def test_transform_pipeline_serialization_with_keypoints(seed, image, keypoints, keypoint_format, labels):
aug = A.Compose(
[
A.OneOrOther(
A.Compose([A.RandomRotate90(), A.OneOf([A.HorizontalFlip(p=0.5), A.VerticalFlip(p=0.5)])]),
A.Compose([A.Rotate(p=0.5), A.OneOf([A.HueSaturationValue(p=0.5), A.RGBShift(p=0.7)], p=1)]),
),
A.SomeOf(
n=2,
transforms=[
A.HorizontalFlip(p=1),
A.Transpose(p=1),
A.HueSaturationValue(p=0.5),
A.RandomBrightnessContrast(p=0.5),
],
replace=False,
),
],
keypoint_params={"format": keypoint_format, "label_fields": ["labels"]},
)
serialized_aug = A.to_dict(aug)
deserialized_aug = A.from_dict(serialized_aug)
set_seed(seed)
aug_data = aug(image=image, keypoints=keypoints, labels=labels)
set_seed(seed)
deserialized_aug_data = deserialized_aug(image=image, keypoints=keypoints, labels=labels)
assert np.array_equal(aug_data["image"], deserialized_aug_data["image"])
assert np.array_equal(aug_data["keypoints"], deserialized_aug_data["keypoints"])
def test_transform_pipeline_serialization(seed, image, mask):
aug = A.Compose([
A.OneOrOther(
A.Compose([
A.Resize(1024, 1024),
A.RandomSizedCrop(min_max_height=(256, 1024),
height=512,
width=512,
p=1),
A.OneOf([
A.RandomSizedCrop(min_max_height=(256, 512),
height=384,
width=384,
p=0.5),
A.RandomSizedCrop(min_max_height=(256, 512),
height=512,
width=512,
p=0.5),
]),
]),
A.Compose([
A.Resize(1024, 1024),
A.RandomSizedCrop(min_max_height=(256, 1025),
height=256,
width=256,
p=1),
A.OneOf([A.HueSaturationValue(p=0.5),
A.RGBShift(p=0.7)], p=1),
]),
),
A.SomeOf(
[
A.HorizontalFlip(p=1),
A.Transpose(p=1),
A.HueSaturationValue(p=0.5),
A.RandomBrightnessContrast(p=0.5),
],
2,
replace=False,
),
])
serialized_aug = A.to_dict(aug)
deserialized_aug = A.from_dict(serialized_aug)
set_seed(seed)
aug_data = aug(image=image, mask=mask)
set_seed(seed)
deserialized_aug_data = deserialized_aug(image=image, mask=mask)
assert np.array_equal(aug_data["image"], deserialized_aug_data["image"])
assert np.array_equal(aug_data["mask"], deserialized_aug_data["mask"])
def randAugment(N=2, M=4, p=1.0, mode="all", cut_out=False):
"""
Examples:
>>> # M from 0 to 20
>>> transforms = randAugment(N=3, M=8, p=0.8, mode='all', cut_out=False)
"""
# Magnitude(M) search space
scale = np.linspace(0, 0.4, 20)
translate = np.linspace(0, 0.4, 20)
rot = np.linspace(0, 30, 20)
shear_x = np.linspace(0, 20, 20)
shear_y = np.linspace(0, 20, 20)
contrast = np.linspace(0.0, 0.4, 20)
bright = np.linspace(0.0, 0.4, 20)
sat = np.linspace(0.0, 0.2, 20)
hue = np.linspace(0.0, 0.2, 20)
shar = np.linspace(0.0, 0.9, 20)
blur = np.linspace(0, 0.2, 20)
noise = np.linspace(0, 1, 20)
cut = np.linspace(0, 0.6, 20)
# Transformation search space
Aug = [ # geometrical
albumentations.Affine(scale=(1.0 - scale[M], 1.0 + scale[M]), p=p),
albumentations.Affine(translate_percent=(-translate[M], translate[M]),
p=p),
albumentations.Affine(rotate=(-rot[M], rot[M]), p=p),
albumentations.Affine(shear={'x': (-shear_x[M], shear_x[M])}, p=p),
albumentations.Affine(shear={'y': (-shear_y[M], shear_y[M])}, p=p),
# Color Based
albumentations.RandomContrast(limit=contrast[M], p=p),
albumentations.RandomBrightness(limit=bright[M], p=p),
albumentations.ColorJitter(brightness=0.0,
contrast=0.0,
saturation=sat[M],
hue=0.0,
p=p),
albumentations.ColorJitter(brightness=0.0,
contrast=0.0,
saturation=0.0,
hue=hue[M],
p=p),
albumentations.Sharpen(alpha=(0.1, shar[M]), lightness=(0.5, 1.0),
p=p),
albumentations.core.composition.PerChannel(albumentations.OneOf([
albumentations.MotionBlur(p=0.5),
albumentations.MedianBlur(blur_limit=3, p=1),
albumentations.Blur(blur_limit=3, p=1),
]),
p=blur[M] * p),
albumentations.GaussNoise(var_limit=(8.0 * noise[M], 64.0 * noise[M]),
per_channel=True,
p=p)
]
# Sampling from the Transformation search space
if mode == "geo":
transforms = albumentations.SomeOf(Aug[0:5], N)
elif mode == "color":
transforms = albumentations.SomeOf(Aug[5:], N)
else:
transforms = albumentations.SomeOf(Aug, N)
if cut_out:
cut_trans = albumentations.OneOf([
albumentations.CoarseDropout(
max_holes=8, max_height=16, max_width=16, fill_value=0, p=1),
albumentations.GridDropout(ratio=cut[M], p=1),
albumentations.Cutout(
num_holes=8, max_h_size=16, max_w_size=16, p=1),
],
p=cut[M])
transforms = albumentations.Compose([transforms, cut_trans])
return transforms
