def test_rescale():
# same scale factor
x = cp.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
scaled = rescale(x,
2,
order=0,
multichannel=False,
anti_aliasing=False,
mode='constant')
ref = cp.zeros((10, 10))
ref[2:4, 2:4] = 1
assert_array_almost_equal(scaled, ref)
# different scale factors
x = cp.zeros((5, 5), dtype=np.double)
x[1, 1] = 1
scaled = rescale(x, (2, 1),
order=0,
multichannel=False,
anti_aliasing=False,
mode='constant')
ref = cp.zeros((10, 5))
ref[2:4, 1] = 1
assert_array_almost_equal(scaled, ref)
def test_keep_range():
image = cp.linspace(0, 2, 25).reshape(5, 5)
out = rescale(image,
2,
preserve_range=False,
clip=True,
order=0,
mode='constant',
multichannel=False,
anti_aliasing=False)
assert out.min() == 0
assert out.max() == 2
out = rescale(image,
2,
preserve_range=True,
clip=True,
order=0,
mode='constant',
multichannel=False,
anti_aliasing=False)
assert out.min() == 0
assert out.max() == 2
out = rescale(image.astype(np.uint8),
2,
preserve_range=False,
mode='constant',
multichannel=False,
anti_aliasing=False,
clip=True,
order=0)
assert out.min() == 0
assert out.max() == 2 / 255.0
def test_rescale_multichannel_defaults():
x = cp.zeros((8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
assert_equal(scaled.shape, (16, 6))
x = cp.zeros((8, 8, 3), dtype=np.double)
scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
assert_equal(scaled.shape, (16, 16, 6))
def test_rescale_invalid_scale():
x = cp.zeros((10, 10, 3))
with pytest.raises(ValueError):
rescale(x, (2, 2),
multichannel=False,
anti_aliasing=False,
mode='constant')
with pytest.raises(ValueError):
rescale(x, (2, 2, 2),
multichannel=True,
anti_aliasing=False,
mode='constant')
def test_bool_array_warnings():
img = cp.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, cp.eye(3), order=1)
def test_rescale_multichannel_multiscale():
x = cp.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_img_rescale():
img = cp.ones((12, 18), dtype=bool)
img[2:-2, 4:-4] = False
res = rescale(img, 0.5)
expected = cp.ones((6, 9))
expected[1:-1, 2:-2] = False
assert_array_equal(res, expected)
def test_downscale_anti_aliasing():
x = cp.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(float(scaled[3:, :].sum()), 0)
assert_equal(float(scaled[:, 3:].sum()), 0)
def test_downscale():
x = cp.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(float(scaled[1, 1]), 1)
assert_equal(float(scaled[2:, :].sum()), 0)
assert_equal(float(scaled[:, 2:].sum()), 0)
def test_warp_clip():
x = cp.zeros((5, 5), dtype=np.double)
x[2, 2] = 1
outx = rescale(x,
3,
order=3,
clip=False,
multichannel=False,
anti_aliasing=False,
mode='constant')
assert outx.min() < 0
outx = rescale(x,
3,
order=3,
clip=True,
multichannel=False,
anti_aliasing=False,
mode='constant')
assert_almost_equal(float(outx.min()), 0)
assert_almost_equal(float(outx.max()), 1)
def test_rescale_multichannel():
# 1D + channels
x = cp.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_array_equal(scaled.shape, (16, 6))
# 2D + channels
x = cp.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 = cp.zeros((8, 8, 8, 3), dtype=np.double)
scaled = rescale(x,
2,
order=0,
multichannel=True,
anti_aliasing=False,
mode='constant')
assert_array_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))