how to save resized images using ImageDataGenerator and flow_from_directory in keras

Question

I am resizing my RGB images stored in a folder(two classes) using following code:

from keras.preprocessing.image import ImageDataGenerator
dataset=ImageDataGener         


        

Answer 1

In case you want to save the images under a folder having same name as label then you can loop over a list of labels and call the augmentation code within the loop.

from tensorflow.keras.preprocessing.image import ImageDataGenerator  

# Augmentation + save augmented images under augmented folder

IMAGE_SIZE = 224
BATCH_SIZE = 500
LABELS = ['lbl_a','lbl_b','lbl_c']

for label in LABELS:
  datagen_kwargs = dict(rescale=1./255)  
  dataflow_kwargs = dict(target_size=(IMAGE_SIZE, IMAGE_SIZE), 
                        batch_size=BATCH_SIZE, interpolation="bilinear")

  train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    rotation_range=40,
    horizontal_flip=True,
    width_shift_range=0.1, height_shift_range=0.1,
    shear_range=0.1, zoom_range=0.1,
    **datagen_kwargs)

  train_generator = train_datagen.flow_from_directory(
      'original_images', subset="training", shuffle=True, save_to_dir='aug_images/'+label, save_prefix='aug', classes=[label], **dataflow_kwargs)
  
  # Following line triggers execution of train_generator
  batch = next(train_generator) 

So why do this when generator can directly be passed to model? In case, you want to use the tflite-model-maker which does not accept a generator and accepts labelled data under folder for each label:

from tflite_model_maker import ImageClassifierDataLoader
data = ImageClassifierDataLoader.from_folder('aug_images')

Result

aug_images
| 
|__ lbl_a
|   |
|   |_____aug_img_a.png
|
|__ lbl_b
|   |
|   |_____aug_img_b.png
| 
|__ lbl_c
|   |
|   |_____aug_img_c.png

Note: You need to ensure the folders already exist.