def augmentation(p: float = 0.5) -> Callable:
"""Data augmentation pipeline.
Fairly basic, can be improved. It would also be nice to define add this to hparams so we can include them in hyperparameter searches"""
return Compose([
RandomRotate90(),
Flip(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2),
ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
IAASharpen(),
RandomBrightnessContrast(),
], p=0.3),
],
p=p)
def strong_aug(p=1.0):
return Compose([
Flip(p=0.75),
ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=30, border_mode=cv2.BORDER_CONSTANT, p=0.3),
RandomBrightnessContrast(brightness_limit=0.1, contrast_limit=0.1, p=0.3),
# RandomSizedCrop(min_max_height=(0,0), height=0, width=0, w2h_ratio=0, p=0.3)
# OneOf([
# Blur(blur_limit=5, p=1.0),
# MedianBlur(blur_limit=5, p=1.0),
# MotionBlur(p=1.0),
# ], p=0.2),
# OneOf([
# HueSaturationValue(hue_shift_limit=10, sat_shift_limit=30, val_shift_limit=20, p=1.0),
# RGBShift(p=1.0)
# ], p=0.1),
# GaussNoise(p=0.1),
# OneOf([
# GridDistortion(p=1.0),
# ElasticTransform(p=1.0)
# ], p=0.2),
# OneOf([
# CLAHE(p=1.0),
# IAASharpen(p=1.0),
# ], p=0.2)
], p=p)
def get_aug(p=.9):
return Compose(
[
OneOf(
[
RandomRotate90(),
Flip(),
# ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.1, rotate_limit=10)
],
p=p),
# OneOf([
# IAAAdditiveGaussianNoise(),
# GaussNoise(),
# ], p = 0.0),
# OneOf([
# MotionBlur(p = .2),
# MedianBlur(blur_limit = 3, p = 0.1),
# Blur(blur_limit = 3, p = 0.0),
# ], p = 0.1),
# OneOf([
# OpticalDistortion(p = 0.1),
# GridDistortion(p = .1),
# IAAPiecewiseAffine(p = 0.3),
# ], p = 0.0),
# OneOf([
# CLAHE(clip_limit = 2),
# IAASharpen(),
# IAAEmboss(),
# RandomBrightnessContrast(),
# ], p = 0.0),
Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
],
p=1.0)
def aug(self, image):
'''
center crop, resize, affine, hue, saturation,
'''
if self.phase == 'train':
imgaug = Compose([
transforms.Resize(height=self.input_shape[0], width=self.input_shape[1]),
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=.2),
MedianBlur(blur_limit=3, p=.1),
Blur(blur_limit=3, p=.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=.2),
ToTensorV2(),
], p=1)
else:
imgaug = Compose([transforms.Resize(height=self.input_shape[0], width=self.input_shape[1]),
ToTensorV2()])
image = imgaug(image=image)['image']
return image
def aug2(p=1.0):
return Compose([
Flip(p=0.75),
ShiftScaleRotate(
rotate_limit=0, border_mode=cv2.BORDER_CONSTANT, p=0.5),
],
p=p)
def aug_daniel(prob=0.8):
return Compose(
[
RandomRotate90(p=0.5),
Transpose(p=0.5),
Flip(p=0.5),
OneOf(
[
IAAAdditiveGaussianNoise(),
GaussNoise(),
#Blur(),
],
p=0.3),
OneOf(
[
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
OneOf([
RandomContrast(),
RandomBrightness(),
]),
#Blur(),
#GaussNoise()
],
p=0.5),
HueSaturationValue(p=0.5)
],
p=prob)
def strong_aug(p=0.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.1),
IAAPiecewiseAffine(p=0.3),
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
], p=0.3),
HueSaturationValue(p=0.3),
], p=p)
def train_aug(self, image, label):
aug = Compose(
[
OneOf(
[CLAHE(), IAASharpen(), IAAEmboss()], p=0.5),
# OneOf([IAAAdditiveGaussianNoise(), GaussNoise()], p=0.2),
# OneOf([MotionBlur(p=0.2), MedianBlur(blur_limit=3, p=0.1), Blur(blur_limit=3, p=0.1)], p=0.2),
RandomContrast(),
RandomBrightness(),
# ChannelShuffle(),
RandomRotate90(),
Flip(),
# RandomScale(scale_limit=(0.0, 0.1)),
OneOf([
ElasticTransform(),
OpticalDistortion(),
GridDistortion(),
IAAPiecewiseAffine()
],
p=0.5),
# HueSaturationValue(p=0.3),
],
p=0.9)
augmented = aug(image=image, mask=label)
augmented = ToGray(p=1)(image=augmented['image'],
mask=augmented['mask'])
augmented = RandomCrop(256, 256)(image=augmented['image'],
mask=augmented['mask'])
image, label = augmented['image'], augmented['mask']
