Keras Data Augmentation

Step 1: Organize imports

from keras.preprocessing.image import ImageDataGenerator
from keras.datasets import cifar10
import numpy as np

Using TensorFlow backend.

Step 2: Define constant/s

IMG_NUM_TO_AUGMENT=1  # Number of images to augment per original image

Step 3: Load dataset

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

Step 4: Define the ImageDataGenerator object

datagen = ImageDataGenerator(
rotation_range=40,       # 0 to 180
width_shift_range=0.2,   # horizontal-translation
height_shift_range=0.2,  # vertical-translation
zoom_range=0.2,          # random zoom
horizontal_flip=True,
fill_mode='nearest')     # filling in pixels strategy 

Step 5: Generate the images

Note: Create a ‘preview’ folder within the directory

xtas, ytas = [], []

for i in range(X_train.shape[0]):
    num_aug = 0
    x = X_train[i]    # (3, 32, 32) CIFAR image
    x = x.reshape((1,) + x.shape) # (1, 3, 32, 32)
    
    for x_aug in datagen.flow(x, batch_size=1,
                              save_to_dir='preview', save_prefix='cifar', save_format='jpeg'):
        if num_aug >= IMG_NUM_TO_AUGMENT:
            break
        xtas.append(x_aug[0])
        num_aug += 1

Sample Result

_config.yml

-mkc

Written on September 30, 2017