def test_rescale():
# same scale factor
x = np.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
scaled = rescale(x,
2,
order=0,
channel_axis=None,
anti_aliasing=False,
mode='constant')
ref = np.zeros((10, 10))
ref[2:4, 2:4] = 1
assert_array_almost_equal(scaled, ref)
# different scale factors
x = np.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
scaled = rescale(x, (2, 1),
order=0,
channel_axis=None,
anti_aliasing=False,
mode='constant')
ref = np.zeros((10, 5))
ref[2:4, 1] = 1
assert_array_almost_equal(scaled, ref)
def test_keep_range():
image = np.linspace(0, 2, 25).reshape(5, 5)
out = rescale(image,
2,
preserve_range=False,
clip=True,
order=0,
mode='constant',
channel_axis=None,
anti_aliasing=False)
assert out.min() == 0
assert out.max() == 2
out = rescale(image,
2,
preserve_range=True,
clip=True,
order=0,
mode='constant',
channel_axis=None,
anti_aliasing=False)
assert out.min() == 0
assert out.max() == 2
out = rescale(image.astype(np.uint8),
2,
preserve_range=False,
mode='constant',
channel_axis=None,
anti_aliasing=False,
clip=True,
order=0)
assert out.min() == 0
assert out.max() == 2
def test_bool_and_anti_aliasing_errors():
img = np.zeros((10, 10), dtype=bool)
with pytest.raises(ValueError):
rescale(img, 0.5, anti_aliasing=True)
with pytest.raises(ValueError):
resize(img, (5, 5), anti_aliasing=True)
def test_rescale_invalid_scale():
x = np.zeros((10, 10, 3))
with testing.raises(ValueError):
rescale(x, (2, 2),
multichannel=False, anti_aliasing=False, mode='constant')
with testing.raises(ValueError):
rescale(x, (2, 2, 2),
multichannel=True, anti_aliasing=False, mode='constant')
def test_rescale_invalid_scale():
x = np.zeros((10, 10, 3))
with pytest.raises(ValueError):
rescale(x, (2, 2),
channel_axis=None, anti_aliasing=False, mode='constant')
with pytest.raises(ValueError):
rescale(x, (2, 2, 2),
channel_axis=-1, anti_aliasing=False, mode='constant')
def test_rescale_multichannel_defaults():
x = np.zeros((8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
assert scaled.shape == (16, 6)
x = np.zeros((8, 8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
assert scaled.shape == (16, 16, 6)
def test_bool_nonzero_order_errors(order):
img = np.zeros((10, 10), dtype=bool)
with pytest.raises(ValueError):
rescale(img, 0.5, order=order)
with pytest.raises(ValueError):
resize(img, (5, 5), order=order)
with pytest.raises(ValueError):
warp(img, np.eye(3), order=order)
def test_rescale_multichannel_deprecated_multiscale():
x = np.zeros((5, 5, 3), dtype=np.float64)
with expected_warnings(["`multichannel` is a deprecated argument"]):
scaled = rescale(x, (2, 1), order=0, multichannel=True,
anti_aliasing=False, mode='constant')
assert scaled.shape == (10, 5, 3)
# repeat prior test, but check for positional multichannel _warnings
with expected_warnings(["Providing the `multichannel` argument"]):
scaled = rescale(x, (2, 1), 0, 'constant', 0, True, False, True,
anti_aliasing=False)
assert scaled.shape == (10, 5, 3)
def test_warp_clip():
x = np.zeros((5, 5), dtype=np.float64)
x[2, 2] = 1
outx = rescale(x, 3, order=3, clip=False, anti_aliasing=False,
mode='constant')
assert outx.min() < 0
outx = rescale(x, 3, order=3, clip=True, anti_aliasing=False,
mode='constant')
assert_array_almost_equal(outx.min(), 0)
assert_array_almost_equal(outx.max(), 1)
def test_rescale_channel_axis_multiscale(channel_axis):
x = np.zeros((5, 5, 3), dtype=np.float64)
x = np.moveaxis(x, -1, channel_axis)
scaled = rescale(x, scale=(2, 1), order=0, channel_axis=channel_axis,
anti_aliasing=False, mode='constant')
scaled = np.moveaxis(scaled, channel_axis, -1)
assert scaled.shape == (10, 5, 3)
def test_rescale_multichannel_multiscale():
x = np.zeros((5, 5, 3), dtype=np.double)
scaled = rescale(x, (2, 1),
order=0,
multichannel=True,
anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (10, 5, 3))
def test_bool_array_warnings():
img = np.zeros((10, 10), dtype=bool)
with expected_warnings(['Input image dtype is bool']):
rescale(img, 0.5, anti_aliasing=True)
with expected_warnings(['Input image dtype is bool']):
resize(img, (5, 5), anti_aliasing=True)
with expected_warnings(['Input image dtype is bool']):
rescale(img, 0.5, order=1)
with expected_warnings(['Input image dtype is bool']):
resize(img, (5, 5), order=1)
with expected_warnings(['Input image dtype is bool']):