return image, label
def get_input_pair(self, image_info):
dataset_path = self.dataset_path
img_path = os.path.join(dataset_path, image_info["dataset_folder"], self.images_folder,
image_info["name"] + '_' + image_info["position"] + '.' + self.image_type)
mask_path = os.path.join(dataset_path, image_info["dataset_folder"], self.masks_folder,
image_info["name"] + '_' + image_info["position"] + '.' + self.mask_type)
img = Image.open(img_path)
mask = Image.open(mask_path)
img_array = np.array(img)
mask_array = np.array(mask).astype(np.float32)
aug = Compose([
RandomCrop(224, 224),
RandomRotate90(),
Flip(),
OneOf([
RGBShift(),
CLAHE(clip_limit=2)
], p=0.4),
ToTensor()
], p=1)
augmented = aug(image=img_array, mask=mask_array)
augmented_img = augmented['image']
augmented_mask = augmented['mask']
augmented_mask = augmented_mask.squeeze().long()
return {"features": augmented_img, "targets": augmented_mask}
def get_input_pair(self, image_info):
dataset_path = self.dataset_path
img_path = os.path.join(
dataset_path, image_info["dataset_folder"], self.images_folder,
image_info["name"] + '_' + image_info["position"] + '.' +
self.image_type)
img = Image.open(img_path)
img_array = np.array(img)
augm = Compose([
RandomCrop(224, 224),
RandomRotate90(),
Flip(),
OneOf([RGBShift(), CLAHE(clip_limit=2)], p=0.4),
ToTensor()
],
p=1)
augmented = augm(image=img_array)
augmented_img = augmented['image']
return {"features": augmented_img, "targets": augmented_img}
def strong_aug(p=1):
return Compose([
ToFloat(),
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2),
ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.1),
IAAPiecewiseAffine(p=0.3),
],
p=0.2),
FromFloat(dtype='uint16', max_value=65535.0)
],
p=p)
def get_train_transforms():
augmentations = Compose([
Resize(236,236),
Flip(),
OneOf([
IAAAdditiveGaussianNoise(p=.5),
GaussNoise(p=.4),
], p=0.4),
OneOf([
MotionBlur(p=0.6),
Blur(blur_limit=3, p=0.2),
], p=0.4),
ShiftScaleRotate(shift_limit=0.0725, scale_limit=0.2, rotate_limit=45, p=0.6),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.4),
IAAPiecewiseAffine(p=0.2),
], p=0.3),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
], p=0.25),
HueSaturationValue(p=0.3),
CenterCrop(224,224),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
ToTensor()
])
return lambda img:augmentations(image=np.array(img))
def Rotate_Crop(img, v): # [-90, 90]
assert -90 <= v <= 90
if random.random() < 0.5:
v = -v
transform = Compose([Flip(), Rotate(limit=v, interpolation=2), CenterCrop(img.shape[1], img.shape[1])])
Aug_img = transform(image=img)
return Aug_img
def strong_aug(p=1.0):
return Compose(
[
Flip(p=0.75), # ok
# RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
# RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=1.0, brightness_by_max=False),
# # RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
# ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=45, border_mode=cv2.BORDER_CONSTANT, p=0.2),
# OneOf([
# Blur(blur_limit=5, p=1.0),
# MedianBlur(blur_limit=5, p=1.0),
# MotionBlur(p=1.0),
# ], p=0.2),
# OneOf([
# HueSaturationValue(hue_shift_limit=10, sat_shift_limit=30, val_shift_limit=20, p=1.0),
# RGBShift(p=1.0)
# ], p=0.1),
# GaussNoise(p=0.1),
# OneOf([
# GridDistortion(p=1.0),
# ElasticTransform(p=1.0)
# ], p=0.2),
# OneOf([
# CLAHE(p=1.0),
# IAASharpen(p=1.0),
# ], p=0.2)
],
p=p)
def get_transforms():
return Compose([
RandomRotate90(p=0.5),
Flip(p=0.5),
Transpose(p=0.5),
# OneOf([
# IAAAdditiveGaussianNoise(),
# GaussNoise(),
# ], p=0.2),
# OneOf([
# MotionBlur(p=.2),
# MedianBlur(blur_limit=3, p=0.1),
# Blur(blur_limit=3, p=0.1),
# ], p=0.2),
ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.2,
rotate_limit=45,
p=0.2),
# OneOf([
# CLAHE(clip_limit=2),
# IAASharpen(),
# IAAEmboss(),
# RandomBrightnessContrast(),
# ], p=0.3),
HueSaturationValue(p=0.3),
