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

Heres a very simple version of saving augmented images of one image wherever you want:

Step 1. Initialize image data generator

Here we figure out what changes we want to make to the original image and generate the augmented images
You can read up about the diff effects here- https://keras.io/preprocessing/image/

datagen = 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)

Step 2: Here we pick the original image to perform the augmentation on

read in the image

image_path = 'C:/Users/Darshil/gitly/Deep-Learning/My 
Projects/CNN_Keras/test_augment/caty.jpg'

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

step 3: pick where you want to save the augmented images

save_here = 'C:/Users/Darshil/gitly/Deep-Learning/My 
Projects/CNN_Keras/test_augment'

Step 4. we fit the original image

datagen.fit(image)

step 5: iterate over images and save using the "save_to_dir" parameter

for x, val in zip(datagen.flow(image,                    #image we chose
        save_to_dir=save_here,     #this is where we figure out where to save
         save_prefix='aug',        # it will save the images as 'aug_0912' some number for every new augmented image
        save_format='png'),range(10)) :     # here we define a range because we want 10 augmented images otherwise it will keep looping forever I think
pass