def augmentation(mode, target_size, prob=0.5, aug_m=2): ''' description: augmentation mode: 'train' 'test' target_size: int or list, the shape of image , aug_m: Strength of transform ''' high_p = prob low_p = high_p / 2.0 M = aug_m first_size = [int(x / 0.7) for x in target_size] if mode == 'train': return composition.Compose([ transforms.Resize(first_size[0], first_size[1], interpolation=3), transforms.Flip(p=0.5), composition.OneOf([ RandomCenterCut(scale=0.1 * M), transforms.ShiftScaleRotate(shift_limit=0.05 * M, scale_limit=0.1 * M, rotate_limit=180, border_mode=cv2.BORDER_CONSTANT, value=0), albumentations.imgaug.transforms.IAAAffine( shear=(-10 * M, 10 * M), mode='constant') ], p=high_p), transforms.RandomBrightnessContrast( brightness_limit=0.1 * M, contrast_limit=0.03 * M, p=high_p), transforms.HueSaturationValue(hue_shift_limit=5 * M, sat_shift_limit=15 * M, val_shift_limit=10 * M, p=high_p), transforms.OpticalDistortion(distort_limit=0.03 * M, shift_limit=0, border_mode=cv2.BORDER_CONSTANT, value=0, p=low_p), composition.OneOf([ transforms.Blur(blur_limit=7), albumentations.imgaug.transforms.IAASharpen(), transforms.GaussNoise(var_limit=(2.0, 10.0), mean=0), transforms.ISONoise() ], p=low_p), transforms.Resize(target_size[0], target_size[1], interpolation=3), transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), max_pixel_value=255.0) ], p=1) else: return composition.Compose([ transforms.Resize(target_size[0], target_size[1], interpolation=3), transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), max_pixel_value=255.0) ], p=1)
def get_presize_combine_transforms_V4(): transforms_presize = A.Compose([ transforms.PadIfNeeded(600, 800), geometric.Perspective( scale=[0, .1], pad_mode=cv2.BORDER_REFLECT, interpolation=cv2.INTER_AREA, p = .3), transforms.Flip(), geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.95, scale_limit=0.0), crops.RandomResizedCrop( TARGET_SIZE, TARGET_SIZE, scale=(config['rrc_scale_min'], config['rrc_scale_max']), ratio=(.70, 1.4), interpolation=cv2.INTER_CUBIC, p=1.0), transforms.Transpose() #rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99), ]) transforms_postsize = A.Compose([ #imgaug.IAAPiecewiseAffine(), transforms.CoarseDropout(), transforms.CLAHE(p=.1), transforms.RandomToneCurve(scale=.1, p=0.2), transforms.RandomBrightnessContrast( brightness_limit=.1, contrast_limit=0.4, p=.8), transforms.HueSaturationValue( hue_shift_limit=20, sat_shift_limit=50, val_shift_limit=0, p=0.5), transforms.Equalize(p=0.05), transforms.FancyPCA(p=0.05), transforms.RandomGridShuffle(p=0.1), A.OneOf([ transforms.MotionBlur(blur_limit=(3, 9)), transforms.GaussianBlur(), transforms.MedianBlur() ], p=0.1), transforms.ISONoise(p=.2), transforms.GaussNoise(var_limit=127., p=.3), A.OneOf([ transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5), transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3), ], p=.3), geometric.ElasticTransform(alpha=4, sigma=4, alpha_affine=4, interpolation=cv2.INTER_AREA, p=0.3), transforms.CoarseDropout(), transforms.Normalize(), ToTensorV2() ]) return transforms_presize, transforms_postsize
def get_augmentations(): """Get a list of 'major' and 'minor' augmentation functions for the pipeline in a dictionary.""" return { "major": { "shift-scale-rot": trans.ShiftScaleRotate( shift_limit=0.05, rotate_limit=35, border_mode=cv2.BORDER_REPLICATE, always_apply=True, ), "crop": trans.RandomResizedCrop(100, 100, scale=(0.8, 0.95), ratio=(0.8, 1.2), always_apply=True), # "elastic": trans.ElasticTransform( # alpha=0.8, # alpha_affine=10, # sigma=40, # border_mode=cv2.BORDER_REPLICATE, # always_apply=True, # ), "distort": trans.OpticalDistortion(0.2, always_apply=True), }, "minor": { "blur": trans.GaussianBlur(7, always_apply=True), "noise": trans.GaussNoise((20.0, 40.0), always_apply=True), "bright-contrast": trans.RandomBrightnessContrast(0.4, 0.4, always_apply=True), "hsv": trans.HueSaturationValue(30, 40, 50, always_apply=True), "rgb": trans.RGBShift(always_apply=True), "flip": trans.HorizontalFlip(always_apply=True), }, }
def get_train_transforms(): return A.Compose([ transforms.PadIfNeeded(600, 800), geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99, scale_limit=0.8), geometric.Perspective(pad_mode=cv2.BORDER_REFLECT,interpolation=cv2.INTER_AREA), crops.RandomResizedCrop( TARGET_SIZE, TARGET_SIZE, scale=(config['rrc_scale_min'], config['rrc_scale_max']), interpolation=cv2.INTER_CUBIC, p=1.0), transforms.Transpose(), transforms.Flip(), transforms.CoarseDropout(), transforms.CLAHE(p=.1), transforms.RandomToneCurve(scale=.1), transforms.RandomBrightnessContrast( brightness_limit=.1, contrast_limit=0.3, p=.7), transforms.HueSaturationValue( hue_shift_limit=20, sat_shift_limit=60, val_shift_limit=0, p=0.6), transforms.RandomGridShuffle(p=0.1), A.OneOf([ transforms.MotionBlur(blur_limit=(3, 9)), transforms.GaussianBlur(), transforms.MedianBlur() ], p=0.2), transforms.ISONoise(p=.3), transforms.GaussNoise(var_limit=255., p=.3), A.OneOf([ transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5), transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3), ], p=.3), geometric.ElasticTransform(alpha=4, sigma=100, alpha_affine=100, interpolation=cv2.INTER_AREA, p=0.3), transforms.CoarseDropout(), transforms.Normalize(), ToTensorV2() ])