def transform_v3(config): train_transforms = Compose([ ImageCompression(quality_lower=60, quality_upper=100, p=0.5), GaussNoise(p=1), GaussianBlur(blur_limit=3, p=1), HorizontalFlip(), Resize(config.image_size, config.image_size), OneOf([RandomBrightnessContrast(), FancyPCA(), HueSaturationValue()], p=1), ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=10, border_mode=cv2.BORDER_CONSTANT, p=1), ToTensor() ]) test_transforms = Compose([ GaussNoise(p=1), GaussianBlur(blur_limit=3, p=1), Resize(config.image_size, config.image_size), OneOf([RandomBrightnessContrast(), FancyPCA(), HueSaturationValue()], p=1), ToTensor() ]) return train_transforms, test_transforms
def augment(patch_size=patch_size): return Compose([ VerticalFlip(p=.5), HorizontalFlip(p=.5), HueSaturationValue(hue_shift_limit=(-15, 15), sat_shift_limit=0, val_shift_limit=0, p=.5), HueSaturationValue(hue_shift_limit=(-10, 10), sat_shift_limit=(-20, 20), val_shift_limit=0, p=.5), Rotate(limit=(0, 359), p=.5, border_mode=cv2.BORDER_CONSTANT), RandomBrightnessContrast(brightness_limit=0.15, contrast_limit=0.1, always_apply=False, p=0.5), ElasticTransform(always_apply=True, approximate=True, alpha=20, sigma=10, alpha_affine=0, border_mode=cv2.BORDER_CONSTANT), GridDistortion(num_steps=16, distort_limit=0.5, border_mode=cv2.BORDER_CONSTANT, always_apply=False, p=0.5), ])
def aug_train(resolution, p=1): return Compose([Resize(resolution, resolution), OneOf([ HorizontalFlip(), VerticalFlip(), RandomRotate90(), Transpose()], p=0.5), OneOf([ IAAAdditiveGaussianNoise(), GaussNoise(), ], p=0.5), OneOf([ MotionBlur(p=.2), MedianBlur(blur_limit=3, p=0.1), Blur(blur_limit=3, p=0.1), ], p=0.5), ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2), OneOf([ OpticalDistortion(p=0.3), GridDistortion(p=.1), IAAPiecewiseAffine(p=0.3), ], p=0.5), OneOf([ CLAHE(clip_limit=2), IAASharpen(), IAAEmboss(), RandomBrightnessContrast(), ], p=0.5), HueSaturationValue(p=0.3), Normalize() ], p=p)
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 strong_aug(p=.5): return Compose([ HorizontalFlip(), OneOf([ IAAAdditiveGaussianNoise(), GaussNoise(), ], p=0.4), OneOf([ MotionBlur(p=.2), MedianBlur(blur_limit=3, p=.1), Blur(blur_limit=3, p=.1), ], p=0.3), OneOf([ OpticalDistortion(p=0.3), GridDistortion(p=.1), IAAPiecewiseAffine(p=0.3), ], p=0.2), OneOf([ CLAHE(clip_limit=2), IAASharpen(), RandomContrast(), RandomBrightness(), ], p=0.3), HueSaturationValue(p=0.3), ChannelShuffle(), Cutout(num_holes=20, max_h_size=16, max_w_size=16) ], p=p)
def get_transforms(*, data): if data == 'train': return Compose([ RandomResizedCrop(CFG.size, CFG.size), Transpose(p=0.5), HorizontalFlip(p=0.5), VerticalFlip(p=0.5), ShiftScaleRotate(p=0.5), HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5), RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1), p=0.5), Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ToTensorV2(), ]) elif data == 'valid': return Compose([ Resize(CFG.size, CFG.size), Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ToTensorV2(), ])
