def create_train_transforms(size):
return solt.Stream([
slt.JPEGCompression(p=0.5,quality_range=(60,100)),
slt.Noise(p=0.25),
slt.Brightness(),
slt.Contrast(),
slt.Flip(),
slt.Rotate90(),
solt.SelectiveStream([
slt.GammaCorrection(gamma_range=0.5, p=1),
slt.Noise(gain_range=0.1, p=1),
slt.SaltAndPepper(),
slt.Blur(),
], n=3),
slt.Rotate(angle_range=(-10, 10), p=0.5),
slt.Resize((size,size)),
])
def test_gaussian_noise_no_image_throws_value_error():
trf = slt.Noise(p=1)
# Setting up the data
kpts_data = np.array([[0, 0], [0, 5], [1, 3], [2, 0]]).reshape((4, 2))
kpts = slc.Keypoints(kpts_data, 6, 6)
dc = slc.DataContainer((kpts, ), "P")
with pytest.raises(ValueError):
trf(dc)
def test_complex_transform_serialization():
stream = slc.Stream([
slt.Flip(axis=1, p=0.5),
slc.SelectiveStream([
slt.Rotate(angle_range=(-45, -45), p=1, padding="r"),
slt.Rotate90(1, p=1),
slt.Rotate(angle_range=(45, 45), p=1, padding="r"),
]),
slt.Crop((350, 350)),
slc.SelectiveStream([
slt.GammaCorrection(gamma_range=0.5, p=1),
slt.Noise(gain_range=0.1, p=1),
slt.Blur()
],
n=3),
slt.Projection(
affine_transforms=slc.Stream([
slt.Rotate(angle_range=(-45, 45), p=1),
slt.Scale(range_x=(0.8, 1.5),
range_y=(0.8, 1.5),
p=1,
same=False),
]),
v_range=(1e-4, 1e-3),
p=1,
),
slc.SelectiveStream(
[
slt.CutOut(40, p=1),
slt.CutOut(30, p=1),
slt.CutOut(20, p=1),
slt.CutOut(40, p=1),
slc.Stream(),
slc.Stream(),
slc.Stream(),
],
n=3,
),
])
assert slu.from_yaml(stream.to_yaml()).to_yaml() == slu.from_yaml(
stream.to_yaml()).to_yaml()
def test_image_doesnt_change_when_gain_0_in_gaussian_noise_addition(img_3x3):
dc = slc.DataContainer((img_3x3, ), "I")
trf = slt.Noise(gain_range=(0, 0), p=1)
dc_res = trf(dc)
np.testing.assert_array_equal(img_3x3, dc_res.data[0])
def test_gaussian_noise_float_gain():
trf = slt.Noise(gain_range=0.2, p=1)
assert isinstance(trf.gain_range, tuple)
assert len(trf.gain_range) == 2
assert trf.gain_range[0] == 0 and trf.gain_range[1] == 0.2