warp(img, np.eye(3), order=1)
def test_order_0_warp_dtype(dtype):
img = _convert(astronaut()[:10, :10, 0], dtype)
assert resize(img, (12, 12), order=0).dtype == dtype
assert rescale(img, 0.5, order=0).dtype == dtype
assert rotate(img, 45, order=0).dtype == dtype
assert warp_polar(img, order=0).dtype == dtype
assert swirl(img, order=0).dtype == dtype
def test_bool_img_rescale():
img = np.ones((12, 18), dtype=bool)
img[2:-2, 4:-4] = False
res = rescale(img, 0.5)
expected = np.ones((6, 9))
expected[1:-1, 2:-2] = False
assert_array_equal(res, expected)
def test_downscale_anti_aliasing():
x = np.zeros((10, 10), dtype=np.double)
x[2, 2] = 1
scaled = rescale(x, 0.5, order=1, anti_aliasing=True,
multichannel=False, mode='constant')
assert_equal(scaled.shape, (5, 5))
assert np.all(scaled[:3, :3] > 0)
assert_equal(scaled[3:, :].sum(), 0)
assert_equal(scaled[:, 3:].sum(), 0)
def test_downscale():
x = np.zeros((10, 10), dtype=np.double)
x[2:4, 2:4] = 1
scaled = rescale(x, 0.5, order=0, anti_aliasing=False,
multichannel=False, mode='constant')
assert_equal(scaled.shape, (5, 5))
assert_equal(scaled[1, 1], 1)
assert_equal(scaled[2:, :].sum(), 0)
assert_equal(scaled[:, 2:].sum(), 0)
def test_downscale_anti_aliasing():
x = np.zeros((10, 10), dtype=np.float64)
x[2, 2] = 1
scaled = rescale(x, 0.5, order=1, anti_aliasing=True,
channel_axis=None, mode='constant')
assert scaled.shape == (5, 5)
assert np.all(scaled[:3, :3] > 0)
assert scaled[3:, :].sum() == 0
assert scaled[:, 3:].sum() == 0
def _coarsenImage(image, f):
'''
seems to be a more precise (but slower)
way to down-scale an image
'''
from skimage.morphology import square
from skimage.filters import rank
from skimage.transform._warps import rescale
selem = square(f)
arri = rank.mean(image, selem=selem)
return rescale(arri, 1 / f, order=0)
def test_nonzero_order_warp_dtype(dtype, order):
img = _convert(astronaut()[:10, :10, 0], dtype)
float_dtype = _supported_float_type(dtype)
assert resize(img, (12, 12), order=order).dtype == float_dtype
assert rescale(img, 0.5, order=order).dtype == float_dtype
assert rotate(img, 45, order=order).dtype == float_dtype
assert warp_polar(img, order=order).dtype == float_dtype
assert swirl(img, order=order).dtype == float_dtype
def test_downscale(dtype):
x = np.zeros((10, 10), dtype=dtype)
x[2:4, 2:4] = 1
scaled = rescale(x, 0.5, order=0, anti_aliasing=False,
channel_axis=None, mode='constant')
expected_dtype = np.float32 if dtype == np.float16 else dtype
assert scaled.dtype == expected_dtype
assert scaled.shape == (5, 5)
assert scaled[1, 1] == 1
assert scaled[2:, :].sum() == 0
assert scaled[:, 2:].sum() == 0
def test_rescale_multichannel():
# 1D + channels
x = np.zeros((8, 3), dtype=np.float64)
scaled = rescale(x, 2, order=0, channel_axis=-1, anti_aliasing=False,
mode='constant')
assert scaled.shape == (16, 3)
# 2D
scaled = rescale(x, 2, order=0, channel_axis=None, anti_aliasing=False,
mode='constant')
assert scaled.shape == (16, 6)
# 2D + channels
x = np.zeros((8, 8, 3), dtype=np.float64)
scaled = rescale(x, 2, order=0, channel_axis=-1, anti_aliasing=False,
mode='constant')
assert scaled.shape == (16, 16, 3)
# 3D
scaled = rescale(x, 2, order=0, channel_axis=None, anti_aliasing=False,
mode='constant')
assert scaled.shape == (16, 16, 6)
# 3D + channels
x = np.zeros((8, 8, 8, 3), dtype=np.float64)
scaled = rescale(x, 2, order=0, channel_axis=-1, anti_aliasing=False,
mode='constant')
assert scaled.shape == (16, 16, 16, 3)
# 4D
scaled = rescale(x, 2, order=0, channel_axis=None, anti_aliasing=False,
mode='constant')
assert scaled.shape == (16, 16, 16, 6)
def test_rescale_multichannel():
# 1D + channels
x = np.zeros((8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (16, 3))
# 2D
scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (16, 6))
# 2D + channels
x = np.zeros((8, 8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (16, 16, 3))
# 3D
scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (16, 16, 6))
# 3D + channels
x = np.zeros((8, 8, 8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (16, 16, 16, 3))
# 4D
scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
mode='constant')
assert_equal(scaled.shape, (16, 16, 16, 6))
def test_downscale_to_the_limit():
img = np.random.rand(3, 4)
out = rescale(img, 1e-3)
assert out.size == 1
