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(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_gaussian_filter():
from scipy import ndimage
f = luispedro_jpg(1)
for s in (4., 8., 12.):
g = gaussian_filter(f, s)
n = ndimage.gaussian_filter(f, s)
assert np.max(np.abs(n - g)) < 1.e-5
def test_gaussian_filter():
from scipy import ndimage
f = luispedro_jpg(1)
for s in (4.,8.,12.):
g = gaussian_filter(f, s)
n = ndimage.gaussian_filter(f, s)
assert np.max(np.abs(n - g)) < 1.e-5
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_idaubechies():
f = luispedro_jpg(1)
f = f[:256, :256]
fo = f.copy()
d = mahotas.daubechies(f, 'D8')
r = mahotas.idaubechies(d, 'D8')
assert np.mean((r[4:-4, 4:-4] - fo[4:-4, 4:-4])**2) < 1.
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_idaubechies():
f = luispedro_jpg(1)
f = f[:256,:256]
fo = f.copy()
d = mahotas.daubechies(f, 'D8')
r = mahotas.idaubechies(d, 'D8')
assert np.mean( (r[4:-4,4:-4] - fo[4:-4,4:-4])**2) < 1.
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(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_rgb2grey():
f = luispedro_jpg()
fg = rgb2grey(f)
fg8 = rgb2grey(f, dtype=np.uint8)
assert f.ndim == 3
assert fg.ndim == 2
assert fg8.ndim == 2
assert fg.shape[0] == f.shape[0]
assert fg.shape[1] == f.shape[1]
assert fg.shape == fg8.shape
assert fg8.dtype == np.uint8
def test_colors():
f = luispedro_jpg()
lab = rgb2lab(f)
assert np.max(np.abs(lab)) <= 100.
assert np.max(np.abs(xyz2rgb(rgb2xyz(f)) - f)) < 1.
lab8 = rgb2lab(f, dtype=np.uint8)
assert lab.dtype != np.uint8
assert lab8.dtype == np.uint8
xyz = rgb2xyz(f, dtype=np.uint8)
assert xyz.shape == f.shape
assert xyz.dtype == np.uint8
def test_center_wavelet_iwavelet_decenter():
from mahotas import wavelet_center, wavelet_decenter
import mahotas
import numpy as np
f = luispedro_jpg(1)
f = f[:100,:250]
fo = f.copy()
for wav in ('D2', 'D4', 'D6', 'D8', 'D10', 'D12', 'D16'):
fc = mahotas.wavelet_center(fo, border=24)
t = mahotas.daubechies(fc, wav)
r = mahotas.idaubechies(t, wav)
rd = mahotas.wavelet_decenter(r, fo.shape, border=24)
assert np.allclose(fo, rd)
def test_center_wavelet_iwavelet_decenter():
from mahotas import wavelet_center, wavelet_decenter
import mahotas
import numpy as np
f = luispedro_jpg(1)
f = f[:100, :250]
fo = f.copy()
for wav in ('D2', 'D4', 'D6', 'D8', 'D10', 'D12', 'D16'):
fc = mahotas.wavelet_center(fo, border=24)
t = mahotas.daubechies(fc, wav)
r = mahotas.idaubechies(t, wav)
rd = mahotas.wavelet_decenter(r, fo.shape, border=24)
assert np.allclose(fo, rd)
def test_lbp_transform():
im = luispedro_jpg().max(2)
transformed = lbp_transform(im, 8, 4, preserve_shape=True)
assert transformed.shape == im.shape
assert transformed.min() >= 0
assert transformed.max() < 2**4
transformed = lbp_transform(im, 8, 4, preserve_shape=False)
assert len(transformed.shape) == 1
assert transformed.size == im.size
np.random.seed(234)
im *= np.random.random(im.shape) > .1
transformed = lbp_transform(im, 8, 4, preserve_shape=False, ignore_zeros=True)
assert len(transformed.shape) == 1
assert transformed.size == (im.size - (im==0).sum())
def test_wavelet_iwavelet():
f = luispedro_jpg(1)
f = f[:256,:256]
fo = f.copy()
D4 = np.array([0.6830127, 1.1830127, 0.3169873, -0.1830127], dtype=np.float32)
D4_high = D4[::-1].copy()
D4_high[1::2] *= -1
f = f[34]
low = np.convolve(f, D4)
high = np.convolve(f,D4_high)
low[::2] = 0
high[::2] = 0
rec = (np.convolve(high, D4_high[::-1])+np.convolve(low, D4[::-1]))
rec /= 2
f2 = np.array([f])
mahotas._convolve.wavelet(f2,D4)
hand = np.concatenate((low[3::2],high[3::2]))
wav = f2.ravel()
assert np.allclose(hand,wav)
mahotas._convolve.iwavelet(f2,D4)
assert np.allclose(rec[3:-3],f)
assert np.allclose(f2.ravel()[3:-3],f[3:-3])
def test_wavelet_iwavelet():
f = luispedro_jpg(1)
f = f[:256, :256]
fo = f.copy()
D4 = np.array([0.6830127, 1.1830127, 0.3169873, -0.1830127],
dtype=np.float32)
D4_high = D4[::-1].copy()
D4_high[1::2] *= -1
f = f[34]
low = np.convolve(f, D4)
high = np.convolve(f, D4_high)
low[::2] = 0
high[::2] = 0
rec = (np.convolve(high, D4_high[::-1]) + np.convolve(low, D4[::-1]))
rec /= 2
f2 = np.array([f])
mahotas._convolve.wavelet(f2, D4)
hand = np.concatenate((low[3::2], high[3::2]))
wav = f2.ravel()
assert np.allclose(hand, wav)
mahotas._convolve.iwavelet(f2, D4)
assert np.allclose(rec[3:-3], f)
assert np.allclose(f2.ravel()[3:-3], f[3:-3])
def test_sepia():
f = luispedro_jpg()
sepia= mahotas.colors.rgb2sepia(f)
assert sepia.shape == f.shape
def test_non_valid_daubechies():
image = luispedro_jpg()
mahotas.daubechies(image, 'D-4')
def test_non_valid_daubechies():
image = luispedro_jpg()
mahotas.daubechies(image, 'D-4')