def medium_aug(p=1.0): return Compose( [ HorizontalFlip(p=0.5), ShiftScaleRotate(p=0.75, shift_limit=0.1, scale_limit=0.2, rotate_limit=45, border_mode=cv2.BORDER_CONSTANT), RandomBrightnessContrast( brightness_limit=0.6, contrast_limit=0.6, p=0.5), OneOf([ HueSaturationValue(p=1.0), RGBShift(p=1.0), ChannelShuffle(p=1.0) ], p=0.5), OneOf([ Blur(p=1.0), MedianBlur(p=1.0), MotionBlur(p=1.0), ], p=0.3), OneOf([GridDistortion(p=1.0), ElasticTransform(p=1.0)], p=0.3), OneOf([ CLAHE(p=1.0), IAASharpen(p=1.0), ], p=0.3), IAAAdditiveGaussianNoise(p=0.5) # ToGray(p=1.0), ], 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 HSV(img, v): # [-1, 1] assert -1 <= v <= 1 if random.random() < 0.5: v = -v transform = Compose([HueSaturationValue(hue_shift_limit=v, sat_shift_limit=v, val_shift_limit=v)]) Aug_img = transform(image=img) return Aug_img
def make(p=0.5): return Compose( [ OneOf([IAAAdditiveGaussianNoise(), GaussNoise(), ISONoise()], p=0.9), MotionBlur(p=0.3), ShiftScaleRotate(shift_limit=0.0925, scale_limit=0.4, rotate_limit=7, border_mode=cv2.BORDER_CONSTANT, value=0, p=0.6), # IAAPerspective(scale=(.055, .060), keep_size=False, p=.2), # OpticalDistortion(p=0.2), OneOf([ CLAHE(clip_limit=2), IAASharpen(), IAAEmboss(), RandomBrightnessContrast(), ], p=0.3), HueSaturationValue(p=0.3), RGBShift(40, 40, 40) ], p=p)
def hard_transform(image_size: int = 256, p: float = 0.5, **kwargs): """Hard augmentations (on training)""" _add_transform_default_params(kwargs) transforms = Compose([ ShiftScaleRotate( shift_limit=0.1, scale_limit=0.1, rotate_limit=15, border_mode=cv2.BORDER_REFLECT, p=p, ), IAAPerspective(scale=(0.02, 0.05), p=p), OneOf([ HueSaturationValue(p=p), ToGray(p=p), RGBShift(p=p), ChannelShuffle(p=p), ]), RandomBrightnessContrast(brightness_limit=0.5, contrast_limit=0.5, p=p), RandomGamma(p=p), CLAHE(p=p), JpegCompression(quality_lower=50, p=p), PadIfNeeded(image_size, image_size, border_mode=cv2.BORDER_CONSTANT), ], **kwargs) return transforms
def create_train_transforms(conf): height = conf['crop_height'] width = conf['crop_width'] return Compose([ SafeRotate(45, p=0.4, border_mode=cv2.BORDER_CONSTANT), OneOf([ RandomSizedCrop(min_max_height=(int(height * 0.7), int( height * 1.3)), w2h_ratio=1., height=height, width=width, p=0.8), RandomCrop(height=height, width=width, p=0.2) ], p=1), HorizontalFlip(), VerticalFlip(), RandomRotate90(), Transpose(), ImageCompression(p=0.1), Lighting(alphastd=0.3), RandomBrightnessContrast(p=0.4), RandomGamma(p=0.4), OneOf([RGBShift(), HueSaturationValue()], p=0.2) ], additional_targets={'image1': 'image'})
def strong_aug(config, aug_prob): return Compose( [ # Resize(config.image_height, config.image_width, always_apply=True), RandomSizedCrop( p=config.random_sized_crop_prob, min_max_height=(int( config.image_height * config.min_max_height), config.image_height), height=config.image_height, width=config.image_width, w2h_ratio=config.image_width / config.image_height), HorizontalFlip(p=config.horizontal_flip_prob), RandomGamma(p=config.random_gamma_prob), RandomContrast(p=config.random_contrast_prob, limit=config.random_contrast_limit), RandomBrightness(p=config.random_brightness_prob, limit=config.random_brightness_limit), OneOf([ MotionBlur(p=config.motion_blur_prob), MedianBlur(blur_limit=config.median_blur_limit, p=config.median_blur_prob), Blur(blur_limit=config.blur_limit, p=config.blur_prob), ], p=config.one_of_blur_prob), CLAHE(clip_limit=config.clahe_limit, p=config.clahe_prob), IAAEmboss(p=config.iaaemboss_prob), HueSaturationValue(p=config.hue_saturation_value_prob, hue_shift_limit=config.hue_shift_limit, sat_shift_limit=config.sat_shift_limit, val_shift_limit=config.val_shift_limit) ], p=aug_prob)
