def test_haar():
image = luispedro_jpg(1)
image = image[:256,:256]
wav = mahotas.haar(image)
assert wav.shape == image.shape
assert np.allclose((image[0].reshape((-1,2)).mean(1)+image[1].reshape((-1,2)).mean(1))/2, wav[0,:128]/2.)
assert np.abs(np.mean(image**2) - np.mean(wav**2)) < 1.
image = luispedro_jpg(1)
wav = mahotas.haar(image, preserve_energy=False)
assert np.abs(np.mean(image**2) - np.mean(wav**2)) > 16.
wav = mahotas.haar(image, inline=True)
assert id(image) == id(wav)
def test_haar():
image = luispedro_jpg()
image = image[:256, :256]
wav = mahotas.haar(image)
assert wav.shape == image.shape
assert np.allclose((image[0].reshape((-1, 2)).mean(1) + image[1].reshape(
(-1, 2)).mean(1)) / 2, wav[0, :128] / 2.)
assert np.abs(np.mean(image**2) - np.mean(wav**2)) < 1.
image = luispedro_jpg()
wav = mahotas.haar(image, preserve_energy=False)
assert np.abs(np.mean(image**2) - np.mean(wav**2)) > 16.
wav = mahotas.haar(image, inline=True)
assert id(image) == id(wav)
def test_daubechies_D2_haar():
image = luispedro_jpg(1)
image = image[:256,:256]
wav = mahotas.haar(image, preserve_energy=False)
dau = mahotas.daubechies(image, 'D2')
assert wav.shape == dau.shape
assert np.allclose(dau, wav)
def test_daubechies_D2_haar():
image = luispedro_jpg()
image = image[:256, :256]
wav = mahotas.haar(image, preserve_energy=False)
dau = mahotas.daubechies(image, 'D2')
assert wav.shape == dau.shape
assert np.allclose(dau, wav)
def test_ihaar():
image = luispedro_jpg(1)
image = image[:256,:256]
wav = mahotas.haar(image)
iwav = mahotas.ihaar(wav)
assert np.allclose(image, iwav)
iwav = mahotas.ihaar(wav, preserve_energy=False)
assert not np.allclose(wav, iwav)
iwav = mahotas.ihaar(wav, inline=True)
assert id(iwav) == id(wav)
def test_ihaar():
image = luispedro_jpg()
image = image[:256, :256]
wav = mahotas.haar(image)
iwav = mahotas.ihaar(wav)
assert np.allclose(image, iwav)
iwav = mahotas.ihaar(wav, preserve_energy=False)
assert not np.allclose(wav, iwav)
iwav = mahotas.ihaar(wav, inline=True)
assert id(iwav) == id(wav)
def haar(img):
"""Haar wavelet transform."""
# Cannot have nans here, they might have global influence.
img = np.asarray_chkfinite(img)
assert img.ndim == 2
# assert img.shape[0] % 2 == img.shape[1] % 2 == 0
# Prune possible odd borders.
newshape = [x - x % 2 for x in img.shape]
img = img[:newshape[0], :newshape[1]]
a = mahotas.haar(img)
h, w = [x // 2 for x in a.shape]
coeffs = [a[:h, :w], a[:h, w:], a[h:, :w], a[h:, w:]]
coeffs = [ndimage.interpolation.zoom(l, 2.) for l in coeffs]
return coeffs
