def test_scale_range_from_number(scale, expected):
if expected is not None:
trf = slt.Scale(range_x=scale, range_y=scale)
assert trf.range_x == expected
assert trf.range_x == expected
else:
with pytest.raises(ValueError):
slt.Scale(range_x=scale)
with pytest.raises(ValueError):
slt.Scale(range_x=None, range_y=scale)
def test_scale_xy_axis_even(img_6x6):
stream = slt.Scale((0.5, 0.5), (2, 2), same=False, p=1, ignore_fast_mode=True)
dc = slc.DataContainer((img_6x6,), "I")
H, W = img_6x6.shape[0], img_6x6.shape[1]
img_res = stream(dc)[0][0]
assert H * 2 == img_res.shape[0]
assert W // 2 == img_res.shape[1]
def test_scale_xy_axis_odd(img_5x5):
stream = slt.Scale((0.5, 0.5), (3, 3), same=False, p=1, ignore_fast_mode=True)
dc = slc.DataContainer((img_5x5,), "I")
H, W = img_5x5.shape[0], img_5x5.shape[1]
img_res = stream(dc)[0][0]
assert H * 3 == img_res.shape[0]
assert W // 2 == img_res.shape[1]
def test_zoom_x_axis_odd(img_5x5):
stream = slt.Scale(range_x=(0.5, 0.5), range_y=(1, 1), same=False, p=1, ignore_fast_mode=True)
dc = slc.DataContainer((img_5x5,), "I")
H, W = img_5x5.shape[0], img_5x5.shape[1]
img_res = stream(dc)[0][0]
assert H == img_res.shape[0]
assert W // 2 == img_res.shape[1]
def test_scale_img_mask(img_3x4, mask_3x4):
img_mask_3x4 = img_3x4, mask_3x4
stream = slt.Scale((0.5, 0.5), (2, 2), same=False, p=1, ignore_fast_mode=True)
dc = slc.DataContainer(img_mask_3x4, "IM")
H, W = img_mask_3x4[0].shape[0], img_mask_3x4[0].shape[1]
img_res = stream(dc)[0][0]
mask_res = stream(dc)[1][0]
assert H * 2 == img_res.shape[0], W // 2 == img_res.shape[1]
assert H * 2 == mask_res.shape[0], W // 2 == mask_res.shape[1]
def test_fusion_happens(img_5x5):
ppl = slc.Stream([
slt.Scale((0.5, 1.5), (0.5, 1.5), p=1),
slt.Rotate((-50, 50), padding='z', p=1),
slt.Shear((-0.5, 0.5), (-0.5, 0.5), padding='z', p=1),
])
dc = slc.DataContainer(img_5x5, 'I')
st = ppl.optimize_transforms_stack(ppl.transforms, dc)
assert len(st) == 1
def test_scale_x_axis_even(img):
stream = slt.Scale((0.5, 0.5), (1, 1),
same=False,
p=1,
ignore_fast_mode=True)
dc = slc.DataContainer((img, ), "I")
H, W = img.shape[:2]
img_res = stream(dc)[0][0]
assert H == img_res.shape[0]
assert W // 2 == img_res.shape[1]
def my_transforms():
train_trf = solt.Stream([
slt.Scale(range_x=(0.9, 1.1), same=False, p=0.5),
slt.Shear(range_x=(-0.05, 0.05), p=0.5),
slt.Rotate((-5, 5), p=0.5),
slt.Pad(pad_to=(32, 32))
])
test_trf = solt.Stream([slt.Pad(pad_to=(32, 32))])
return {'train': train_trf, 'eval': test_trf}
def test_random_proj_and_selective_stream(img):
dc = slc.DataContainer((img, ), "I")
ppl = slt.Projection(
slc.SelectiveStream(
[
slt.Rotate(angle_range=(90, 90), p=0),
slt.Scale(range_y=(0, 0.1), same=True, p=0),
slt.Shear(range_y=(-0.1, 0.1), p=0),
],
n=3,
),
v_range=(0, 0),
)
dc_res = ppl(dc)
assert np.array_equal(dc.data, dc_res.data)
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()
}
},
}
},
]
}
},
slc.Stream([
slc.Stream([slt.Pad(34),
slt.Crop(32, "r"),
slt.CutOut(2)]),
slt.Pad(34),
slt.Crop(32, "c"),
slt.CutOut(4),
slt.Projection(slc.Stream([slt.Rotate(30),
slt.Scale(2)]),
v_range=(0, 1e-3)),
]),
],
],
)
def test_deserialize_from_dict_nested(serialized: dict, stream: slc.Stream):
serialized_trfs = json.dumps(stream.to_dict())
serialized_from_deserialized = json.dumps(
slu.from_dict(serialized).to_dict())
assert serialized_trfs == serialized_from_deserialized
def test_stream_serializes_all_args_are_set():
ppl = slc.Stream([
def test_scale_sampling_scale(same, scale_x, scale_y, expected, img_6x6_rgb):
dc = slc.DataContainer(img_6x6_rgb, "I")
trf = slt.Scale(range_x=scale_x, range_y=scale_y, same=same)
trf.sample_transform(dc)
assert expected == (trf.state_dict["scale_x"], trf.state_dict["scale_y"])
def test_scale_when_range_x_is_none(scale_x, scale_y, same, expected,
img_6x6_rgb):
dc = slc.DataContainer(img_6x6_rgb, "I")
trf = slt.Scale(range_x=scale_x, range_y=scale_y, same=same, p=1)
trf.sample_transform(dc)
assert (trf.state_dict["scale_x"], trf.state_dict["scale_y"]) == expected
