DataAugmentation.py

# !/usr/bin/python

""" Dataset Structure:
Data:
.....1_Images
..........1_001.jpeg
..........1_002.jpeg
..........1_003.jpeg

.
.
.

.....6_Images
..........6_001.jpeg
..........6_002.jpeg
..........6_003.jpeg


"""

import numpy 
import tensorflow as tf
from glob import glob
from keras.preprocessing.image import ImageDataGenerator,array_to_img,img_to_array,load_img

COUNTER = 15

DataGen = ImageDataGenerator(
    rotation_range = 180,
    width_shift_range = 0.4,
    height_shift_range = 0.4,
    shear_range = 0.3,
    zoom_range = 0.3,
    horizontal_flip = True,
    vertical_flip = True,)


Debug : print("[INFO]: .......Augmenting the Dataset....")

for k in range(1,7):
    Debug : print(f"[INFO]: .......Glob Files for K = {k}....")
    imagelist = glob('Data/{}_Images/{}*'.format(k,k)) #Arrange all images in a class to a particular folder

    for eachImage in imagelist:
        Debug : print(f"[INFO]: .......Preprocessing Images in the Data folder for K = {k} ....... (Grayscale, Expanded Dims)")
        img = load_img(eachImage,color_mode = 'grayscale')
        x = img_to_array(img)
        x = numpy.expand_dims(x,0)


        i = 0
        Debug : print(f"[INFO]: .......Saving the Images in the Train Folder -  for K = {k} .......")
        for batch in DataGen.flow(x,batch_size = 1, save_to_dir = 'Train/{}_Images'.format(k),save_prefix = str(k),save_format = 'jpeg'):
            i+=1
            if i>COUNTER:
                break
        Debug : print(f"[INFO]: .......From a particular image, {COUNTER} images are augmented in the Train folder for K = {k} ....... ")

Debug : print(f"[INFO]: ....... Dataset Augmentation is completed. Now run  'Dataset.py' .........")