def test_sp_backfill_numba():
sp_backfill = select('sp_backfill', backend='numba')
x = np.load(ASSETS('beach_sp_mean.npy'))
segments = np.load(ASSETS('beach_sp.npy'))
y = sp_backfill(x, segments)
y_true = np.load(ASSETS('beach_sp_restore.npy'))
npt.assert_allclose(y, y_true, rtol=0.005)
def test_sp_mean_cupy():
sp_mean = select('sp_mean', backend='cupy')
im_test = read_resize(ASSETS('beach24.png'), (224,224))[...,0]
segments = np.load(ASSETS('beach_sp.npy'))
y = sp_mean(im_test, segments)
y_true = np.load(ASSETS('beach_sp_mean.npy'))
npt.assert_allclose(y, y_true, rtol=1e-03)
def test_read_resize_lycon_shape():
read_resize = select('read_resize', backend='lycon')
im_test = read_resize(ASSETS('beach24.png'), (224,224))
assert im_test.shape == (224,224,3)
def test_read_resize_lycon_range():
read_resize = select('read_resize', backend='lycon')
im_test = read_resize(ASSETS('beach24.png'), (224,224))
assert 178 < im_test.mean() < 180
def test_read_resize_lycon_dtype():
read_resize = select('read_resize', backend='lycon')
im_test = read_resize(ASSETS('beach24.png'), (224,224))
assert im_test.dtype == np.dtype('u1')