def train_transform(p=1): return Compose( [ # Rescale(SIZE), RandomCrop(SIZE), RandomBrightness(0.2), OneOf([ IAAAdditiveGaussianNoise(), GaussNoise(), ], p=0.15), # OneOf([ # OpticalDistortion(p=0.3), # GridDistortion(p=.1), # IAAPiecewiseAffine(p=0.3), # ], p=0.1), # OneOf([ # IAASharpen(), # IAAEmboss(), # RandomContrast(), # RandomBrightness(), # ], p=0.15), HueSaturationValue(p=0.15), HorizontalFlip(p=0.5), Normalize(p=1), ], 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 transforms_train(aug_proba=1.): return Compose(transforms=[ HorizontalFlip(p=0.5), Rotate(limit=25, p=0.5, border_mode=cv2.BORDER_CONSTANT, value=0, interpolation=cv2.INTER_CUBIC), OneOf([ IAAAdditiveGaussianNoise(p=1), GaussNoise(p=1), ], p=0.2), OneOf([ HueSaturationValue(hue_shift_limit=10, sat_shift_limit=15, val_shift_limit=10, p=1), RGBShift(r_shift_limit=10, g_shift_limit=10, b_shift_limit=10, p=1) ]), OneOf([RandomContrast(p=1), RandomBrightness(p=1)], p=0.3), OpticalDistortion(p=0.1), Resize(*SIZE), Normalize() ], p=aug_proba, additional_targets={'trimap': 'mask'})
def Auger(self): List_Transforms = [] if self.is_train: List_Transforms.extend([ HueSaturationValue(10, 10, 10, p=0.3), HorizontalFlip(0.3), VerticalFlip(p=0.3), # May be it not work,will rescale [0,255] -> [0.0,1.0] ToFloat(always_apply=True), ShiftScaleRotate( shift_limit=0.1, # no resizing scale_limit=0.1, rotate_limit=3, # rotate p=0.5, border_mode=cv2.BORDER_REFLECT), PadIfNeeded(self.padshape, self.padshape), ]) List_Transforms.extend([ # [0.12110683835022196, 0.1308642819666743, 0.14265566800591103] #Normalize(mean=(0.397657144,0.351649219,0.305031406),std=(0.12110683835022196, 0.1308642819666743, 0.14265566800591103)), RandomCrop(self.shape, self.shape), ToTensor(), ]) TR = Compose(List_Transforms) return TR
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=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 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 get_transforms(*, data): if data == 'train': return Compose([ #Resize(CFG.size, CFG.size), RandomResizedCrop(CFG.size, CFG.size, scale=(0.85, 1.0)), HorizontalFlip(p=0.5), RandomBrightnessContrast(p=0.2, brightness_limit=(-0.2, 0.2), contrast_limit=(-0.2, 0.2)), HueSaturationValue(p=0.2, hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2), ShiftScaleRotate(p=0.2, shift_limit=0.0625, scale_limit=0.2, rotate_limit=20), CoarseDropout(p=0.2), Cutout(p=0.2, max_h_size=16, max_w_size=16, fill_value=(0., 0., 0.), num_holes=16), Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ToTensorV2(), ]) elif data == 'valid': return Compose([ Resize(CFG.size, CFG.size), Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ), ToTensorV2(), ])
def hard_transform(image_size: int = 256, p: float = 0.5): """Hard augmentations""" transforms = Compose([ ShiftScaleRotate( shift_limit=0.1, scale_limit=0.1, rotate_limit=15, border_mode=cv2.BORDER_REFLECT, p=p, ), IAAPerspective(scale=(0.02, 0.05), p=p), OneOf([ HueSaturationValue(p=p), ToGray(p=p), RGBShift(p=p), ChannelShuffle(p=p), ]), RandomBrightnessContrast( brightness_limit=0.5, contrast_limit=0.5, p=p ), RandomGamma(p=p), CLAHE(p=p), JpegCompression(quality_lower=50, p=p), ]) return transforms
