augmentation_functions.py

import numpy as np
from skimage import transform
from skimage.transform import warp, AffineTransform

def rotate_90_deg(X):    
    X_aug = np.zeros_like(X)
    for i,img in enumerate(X):
        X_aug[i] = transform.rotate(img, 270.0)
    return X_aug

def rotate_180_deg(X):    
    X_aug = np.zeros_like(X)
    for i,img in enumerate(X):
        X_aug[i] = transform.rotate(img, 180.0)
    return X_aug

def rotate_270_deg(X):    
    X_aug = np.zeros_like(X)
    for i,img in enumerate(X):
        X_aug[i] = transform.rotate(img, 90.0)
    return X_aug

def rotate_up_to_20_deg(X):
    X_aug = np.zeros_like(X)
    delta = 20.
    for i,img in enumerate(X):
        X_aug[i] = transform.rotate(img, random.uniform(-delta, delta), mode='edge')
    return X_aug

def flip_vert(X):
    X_aug = deepcopy(X)
    return X_aug[:, :, ::-1, :]

def flip_horiz(X):
    X_aug = deepcopy(X)
    return X_aug[:, ::-1, :, :]

def affine_transform(X, shear_angle=0.0, scale_margins=[0.8, 1.5], p=1.0):
    """This function allows applying shear, scale and  transformations
    with the specified magnitude and probability p.
    
    Args:
        X: numpy array of images.
        shear_angle: maximum shear angle in counter-clockwise direction as radians.
        scale_margins: minimum and maximum margins to be used in scaling.
        p: a fraction of images to be augmented.
    """
    X_aug = deepcopy(X)
    shear = shear_angle * np.random.rand()
    for i in np.random.choice(len(X_aug), int(len(X_aug) * p), replace=False):
        _scale = random.uniform(scale_margins[0], scale_margins[1])
        X_aug[i] = warp(X_aug[i], AffineTransform(scale=(_scale, _scale), shear=shear), mode='edge')
    return X_aug