[:508, :504]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_coldfilt(im.T, ha, hb).T
y_op = rowdfilt(im_t, ha, hb)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift1():
h = qshift('qshift_c')[0]
ref = np_colfilter(mandrill.T, h).T
y_op = rowfilter(mandrill_t, h)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_coldfilt():
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = colifilt(mandrill, g0b, g0a)
assert_almost_equal_to_summary(A,
verif['mandrill_colifilt'],
tolerance=TOLERANCE)
def test_simple_level_4_recon_custom_wavelets():
# Test for perfect reconstruction with 3 levels
b = biort('legall')
q = qshift('qshift_06')
Yl, Yh = dtwavexfm3(ellipsoid, 4, biort=b, qshift=q)
ellipsoid_recon = dtwaveifm3(Yl, Yh, biort=b, qshift=q)
assert ellipsoid.size == ellipsoid_recon.size
assert np.max(np.abs(ellipsoid - ellipsoid_recon)) < TOLERANCE
def test_simple_level_4_recon_custom_wavelets():
# Test for perfect reconstruction with 3 levels
b = biort('legall')
q = qshift('qshift_06')
Yl, Yh = dtwavexfm3(ellipsoid, 4, biort=b, qshift=q)
ellipsoid_recon = dtwaveifm3(Yl, Yh, biort=b, qshift=q)
assert ellipsoid.size == ellipsoid_recon.size
assert np.max(np.abs(ellipsoid - ellipsoid_recon)) < TOLERANCE
def test_equal_numpy_qshift2():
h = qshift('qshift_c')[0]
im = mandrill[52:407, 30:401]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_colfilter(im, h)
y_op = colfilter(im_t, h)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def test_equal_numpy_qshift2():
h = qshift('qshift_c')[0]
im = mandrill[15:307, 40:267]
im_t = tf.expand_dims(tf.constant(im, tf.float32), axis=0)
ref = np_colfilter(im.T, h).T
y_op = rowfilter(im_t, h)
with tf.Session() as sess:
y = sess.run(y_op)
np.testing.assert_array_almost_equal(y[0], ref, decimal=4)
def main():
GRID_SIZE = 128
SPHERE_RAD = int(0.45 * GRID_SIZE) + 0.5
# Compute an image of the sphere
grid = np.arange(-(GRID_SIZE>>1), GRID_SIZE>>1)
X, Y, Z = np.meshgrid(grid, grid, grid)
r = np.sqrt(X*X + Y*Y + Z*Z)
sphere = (0.5 + np.clip(SPHERE_RAD-r, -0.5, 0.5)).astype(np.float32)
# Specify number of levels and wavelet family to use
nlevels = 2
b = biort('near_sym_a')
q = qshift('qshift_a')
# Form the DT-CWT of the sphere. We use discard_level_1 since we're
# uninterested in the inverse transform and this saves us some memory.
Yl, Yh = dtwavexfm3(sphere, nlevels, b, q, discard_level_1=False)
# Plot maxima
figure(figsize=(8,8))
ax = gcf().add_subplot(1,1,1, projection='3d')
ax.set_aspect('equal')
ax.view_init(35, 75)
# Plot unit sphere +ve octant
thetas = np.linspace(0, np.pi/2, 10)
phis = np.linspace(0, np.pi/2, 10)
tris = []
rad = 0.99 # so that points plotted latter are not z-clipped
for t1, t2 in zip(thetas[:-1], thetas[1:]):
for p1, p2 in zip(phis[:-1], phis[1:]):
tris.append([
sphere_to_xyz(rad, t1, p1),
sphere_to_xyz(rad, t1, p2),
sphere_to_xyz(rad, t2, p2),
sphere_to_xyz(rad, t2, p1),
])
sphere = Poly3DCollection(tris, facecolor='w', edgecolor=(0.6,0.6,0.6))
ax.add_collection3d(sphere)
locs = []
scale = 1.1
for idx in range(Yh[-1].shape[3]):
Z = Yh[-1][:,:,:,idx]
C = np.abs(Z)
max_loc = np.asarray(np.unravel_index(np.argmax(C), C.shape)) - np.asarray(C.shape)*0.5
max_loc /= np.sqrt(np.sum(max_loc * max_loc))
