def test_integer_perfect_recon():
# Check that an integer input is correctly coerced into a floating point
# array and reconstructed
A = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.int32)
Yl, Yh = dtwavexfm2(A)
B = dtwaveifm2(Yl, Yh)
assert np.max(np.abs(A - B)) < 1e-5
def test_integer_perfect_recon():
# Check that an integer input is correctly coerced into a floating point
# array and reconstructed
A = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.int32)
Yl, Yh = dtwavexfm2(A)
B = dtwaveifm2(Yl, Yh)
assert np.max(np.abs(A - B)) < 1e-5
def test_float32_input():
# Check that an float32 input is correctly output as float32
Yl, Yh = dtwavexfm2(lena.astype(np.float32))
assert np.issubsctype(Yl.dtype, np.float32)
assert np.all(list(np.issubsctype(x.dtype, np.complex64) for x in Yh))
lena_recon = dtwaveifm2(Yl, Yh)
assert np.issubsctype(lena_recon.dtype, np.float32)
def inv_transform_and_save_image(filename, transform, xfmargs=None):
"""Inverse transform assuming Yl, Yh = transform passing biort and qshift
from xfmargs to dtwaveifm2 if they are present. Save the resulting image to
filename in PNG format.
"""
im_array = dtwaveifm2(transform[0], transform[1], biort=xfmargs['biort'], qshift=xfmargs['qshift'])
im = Image.fromarray(np.clip(im_array, 0, 255).astype(np.uint8))
im.save(filename, format='PNG')
def inv_transform_and_save_image(filename, transform, xfmargs=None):
"""Inverse transform assuming Yl, Yh = transform passing biort and qshift
from xfmargs to dtwaveifm2 if they are present. Save the resulting image to
filename in PNG format.
"""
im_array = dtwaveifm2(transform[0],
transform[1],
biort=xfmargs['biort'],
qshift=xfmargs['qshift'])
im = Image.fromarray(np.clip(im_array, 0, 255).astype(np.uint8))
im.save(filename, format='PNG')
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 test_reconstruct_crop():
# Reconstruction up to tolerance
Yl_crop, Yh_crop = dtwavexfm2(lena_crop)
lena_recon = dtwaveifm2(Yl_crop, Yh_crop)[:lena_crop.shape[0], :lena_crop.shape[1]]
assert np.all(np.abs(lena_recon - lena_crop) < TOLERANCE)
def test_reconstruct():
# Reconstruction up to tolerance
Yl, Yh = dtwavexfm2(lena)
lena_recon = dtwaveifm2(Yl, Yh)
assert np.all(np.abs(lena_recon - lena) < TOLERANCE)
lena_direct = scale_direct(lena)
# Transform lena
lena_l, lena_h = dtcwt.dtwavexfm2(lena, nlevels=4)
# Re-scale each component and transform back. Do this both with and without
# shifting back to DC.
lena_l = scale_direct(lena_l)
lena_h_a, lena_h_b = [], []
for h in lena_h:
lena_h_a.append(scale_direct(h))
lena_h_b.append(scale_highpass(h))
# Transform back
lena_a = dtcwt.dtwaveifm2(lena_l, lena_h_a)
lena_b = dtcwt.dtwaveifm2(lena_l, lena_h_b)
figure(figsize=(10,10))
subplot(2,2,1)
imshow(lena, cmap=cm.gray, clim=(0,1), interpolation='none')
axis('off')
title('Original')
subplot(2,2,2)
imshow(lena_direct, cmap=cm.gray, clim=(0,1), interpolation='none')
axis('off')
title('Directly up-sampled')
subplot(2,2,3)
lena_direct = scale_direct(lena)
# Transform lena
lena_l, lena_h = dtcwt.dtwavexfm2(lena, nlevels=4)
# Re-scale each component and transform back. Do this both with and without
# shifting back to DC.
lena_l = scale_direct(lena_l)
lena_h_a, lena_h_b = [], []
for h in lena_h:
lena_h_a.append(scale_direct(h))
lena_h_b.append(scale_highpass(h))
# Transform back
lena_a = dtcwt.dtwaveifm2(lena_l, lena_h_a)
lena_b = dtcwt.dtwaveifm2(lena_l, lena_h_b)
figure(figsize=(10, 10))
subplot(2, 2, 1)
imshow(lena, cmap=cm.gray, clim=(0, 1), interpolation='none')
axis('off')
title('Original')
subplot(2, 2, 2)
imshow(lena_direct, cmap=cm.gray, clim=(0, 1), interpolation='none')
axis('off')
title('Directly up-sampled')
subplot(2, 2, 3)