])
def build_train(self):
return Compose([
RandomCrop(self.H, 416),
Flip(p=0.5),
Normalize(mean=self.MEAN, std=self.STD),
ToTensor(),
])
def alb_transform_train(imsize = 256, p=1):
albumentations_transform = Compose([
# RandomCrop(imsize),
# RandomRotate90(),
Flip(),
# Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=.2),
MedianBlur(blur_limit=3, p=.1),
Blur(blur_limit=3, p=.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=.1),
IAAPiecewiseAffine(p=0.3),
], p=0.2),
OneOf([
# CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomContrast(),
RandomBrightness(),
], p=0.3),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
], p=p)
return albumentations_transform
def transform(config, image, mask):
try:
p = config["train"]["dap"]["p"]
except:
p = 1
assert 0 <= p <= 1
# Inspire by: https://albumentations.readthedocs.io/en/latest/examples.html
return Compose([
Flip(),
Transpose(),
OneOf([IAAAdditiveGaussianNoise(),
GaussNoise()], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1)
],
p=0.2),
ShiftScaleRotate(shift_limit=0.0625,
scale_limit=0.2,
rotate_limit=45,
p=0.2),
OneOf([IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast()], p=0.3),
HueSaturationValue(p=0.3),
])(image=image, mask=mask, p=p)
def strong_aug(p=1):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
],
p=0.3),
HueSaturationValue(p=0.3),
],
p=p)
def gettraintransforms(self, mean, std, p=1):
# Train Phase transformations
albumentations_transform = Compose([
# RandomRotate90(),
PadIfNeeded(72, 72, border_mode=cv2.BORDER_REFLECT, always_apply=True),
RandomCrop(64, 64, True),
Flip(),
GaussNoise(p=0.8, mean=mean),
OneOf([
MotionBlur(p=0.4),
MedianBlur(blur_limit=3, p=0.2),
Blur(blur_limit=3, p=0.2),
], p=0.4),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.6),
OneOf([
OpticalDistortion(p=0.8),
GridDistortion(p=0.4),
], p=0.6),
HueSaturationValue(hue_shift_limit=20, sat_shift_limit=0.1, val_shift_limit=0.1, p=0.6),
CoarseDropout(always_apply=True, max_holes=1, min_holes=1, max_height=16, max_width=16,
fill_value=(255 * .6), min_height=16, min_width=16),
Normalize(mean=mean, std=std, always_apply=True),
pytorch.ToTensorV2(always_apply=True),
], p=p)
return albumentations_transform;
def strong_aug(p=1):
return Compose([
OneOf([
RandomRotate90(p=1),
Flip(p=1),
], p=1),
ShiftScaleRotate(shift_limit=0.1,
scale_limit=0.1,
rotate_limit=45,
p=1,
value=0,
border_mode=2),
OneOf([
IAAAdditiveGaussianNoise(p=0.7),
GaussNoise(p=0.7),
], p=1),
OneOf([
MotionBlur(p=0.7),
MedianBlur(blur_limit=3, p=0.7),
Blur(blur_limit=3, p=0.7),
],
p=1),
RandomBrightnessContrast(p=0.5),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(p=0.7),
],
p=1)
],
p=p)
def gettraintransforms(self, mean, std, p=1):
# Train Phase transformations
albumentations_transform = Compose([
RandomRotate90(),
Flip(),
GaussNoise(p=0.6, mean=mean),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2),
ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.3),
OneOf([
OpticalDistortion(p=0.4),
GridDistortion(p=0.2),
], p=0.3),
HueSaturationValue(hue_shift_limit=20,
sat_shift_limit=0.1,
val_shift_limit=0.1,
p=0.3),
Cutout(always_apply=True,
num_holes=2,
max_h_size=10,
max_w_size=10,
fill_value=(255 * .6)),
Normalize(mean=mean, std=std, always_apply=True),
pytorch.ToTensorV2(always_apply=True),
],
p=p)
return albumentations_transform
