how to save resized images using ImageDataGenerato

Question

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

from keras.preprocessing.image import ImageDataGenerator
dataset=ImageDataGenerator()
dataset.flow_from_directory('/home/1',target_size=(50,50),save_to_dir='/home/resized',class_mode='binary',save_prefix='N',save_format='jpeg',batch_size=10)

My data tree is like following:

1/
 1_1/
     img1.jpg
     img2.jpg
     ........
 1_2/
     IMG1.jpg
     IMG2.jpg
     ........
resized/
        1_1/ (here i want to save resized images of 1_1)
        2_2/ (here i want to save resized images of 1_2)

After running the code i am getting following output but not images:

Found 271 images belonging to 2 classes.
Out[12]: <keras.preprocessing.image.DirectoryIterator at 0x7f22a3569400>

How to save images?

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

Answer 2

The flow_from_directory method gives you an "iterator", as described in your output. An iterator doesn't really do anything on its own. It's waiting to be iterated over, and only then the actual data will be read and generated.

An iterator in Keras for fitting is to be used like this:

generator = dataset.flow_from_directory('/home/1',target_size=(50,50),save_to_dir='/home/resized',class_mode='binary',save_prefix='N',save_format='jpeg',batch_size=10)

for inputs,outputs in generator:

    #do things with each batch of inputs and ouptus

Normally, instead of doing the loop above, you just pass the generator to a fit_generator method. There is no real need to do a for loop:

model.fit_generator(generator, ......)

Keras will only save images after they're loaded and augmented by iterating over the generator.

Answer 3

Its only a declaration, you must use that generator, for example, .next()

from keras.preprocessing.image import ImageDataGenerator
dataset=ImageDataGenerator()
image = dataset.flow_from_directory('/home/1',target_size=(50,50),save_to_dir='/home/resized',class_mode='binary',save_prefix='N',save_format='jpeg',batch_size=10)
image.next()

then you will see images in /home/resized