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 load_and_transform_image(filename, xfmargs=None):
"""Load an image from a file named *filename* and transform it. If
necessary the image will be transformed into luminance. *xfmargs* is a
dictionary which is passwd as kwargs to dtwavexfm2.
"""
log.info('Loading image from {0}'.format(filename))
im = as_luminance(Image.open(filename)).astype(np.float32)
return dtwavexfm2(im, **(xfmargs or {}))
def load_and_transform_image(filename, xfmargs=None):
"""Load an image from a file named *filename* and transform it. If
necessary the image will be transformed into luminance. *xfmargs* is a
dictionary which is passwd as kwargs to dtwavexfm2.
"""
log.info('Loading image from {0}'.format(filename))
im = as_luminance(Image.open(filename)).astype(np.float32)
return dtwavexfm2(im, **(xfmargs or {}))
def test_simple_w_scale():
Yl, Yh, Yscale = dtwavexfm2(lena, include_scale=True)
def test_3d():
Yl, Yh = dtwavexfm2(np.dstack((lena, lena)))
def test_1d():
Yl, Yh = dtwavexfm2(lena[0, :])
def test_simple_w_scale():
Yl, Yh, Yscale = dtwavexfm2(lena, include_scale=True)
def test_specific_wavelet():
Yl, Yh = dtwavexfm2(lena, biort=biort("antonini"), qshift=qshift("qshift_06"))
def test_0_levels_w_scale():
Yl, Yh, Yscale = dtwavexfm2(lena, nlevels=0, include_scale=True)
assert np.all(np.abs(Yl - lena) < TOLERANCE)
assert len(Yh) == 0
assert len(Yscale) == 0
def test_odd_rows_and_cols_w_scale():
Yl, Yh, Yscale = dtwavexfm2(lena[:509, :509], include_scale=True)
def test_1d():
Yl, Yh = dtwavexfm2(lena[0, :])
def test_integer_input():
# Check that an integer input is correctly coerced into a floating point
# array
Yl, Yh = dtwavexfm2([[1, 2, 3, 4], [1, 2, 3, 4]])
assert np.any(Yl != 0)
def test_0_levels_w_scale():
Yl, Yh, Yscale = dtwavexfm2(lena, nlevels=0, include_scale=True)
assert np.all(np.abs(Yl - lena) < TOLERANCE)
assert len(Yh) == 0
assert len(Yscale) == 0
def test_0_levels():
Yl, Yh = dtwavexfm2(lena, nlevels=0)
assert np.all(np.abs(Yl - lena) < TOLERANCE)
assert len(Yh) == 0
def test_odd_rows_and_cols_w_scale():
Yl, Yh, Yscale = dtwavexfm2(lena[:509, :509], include_scale=True)
def test_odd_rows_and_cols():
Yl, Yh = dtwavexfm2(lena[:, :509])
def test_odd_rows():
Yl, Yh = dtwavexfm2(lena[:509, :])
def test_odd_rows():
Yl, Yh = dtwavexfm2(lena[:509, :])
def test_odd_rows_and_cols():
Yl, Yh = dtwavexfm2(lena[:, :509])
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)
def test_0_levels():
Yl, Yh = dtwavexfm2(lena, nlevels=0)
assert np.all(np.abs(Yl - lena) < TOLERANCE)
assert len(Yh) == 0
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_integer_input():
# Check that an integer input is correctly coerced into a floating point
# array
Yl, Yh = dtwavexfm2([[1, 2, 3, 4], [1, 2, 3, 4]])
assert np.any(Yl != 0)
def test_simple():
Yl, Yh = dtwavexfm2(lena)
scale = 1.2
scale_method = 'lanczos'
def scale_direct(im):
"""Scale image directly."""
return dtcwt.sampling.scale(im, (im.shape[0]*scale, im.shape[1]*scale), scale_method)
def scale_highpass(im):
"""Scale image assuming it to be wavelet highpass coefficients."""
return dtcwt.sampling.scale_highpass(im, (im.shape[0]*scale, im.shape[1]*scale), scale_method)
# Rescale lena directly using default (Lanczos) sampling
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))
def test_3d():
Yl, Yh = dtwavexfm2(np.dstack((lena, lena)))
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_simple():
Yl, Yh = dtwavexfm2(lena)
def test_specific_wavelet():
Yl, Yh = dtwavexfm2(lena,
biort=biort('antonini'),
qshift=qshift('qshift_06'))
"""Scale image directly."""
return dtcwt.sampling.scale(im, (im.shape[0] * scale, im.shape[1] * scale),
scale_method)
def scale_highpass(im):
"""Scale image assuming it to be wavelet highpass coefficients."""
return dtcwt.sampling.scale_highpass(
im, (im.shape[0] * scale, im.shape[1] * scale), scale_method)
# Rescale lena directly using default (Lanczos) sampling
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))