# Only record directions in the +ve octant (or those from the -ve quadrant
# which can be flipped).
if np.all(np.sign(max_loc) == 1):
locs.append(max_loc)
ax.text(max_loc[0] * scale, max_loc[1] * scale, max_loc[2] * scale, str(idx+1))
elif np.all(np.sign(max_loc) == -1):
locs.append(-max_loc)
ax.text(-max_loc[0] * scale, -max_loc[1] * scale, -max_loc[2] * scale, str(idx+1))
# Plot all directions as a scatter plot
locs = np.asarray(locs)
ax.scatter(locs[:,0], locs[:,1], locs[:,2], c=np.arange(locs.shape[0]))
w = 1.1
ax.auto_scale_xyz([0, w], [0, w], [0, w])
legend()
title('3D DT-CWT subband directions for +ve hemisphere quadrant')
tight_layout()
show()
def test_wrong_type_b():
with raises(ValueError):
qshift('antonini')
def test_qshift():
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
y = coldfilt(lena, h1b, h1b)
z = coldfilt_gold(lena, h1b, h1a)
assert_almost_equal(y, z)
def test_wrong_type_b():
with raises(ValueError):
qshift('antonini')
def test_qshift_d():
coeffs = qshift('qshift_d')
assert len(coeffs) == 8
for v in coeffs:
assert v.shape[0] == 18
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_qshift():
y = colfilter(mandrill, qshift('qshift_a')[0])
assert y.shape == (mandrill.shape[0] + 1, mandrill.shape[1])
z = colfilter_gold(mandrill, qshift('qshift_a')[0])
assert_almost_equal(y, z)
def test_qshift():
h = qshift('qshift_a')[0]
y_op = colfilter(mandrill_t, h)
assert y_op.get_shape()[1:] == (mandrill.shape[0]+1, mandrill.shape[1])
def test_simple_custom_filter():
vec = np.random.rand(630)
Yl, Yh = dtwavexfm(vec, 4, biort('legall'), qshift('qshift_06'))
vec_recon = dtwaveifm(Yl, Yh, biort('legall'), qshift('qshift_06'))
assert np.max(np.abs(vec_recon - vec)) < TOLERANCE
def test_reconstruct_custom_filter():
# Reconstruction up to tolerance
Yl, Yh = dtwavexfm2(lena, 4, biort('legall'), qshift('qshift_06'))
lena_recon = dtwaveifm2(Yl, Yh, biort('legall'), qshift('qshift_06'))
assert np.all(np.abs(lena_recon - lena) < TOLERANCE)
def main():
GRID_SIZE = 128
SPHERE_RAD = int(0.45 * GRID_SIZE) + 0.5
# Compute an image of the sphere
grid = np.arange(-(GRID_SIZE >> 1), GRID_SIZE >> 1)
X, Y, Z = np.meshgrid(grid, grid, grid)
r = np.sqrt(X * X + Y * Y + Z * Z)
sphere = (0.5 + np.clip(SPHERE_RAD - r, -0.5, 0.5)).astype(np.float32)
# Specify number of levels and wavelet family to use
nlevels = 2
b = biort('near_sym_a')
q = qshift('qshift_a')
