Data Augmentation.py

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.4,
        horizontal_flip=True,
        fill_mode='nearest')

import os 
print(os.getcwd()) ##Previous Directory
os.chdir(r"C:\Users\abhay\desktop\dl\banana_ripeness_detection\data\train\overripe") ##Change with your current working directory
print(os.getcwd())  ##Current Working Directory

for path in os.listdir():
    img = load_img(f"{path}")
    x = img_to_array(img)    # this is a Numpy array with shape (3, 150, 150)
    x = x.reshape((1,) + x.shape)
    i = 0
    for batch in datagen.flow(x, batch_size=1,
                          save_to_dir=".", save_prefix='img', save_format='jpeg'):
        i += 1
        if i > 10:     ## creates 10 image form 1 image 
            break