def test_relative_scaling(self):
img = self.imt[0]
lower = 10
upper = 99
b_min = 100
b_max = 300
scaler = ScaleIntensityRangePercentiles(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
for p in TEST_NDARRAYS:
result = scaler(p(img))
assert_allclose(result, p(expected_img), rtol=1e-3)
scaler = ScaleIntensityRangePercentiles(
lower=lower, upper=upper, b_min=b_min, b_max=b_max, relative=True, clip=True
)
for p in TEST_NDARRAYS:
result = scaler(p(img))
assert_allclose(result, p(np.clip(expected_img, expected_b_min, expected_b_max)), rtol=1e-4)
def __init__(
self,
keys: KeysCollection,
lower: float,
upper: float,
b_min: float,
b_max: float,
clip: bool = False,
relative: bool = False,
) -> None:
super().__init__(keys)
self.scaler = ScaleIntensityRangePercentiles(lower, upper, b_min, b_max, clip, relative)
def test_scaling(self):
img = self.imt[0]
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)
scaler = ScaleIntensityRangePercentiles(lower=lower, upper=upper, b_min=b_min, b_max=b_max, dtype=np.uint8)
for p in TEST_NDARRAYS:
result = scaler(p(img))
assert_allclose(result, p(expected), rtol=1e-4)
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)
expected = (expected * (b_max - b_min)) + b_min
scaler = ScaleIntensityRangePercentiles(lower=lower,
upper=upper,
b_min=b_min,
b_max=b_max)
self.assertTrue(np.allclose(expected, scaler(img)))
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 = ScaleIntensityRangePercentiles(
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:
result = scaler(p(img))
assert_allclose(result, p(expected), rtol=1e-4)
def test_relative_scaling(self):
img = self.imt
lower = 10
upper = 99
b_min = 100
b_max = 300
scaler = ScaleIntensityRangePercentiles(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
self.assertTrue(np.allclose(expected_img, scaler(img)))