AugmentingData.py

# -*- coding: utf-8 -*-
"""
Created on Sun Mar  6 15:01:59 2022

@author: hk01
"""
#

import numpy as np
import matplotlib.pyplot as plt
import os
import random
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
#%matplotlib inline

#plotting images
def plotImages(images_arr):
    fig, axes = plt.subplots(1, 10, figsize=(20,20))
    axes = axes.flatten()
    for img, ax in zip( images_arr, axes):
        ax.imshow(img)
        ax.axis('off')
    plt.tight_layout()
    plt.show()


gen = ImageDataGenerator(rotation_range=10, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.15, zoom_range=0.1, 
    channel_shift_range=10., horizontal_flip=True)

chosen_image = random.choice(os.listdir('data/dogs-vs-cats/train/dog'))
image_path = 'data/dogs-vs-cats/train/dog/' + chosen_image

image = np.expand_dims(plt.imread(image_path),0)

plt.imshow(image[0])

aug_iter = gen.flow(image)

# generate 10 augmented images 
aug_images = [next(aug_iter)[0].astype(np.uint8) for i in range(10)]

plotImages(aug_images)

#saving augmented images
#aug_iter = gen.flow(image, save_to_dir='data/dogs-vs-cats/train/dog', save_prefix='aug-image-', save_format='jpeg')