def __init__(self):
self.imgaug_transform = iaa.Add((127, 127), per_channel=False)
self.augmentor_op = Operations.RandomBrightness(probability=1,
min_factor=1.5,
max_factor=1.5)
self.solt_stream = slc.Stream(
[slt.Brightness(p=1, brightness_range=(127, 127))])
def test_brightness_returns_correct_number_of_channels(img_3x4, img_6x6_rgb):
trf = slt.Brightness(p=1, brightness_range=(10, 10))
dc = slc.DataContainer((img_3x4, img_3x4, img_6x6_rgb), "III")
dc_res = trf(dc)
img1, img2, img3 = dc_res.data
assert len(img1.shape) == 3
assert img1.shape[-1] == 1
assert len(img2.shape) == 3
assert img2.shape[-1] == 1
assert len(img3.shape) == 3
assert img3.shape[-1] == 3
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 __init__(self):
self.imgaug_transform = iaa.Sequential([
iaa.Multiply((1.5, 1.5), per_channel=False),
iaa.Add((127, 127), per_channel=False)
])
self.augmentor_pipeline = Pipeline()
self.augmentor_pipeline.add_operation(
Operations.RandomBrightness(probability=1,
min_factor=1.5,
max_factor=1.5))
self.augmentor_pipeline.add_operation(
Operations.RandomContrast(probability=1,
min_factor=1.5,
max_factor=1.5))
self.solt_stream = slc.Stream([
slt.Brightness(p=1, brightness_range=(127, 127)),
slt.Contrast(p=1, contrast_range=(1.5, 1.5))
])