def test_multiplicative_noise_rgb(image):
dtype = image.dtype
m = 0.5
aug = A.MultiplicativeNoise(m, p=1)
result = aug(image=image)["image"]
image = F.clip(image * m, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape[:2] + (1,)
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(per_channel=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == (3,)
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, dtype, F.MAX_VALUES_BY_DTYPE[dtype])
assert np.allclose(image, result)
aug = A.MultiplicativeNoise(elementwise=True, per_channel=True, p=1)
params = aug.get_params_dependent_on_targets({"image": image})
mul = params["multiplier"]
assert mul.shape == image.shape
result = aug.apply(image, mul)
image = F.clip(image.astype(np.float32) * mul, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.allclose(image, result)
def test_multiply_uint8_optimized():
image = np.random.randint(0, 256, [256, 320], np.uint8)
m = 1.5
result = F._multiply_uint8_optimized(image, [m])
tmp = F.clip(image * m, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.all(tmp == result)
image = np.random.randint(0, 256, [256, 320, 3], np.uint8)
result = F._multiply_uint8_optimized(image, [m])
tmp = F.clip(image * m, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.all(tmp == result)
m = np.array([1.5, 0.75, 1.1])
image = np.random.randint(0, 256, [256, 320, 3], np.uint8)
result = F._multiply_uint8_optimized(image, m)
tmp = F.clip(image * m, image.dtype, F.MAX_VALUES_BY_DTYPE[image.dtype])
assert np.all(tmp == result)
def test_clip_float():
img = np.array(
[[-0.02, 0],
[0.5, 2.2]], dtype=np.float32)
expected = np.array(
[[0, 0],
[0.5, 1.0]], dtype=np.float32)
clipped = F.clip(img, dtype=np.float32, maxval=1.0)
assert_array_almost_equal_nulp(clipped, expected)
def test_clip():
img = np.array(
[[-300, 0],
[100, 400]], dtype=np.float32)
expected = np.array(
[[0, 0],
[100, 255]], dtype=np.float32)
clipped = F.clip(img, dtype=np.uint8, maxval=255)
assert np.array_equal(clipped, expected)
def test_clip():
img = np.array(
[[-300, 0],
[100, 400]], dtype=np.float32)
expected = np.array(
[[0, 0],
[100, 255]], dtype=np.float32)
clipped = F.clip(img, dtype=np.uint8, maxval=255)
assert np.array_equal(clipped, expected)
