flips_rotations_augmentation.py

import numpy as np

def flips_rotations_augmentation(X, y):
    """A pipeline for applying augmentation functions listed in `augmentation_table`
    to a numpy array with images X.
    """
    # Initializing empty arrays to accumulate intermediate results of augmentation
    X_out, y_out = np.empty([0] + list(X.shape[1:]), dtype=np.float32), np.empty([0])
    
    # Cycling through all label classes and applying all available transformations
    for in_label in augmentation_table.index.values:
        available_augmentations = dict(augmentation_table.ix[in_label].dropna(axis=0))
        images = X[y==in_label]

        # Augment images and their labels
        for kind, out_label in available_augmentations.items():
            X_out = np.vstack([X_out, apply_augmentation(images, how=kind)])
            y_out = np.hstack([y_out, [out_label] * len(images)])

    # And stack with initial dataset
    X_out = np.vstack([X_out, X])
    y_out = np.hstack([y_out, y])
    
    # Random rotation is explicitly included in this function's body
    X_out_rotated = rotate_up_to_20_deg(X)
    y_out_rotated = deepcopy(y)

    X_out = np.vstack([X_out, X_out_rotated])
    y_out = np.hstack([y_out, y_out_rotated])

    return X_out, y_out