def test_relative_scaling(self):
img = self.imt
data = {}
data["img"] = img
lower = 10
upper = 99
b_min = 100
b_max = 300
scaler = ScaleIntensityRangePercentilesd(keys=data.keys(),
lower=lower,
upper=upper,
b_min=b_min,
b_max=b_max,
relative=True)
expected_a_min = np.percentile(img, lower)
expected_a_max = np.percentile(img, upper)
expected_b_min = ((b_max - b_min) * (lower / 100.0)) + b_min
expected_b_max = ((b_max - b_min) * (upper / 100.0)) + b_min
expected_img = (img - expected_a_min) / (expected_a_max -
expected_a_min)
expected_img = (expected_img *
(expected_b_max - expected_b_min)) + expected_b_min
np.testing.assert_allclose(expected_img, scaler(data)["img"])
def test_scaling(self):
img = self.imt
data = {}
data["img"] = img
lower = 10
upper = 99
b_min = 0
b_max = 255
a_min = np.percentile(img, lower)
a_max = np.percentile(img, upper)
expected = (img - a_min) / (a_max - a_min)
expected = (expected * (b_max - b_min)) + b_min
scaler = ScaleIntensityRangePercentilesd(keys=data.keys(), lower=lower, upper=upper, b_min=b_min, b_max=b_max)
self.assertTrue(np.allclose(expected, scaler(data)["img"]))
def test_scaling(self):
img = self.imt
lower = 10
upper = 99
b_min = 0
b_max = 255
a_min = np.percentile(img, lower)
a_max = np.percentile(img, upper)
expected = (((img - a_min) / (a_max - a_min)) * (b_max - b_min) +
b_min).astype(np.uint8)
for p in TEST_NDARRAYS:
data = {"img": p(img)}
scaler = ScaleIntensityRangePercentilesd(keys=data.keys(),
lower=lower,
upper=upper,
b_min=b_min,
b_max=b_max,
dtype=np.uint8)
assert_allclose(p(expected), scaler(data)["img"], rtol=1e-4)
def test_invalid_instantiation(self):
self.assertRaises(ValueError,
ScaleIntensityRangePercentilesd,
keys=["img"],
lower=-1,
upper=99,
b_min=0,
b_max=255)
self.assertRaises(ValueError,
ScaleIntensityRangePercentilesd,
keys=["img"],
lower=101,
upper=99,
b_min=0,
b_max=255)
self.assertRaises(ValueError,
ScaleIntensityRangePercentilesd,
keys=["img"],
lower=30,
upper=-2,
b_min=0,
b_max=255)
self.assertRaises(ValueError,
ScaleIntensityRangePercentilesd,
keys=["img"],
lower=30,
upper=1000,
b_min=0,
b_max=255)
with self.assertRaises(ValueError):
s = ScaleIntensityRangePercentilesd(keys=["img"],
lower=30,
upper=90,
b_min=None,
b_max=20,
relative=True)
s(self.imt)
def test_channel_wise(self):
img = np.tile(self.imt, (3, 1, 1, 1))
lower = 10
upper = 99
b_min = 0
b_max = 255
scaler = ScaleIntensityRangePercentilesd(keys="img",
lower=lower,
upper=upper,
b_min=b_min,
b_max=b_max,
channel_wise=True,
dtype=np.uint8)
expected = []
for c in img:
a_min = np.percentile(c, lower)
a_max = np.percentile(c, upper)
expected.append((((c - a_min) / (a_max - a_min)) *
(b_max - b_min) + b_min).astype(np.uint8))
expected = np.stack(expected)
for p in TEST_NDARRAYS:
data = {"img": p(img)}
assert_allclose(scaler(data)["img"], p(expected), rtol=1e-4)