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 train_transform(sz, downscale=1, p=1): augmentation = Compose( [ VerticalFlip(p=0.5), HorizontalFlip(p=0.5), RandomRotate90(p=0.5), # GridDistortion(p=0.5, border_mode=cv2.BORDER_CONSTANT), # RandomGamma(p=0.9, gamma_limit=(80, 150)), HueSaturationValue(p=0.9, hue_shift_limit=10, sat_shift_limit=20, val_shift_limit=10), ], p=p) def transform_fun(img_path): img, mask = read_img_mask(img_path, sz, downscale=downscale) data = {"image": img, "mask": mask} augmented = augmentation(**data) img, mask = augmented["image"], augmented["mask"] img = norm_fun(img) mask = mask_from_rgb(mask) return img, mask return transform_fun
def create_train_transforms(size=300): return Compose([ ImageCompression(quality_lower=60, quality_upper=100, p=0.5), GaussNoise(p=0.1), GaussianBlur(blur_limit=3, p=0.05), HorizontalFlip(), OneOf([ IsotropicResize(max_side=size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC), IsotropicResize(max_side=size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_LINEAR), IsotropicResize(max_side=size, interpolation_down=cv2.INTER_LINEAR, interpolation_up=cv2.INTER_LINEAR), ], p=1), PadIfNeeded(min_height=size, min_width=size, border_mode=cv2.BORDER_CONSTANT), OneOf([RandomBrightnessContrast(), FancyPCA(), HueSaturationValue()], p=0.7), ToGray(p=0.2), ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=10, border_mode=cv2.BORDER_CONSTANT, p=0.5), ])
def strong_aug(p=.5): return Compose([ JpegCompression(p=0.9), HorizontalFlip(p=0.5), OneOf([ IAAAdditiveGaussianNoise(), GaussNoise(), ], p=0.5), OneOf([ MotionBlur(p=.2), MedianBlur(blur_limit=3, p=.1), Blur(blur_limit=3, p=.1), ], p=0.5), ShiftScaleRotate( shift_limit=0.0625, scale_limit=0.2, rotate_limit=15, p=.5), OneOf([ CLAHE(clip_limit=2), IAASharpen(), IAAEmboss(), RandomContrast(), RandomBrightness(), ], p=0.5), HueSaturationValue(p=0.5), ], p=p)
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 get_train_transforms(): return Compose( [ #Resize(args.img_size, args.img_size), RandomResizedCrop(args.img_size, args.img_size), Transpose(p=0.5), HorizontalFlip(p=0.5), VerticalFlip(p=0.25), ShiftScaleRotate(p=0.25), HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.25), RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1), p=0.5), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0), CoarseDropout(p=0.5), # Cutout(p=0.5), ToTensorV2(p=1.0), ], p=1.)
def strong_aug(p=.5): return Compose([ HorizontalFlip(p=0.5), ToGray(p=0.1), OneOf([ IAAAdditiveGaussianNoise(), GaussNoise(), ], p=0.4), OneOf([ MotionBlur(p=.2), MedianBlur(blur_limit=3, p=.1), Blur(blur_limit=3, p=.1), ], p=0.2), OneOf([ OpticalDistortion(p=0.3), GridDistortion(p=.1), IAAPiecewiseAffine(p=0.3), ], p=0.2), OneOf([ CLAHE(clip_limit=2), IAASharpen(), RandomContrast(), RandomBrightness(), ], p=0.3), HueSaturationValue(p=0.3), ], p=p)
def get_corrupter(self): distortion_augs = OneOf([OpticalDistortion(p=1), GridDistortion(p=1)], p=1) effects_augs = OneOf([ IAASharpen(p=1), IAAEmboss(p=1), IAAPiecewiseAffine(p=1), IAAPerspective(p=1), CLAHE(p=1) ], p=1) misc_augs = OneOf([ ShiftScaleRotate(p=1), HueSaturationValue(p=1), RandomBrightnessContrast(p=1) ], p=1) blur_augs = OneOf( [Blur(p=1), MotionBlur(p=1), MedianBlur(p=1), GaussNoise(p=1)], p=1) aug = Compose([distortion_augs, effects_augs, misc_augs, blur_augs]) return aug