def test_cutout_blacks_out_image(img, expected, cut_size):
dc = slc.DataContainer((img, ), "I")
trf = slc.Stream([slt.CutOut(p=1, cutout_size=cut_size)])
dc_res = trf(dc, return_torch=False)
assert np.array_equal(expected, dc_res.data[0])
def test_deserialize_from_dict(serialized):
trfs = slc.Stream([slt.Pad(34), slt.Crop(32, "r"), slt.CutOut(2)])
serialized_trfs = json.dumps(trfs.to_dict())
serialized_from_deserialized = json.dumps(
slu.from_dict(serialized).to_dict())
assert serialized_trfs == serialized_from_deserialized
def test_crop_or_cutout_size_are_too_big(img_2x2, cutout_crop_size):
dc = slc.DataContainer((img_2x2, ), "I")
trf = slt.Crop(crop_to=cutout_crop_size)
with pytest.raises(ValueError):
trf(dc)
trf = slt.CutOut(p=1, cutout_size=cutout_crop_size)
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 my_transforms():
train_trf = solt.Stream([
slt.Pad(pad_to=(36, 36)),
slt.Rotate(10),
slt.Crop((32, 32)),
slt.CutOut((8, 8)),
slt.Flip(p=0.5)
])
test_trf = solt.Stream([])
return {'train': train_trf, 'eval': test_trf}
def my_transforms():
train_trf = solt.Stream([
# slt.Scale(range_x=(0.9, 1.1), range_y=(0.9, 1.1), same=True, p=0.5),
# slt.Translate(range_x=(-0.05, 0.05), range_y=(-0.05, 0.05), p=0.5),
# slt.GammaCorrection(gamma_range=0.1, p=0.5),
slt.Pad(pad_to=(36, 36)),
slt.Rotate((-5, 5), p=0.5),
slt.Crop((32, 32)),
# slt.Noise(gain_range=0.1, p=0.8),
slt.CutOut((8, 8))
])
test_trf = solt.Stream([])
custom_trf = solt.Stream([
slt.Pad(pad_to=(36, 36)),
slt.Rotate((-5, 5), p=0.5),
slt.Crop((32, 32)),
# slt.Noise(gain_range=0.1, p=0.8),
slt.CutOut((8, 8))
])
return {'train': train_trf, 'eval': test_trf, 'transforms': custom_trf}
def test_cutout_1x1_blacks_corner_pixels_2x2_img(img_2x2, cut_size):
dc = slc.DataContainer((img_2x2.copy(), ), "I")
trf = slc.Stream([slt.CutOut(p=1, cutout_size=cut_size)])
dc_res = trf(dc, return_torch=False)
equal = 0
for i in range(2):
for j in range(2):
tmp_opt = img_2x2.copy()
tmp_opt[i, j] = 0
if np.array_equal(dc_res.data[0], tmp_opt):
equal += 1
assert equal == 1
{
"cutout": {
"cutout_size": 2,
"p": 0.5
}
},
],
},
},
]
}
},
slc.Stream([
slt.Pad(34),
slt.Crop(32, "r"),
slt.CutOut(2),
slc.Stream([slt.Pad(34),
slt.Crop(32, "r"),
slt.CutOut(2)]),
]),
],
[
{
"stream": {
"transforms": [
{
"stream": {
"interpolation":
None,
"padding":
None,
def test_wrong_crop_size_types(cutout_crop_size):
with pytest.raises(TypeError):
slt.Crop(crop_to=cutout_crop_size)
with pytest.raises(TypeError):
slt.CutOut(cutout_size=cutout_crop_size)