# Form the DT-CWT of the sphere. We use discard_level_1 since we're
# uninterested in the inverse transform and this saves us some memory.
Yl, Yh = dtwavexfm3(sphere, nlevels, b, q, discard_level_1=False)
# Plot maxima
figure(figsize=(8, 8))
ax = gcf().add_subplot(1, 1, 1, projection='3d')
ax.set_aspect('equal')
ax.view_init(35, 75)
# Plot unit sphere +ve octant
thetas = np.linspace(0, np.pi / 2, 10)
phis = np.linspace(0, np.pi / 2, 10)
tris = []
rad = 0.99 # so that points plotted latter are not z-clipped
for t1, t2 in zip(thetas[:-1], thetas[1:]):
for p1, p2 in zip(phis[:-1], phis[1:]):
tris.append([
sphere_to_xyz(rad, t1, p1),
sphere_to_xyz(rad, t1, p2),
sphere_to_xyz(rad, t2, p2),
sphere_to_xyz(rad, t2, p1),
])
sphere = Poly3DCollection(tris, facecolor='w', edgecolor=(0.6, 0.6, 0.6))
ax.add_collection3d(sphere)
locs = []
scale = 1.1
for idx in range(Yh[-1].shape[3]):
Z = Yh[-1][:, :, :, idx]
C = np.abs(Z)
max_loc = np.asarray(np.unravel_index(
np.argmax(C), C.shape)) - np.asarray(C.shape) * 0.5
max_loc /= np.sqrt(np.sum(max_loc * max_loc))
# Only record directions in the +ve octant (or those from the -ve quadrant
# which can be flipped).
if np.all(np.sign(max_loc) == 1):
locs.append(max_loc)
ax.text(max_loc[0] * scale, max_loc[1] * scale, max_loc[2] * scale,
str(idx + 1))
elif np.all(np.sign(max_loc) == -1):
locs.append(-max_loc)
ax.text(-max_loc[0] * scale, -max_loc[1] * scale,
-max_loc[2] * scale, str(idx + 1))
# Plot all directions as a scatter plot
locs = np.asarray(locs)
ax.scatter(locs[:, 0], locs[:, 1], locs[:, 2], c=np.arange(locs.shape[0]))
w = 1.1
ax.auto_scale_xyz([0, w], [0, w], [0, w])
legend()
title('3D DT-CWT subband directions for +ve hemisphere quadrant')
tight_layout()
show()
def test_specific_wavelet():
Yl, Yh = dtwavexfm2(mandrill, biort=biort('antonini'), qshift=qshift('qshift_06'))
def test_qshift():
y = colfilter(lena, qshift('qshift_a')[0])
assert y.shape == (lena.shape[0]+1, lena.shape[1])
z = colfilter_gold(lena, qshift('qshift_a')[0])
assert_almost_equal(y, z)
def test_reconstruct_custom_filter():
# Reconstruction up to tolerance
Yl, Yh = dtwavexfm2(mandrill, 4, biort('legall'), qshift('qshift_06'))
mandrill_recon = dtwaveifm2(Yl, Yh, biort('legall'), qshift('qshift_06'))
assert np.all(np.abs(mandrill_recon - mandrill) < TOLERANCE)
def test_real_wavelet():
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = coldfilt(mandrill[:,:511], h1b, h1a)
B = coldfilt_gold(mandrill[:,:511], h1b, h1a)
assert_almost_equal(A, B)
def test_specific_wavelet():
Yl, Yh = dtwavexfm2(lena, biort=biort('antonini'), qshift=qshift('qshift_06'))
def test_qshift():
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
y = coldfilt(mandrill, h1b, h1b)
z = coldfilt_gold(mandrill, h1b, h1a)
assert_almost_equal(y, z)
def test_reconstruct_custom_filter():
# Reconstruction up to tolerance
Yl, Yh = dtwavexfm2(mandrill, 4, biort("legall"), qshift("qshift_06"))
mandrill_recon = dtwaveifm2(Yl, Yh, biort("legall"), qshift("qshift_06"))
assert np.all(np.abs(mandrill_recon - mandrill) < TOLERANCE)
def test_qshift():
y = colfilter(lena, qshift('qshift_a')[0])
assert y.shape == (lena.shape[0]+1, lena.shape[1])
def test_simple_custom_filter():
vec = np.random.rand(630)
Yl, Yh = dtwavexfm(vec, 4, biort('legall'), qshift('qshift_06'))