def aug_daniel_prepadded(prob=1.0, image_size=448): return Compose([ RandomRotate90(p=0.5), Transpose(p=0.5), Flip(p=0.5), OneOf([ RandomCrop(height=image_size, width=image_size, p=0.3), Compose([ #3.94 determined by largest angle possible rotatable without introducing nodata pixels into center crop area ShiftScaleRotate(shift_limit=0.0, scale_limit=0.0, rotate_limit=6, border_mode=cv2.BORDER_CONSTANT, p=1.0), CenterCrop(height=int(round(236/224*image_size)), width=int(round(236/224*image_size)), p=1.0), RandomCrop(height=image_size, width=image_size, p=1.0) ], p=0.4), Compose([ #3.94 determined by largest angle possible rotatable without introducing nodata pixels into center crop area ShiftScaleRotate(shift_limit=0.0, scale_limit=0.0, rotate_limit=12, border_mode=cv2.BORDER_CONSTANT, p=1.0), CenterCrop(height=image_size, width=image_size, p=1.0) ], p=0.3) ], p=1.0), #OneOf([ #IAASharpen(), #IAAEmboss(), # RandomBrightnessContrast(brightness_limit=0.01, contrast_limit=0.01) # This causes a blackout for some reason #Blur(), #GaussNoise() #], p=0.5), IAASharpen(p=0.2), IAAAdditiveGaussianNoise(p=0.2), # HueSaturationValue(p=0.3) #ShiftScaleRotate(shift_limit=0.0, scale_limit=0.0, rotate_limit=2, border_mode=cv2.BORDER_CONSTANT, p=.75), #ChannelShuffle(p=0.33) ], p=prob)
def strong_aug2(p=1.0):
return Compose(
[
Flip(p=0.75), # ok
# RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
RandomBrightnessContrast(brightness_limit=(-0.2, 0.2),
contrast_limit=0.2,
p=1.0,
brightness_by_max=False),
ShiftScaleRotate(shift_limit=0.1,
scale_limit=0.2,
rotate_limit=45,
border_mode=cv2.BORDER_CONSTANT,
p=0.2),
OneOf([
IAASharpen(p=1),
Blur(blur_limit=5, p=1.0),
MedianBlur(blur_limit=5, p=1.0),
MotionBlur(p=1.0),
],
p=0.6),
OneOf([
HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=30,
val_shift_limit=20,
p=1.0),
RGBShift(p=1.0),
RandomGamma(p=1),
],
p=0.3),
IAAAdditiveGaussianNoise(p=.2),
],
p=p)
def train_aggressive_transform(from_dicom: bool, longest_max_size: int) -> Compose:
additional_transforms = [
Flip(),
RandomRotate90(),
RandomBrightnessContrast(),
GaussNoise(),
]
return train_transform(from_dicom, longest_max_size, additional_transforms)
def _albumentations_strong_aug() -> Compose:
return Compose([
ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1,
rotate_limit=20),
Flip(),
# ElasticTransform(alpha=720, sigma=24, approximate=False),
# RandomBrightnessContrast(),
])
def build_train(self):
return Compose([
CropNonEmptyMaskIfExists(self.H, self.H),
Flip(p=0.5),
RandomRotate90(p=0.5),
Normalize(mean=self.MEAN, std=self.STD),
ToTensor(),
])
def aug4(p=1.0):
return Compose([
Flip(p=0.75),
ShiftScaleRotate(
rotate_limit=0, border_mode=cv2.BORDER_CONSTANT, p=0.3),
GaussianBlur(blur_limit=3, p=0.3),
],
p=p)
def strong_aug(p=0.5, crop_size=(512, 512)):
return Compose([
RandomResizedCrop(crop_size[0],
crop_size[1],
scale=(0.3, 1.0),
ratio=(0.75, 1.3),
interpolation=4,
p=1.0),
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.8),
OneOf([
MotionBlur(p=0.5),
MedianBlur(blur_limit=3, p=0.5),
Blur(blur_limit=3, p=0.5),
],
p=0.3),
ShiftScaleRotate(
shift_limit=0.2, scale_limit=0.5, rotate_limit=180, p=0.8),
OneOf([
OpticalDistortion(p=0.5),
GridDistortion(p=0.5),
IAAPiecewiseAffine(p=0.5),
ElasticTransform(p=0.5),
],
p=0.3),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomBrightnessContrast(),
],
p=0.3),
OneOf([
GaussNoise(),
RandomRain(
p=0.2, brightness_coefficient=0.9, drop_width=1, blur_value=5),
RandomSnow(p=0.4,
brightness_coeff=0.5,
snow_point_lower=0.1,
snow_point_upper=0.3),
RandomShadow(p=0.2,
num_shadows_lower=1,
num_shadows_upper=1,
shadow_dimension=5,
shadow_roi=(0, 0.5, 1, 1)),
RandomFog(
p=0.5, fog_coef_lower=0.3, fog_coef_upper=0.5, alpha_coef=0.1)
],
p=0.3),
RGBShift(),
HueSaturationValue(p=0.9),
],
p=p)
def __init__(self, size, root, phase):
if phase == 'train':
self.data_list = open(os.path.join(root, 'train.txt'), 'r').readlines()
self.transform = Compose([Flip(),
RandomResizedCrop(width=size, height=size, scale=(0.8, 1.2)),
])
else:
self.data_list = open(os.path.join(root, 'valid.txt'), 'r').readlines()
self.transform = Compose([Resize(width=size, height=size)])
