def test_value(self):
key = "img"
factor = np.random.rand()
offset = np.std(self.imt) * factor
shifter = ShiftIntensityd(keys=[key], offset=offset)
expected = shifter({key: self.imt})
std_shifter = StdShiftIntensityd(keys=[key], factor=factor)
result = std_shifter({key: self.imt})
np.testing.assert_allclose(result[key], expected[key], rtol=1e-5)
def test_dtype(self):
key = "img"
trans_dtype = np.float32
for dtype in [int, np.float32, np.float64]:
image = np.random.rand(2, 2, 2).astype(dtype)
factor = np.random.rand()
std_shifter = StdShiftIntensityd(keys=[key],
factor=factor,
dtype=trans_dtype)
result = std_shifter({key: image})
np.testing.assert_equal(result[key].dtype, trans_dtype)
def test_nonzero(self):
key = "img"
image = np.asarray([[4.0, 0.0, 2.0], [0, 2, 4]]) # std = 1
factor = np.random.rand()
std_shifter = StdShiftIntensityd(keys=[key],
factor=factor,
nonzero=True)
result = std_shifter({key: image})
expected = np.asarray([[4 + factor, 0, 2 + factor],
[0, 2 + factor, 4 + factor]])
np.testing.assert_allclose(result[key], expected, rtol=1e-5)
def test_zerostd(self):
key = "img"
image = np.ones([2, 3, 3])
for nonzero in [True, False]:
for channel_wise in [True, False]:
factor = np.random.rand()
std_shifter = StdShiftIntensityd(keys=[key],
factor=factor,
nonzero=nonzero,
channel_wise=channel_wise)
result = std_shifter({key: image})
np.testing.assert_allclose(result[key], image, rtol=1e-5)
def test_channel_wise(self):
key = "img"
image = np.stack(
(np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0]))) # std: 0.5, 0
factor = np.random.rand()
std_shifter = StdShiftIntensityd(keys=[key],
factor=factor,
channel_wise=True)
result = std_shifter({key: image})
expected = np.stack((np.asarray([1 + 0.5 * factor,
2 + 0.5 * factor]), np.asarray([1,
1])))
np.testing.assert_allclose(result[key], expected, rtol=1e-5)