vec_recon = dtwaveifm(Yl, Yh, biort('legall'), qshift('qshift_06'))
assert np.max(np.abs(vec_recon - vec)) < TOLERANCE
def test_coldfilt():
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = colifilt(mandrill, g0b, g0a)
assert_almost_equal_to_summary(A, verif['mandrill_colifilt'], tolerance=TOLERANCE)
def test_modified():
a = dtwavexfm2_np(mandrill, biort=biort('near_sym_b_bp'), qshift=qshift('qshift_b_bp'))
b = dtwavexfm2_cl(mandrill, biort=biort('near_sym_b_bp'), qshift=qshift('qshift_b_bp'))
_compare_transforms(a, b)
def test_non_exist_qshift():
with raises(IOError):
qshift('this-does-not-exist')
def test_non_exist_qshift():
with raises(IOError):
qshift('this-does-not-exist')
def test_real_wavelet():
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = coldfilt(lena[:,:511], h1b, h1a)
B = coldfilt_gold(lena[:,:511], h1b, h1a)
assert_almost_equal(A, B)
def test_wrong_type_b():
qshift('antonini')
def test_specific_wavelet():
Yl, Yh = dtwavexfm2(mandrill, biort=biort("antonini"), qshift=qshift("qshift_06"))
def test_qshift():
h = qshift('qshift_a')[0]
y_op = rowfilter(mandrill_t, h)
assert y_op.get_shape()[1:] == (mandrill.shape[0], mandrill.shape[1]+1)
def test_qshift():
y = colfilter(mandrill, qshift('qshift_a')[0])
assert y.shape == (mandrill.shape[0] + 1, mandrill.shape[1])
def test_qshift():
y = colfilter(mandrill, qshift('qshift_a')[0])
assert y.shape == (mandrill.shape[0]+1, mandrill.shape[1])
"""
from __future__ import print_function, division
import os
import timeit
import numpy as np
from dtcwt.coeffs import biort, qshift
from dtcwt.opencl.lowlevel import NoCLPresentError, get_default_queue
lena = np.load(os.path.join(os.path.dirname(__file__), '..', 'tests', 'lena.npz'))['lena']
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
def format_time(t):
units = (
(60*60, 'hr'), (60, 'min'), (1, 's'), (1e-3, 'ms')
)
for scale, unit in units:
if t >= scale:
return '{0:.2f} {1}'.format(t/scale, unit)
return '{0:.2f} {1}'.format(t*1e6, 'us')
def benchmark(statement='pass', setup='pass'):
number, repeat = (1, 3)
min_time = 0
"""
from __future__ import print_function, division
import os
import timeit
import numpy as np
from dtcwt.coeffs import biort, qshift
from dtcwt.opencl.lowlevel import NoCLPresentError, get_default_queue
mandrill = np.load(os.path.join(os.path.dirname(__file__), '..', 'tests', 'mandrill.npz'))['mandrill']
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
def format_time(t):
units = (
(60*60, 'hr'), (60, 'min'), (1, 's'), (1e-3, 'ms')
)
for scale, unit in units:
if t >= scale:
return '{0:.2f} {1}'.format(t/scale, unit)
return '{0:.2f} {1}'.format(t*1e6, 'us')
def benchmark(statement='pass', setup='pass'):
number, repeat = (1, 3)
min_time = 0
def test_specific_wavelet():
a = dtwavexfm2_np(mandrill, biort=biort('antonini'), qshift=qshift('qshift_06'))
b = dtwavexfm2_cl(mandrill, biort=biort('antonini'), qshift=qshift('qshift_06'))
_compare_transforms(a, b)
def test_qshift():
y = colfilter(mandrill, qshift('qshift_a')[0])
assert y.shape == (mandrill.shape[0]+1, mandrill.shape[1])
z = colfilter_gold(mandrill, qshift('qshift_a')[0])
assert_almost_equal(y, z)
def test_non_exist_qshift():
qshift('this-does-not-exist')
def test_qshift_d():
coeffs = qshift('qshift_d')
assert len(coeffs) == 8
for v in coeffs:
assert v.shape[0] == 18
