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I'm trying to classify rooftop sky images orientations, whether it is horizontal or vertical. Knowing that the most obvious feature here is known: orientation. I can simply augment each class by rotating it 90° so it belongs to the other class. It is like dynamic augmentation to different classes.

Using keras image classification capabilities, obviously defeats the purpose:

datagen = ImageDataGenerator(
        zoom_range=0.2, # randomly zoom into images
#       rotation_range=90,  # mistaken the model
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

I am using keras for classification but open to other libraries, and techniques other than deep learning.

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  • $\begingroup$ So, basically, you can use any other augmentation provided in keras ImageDataGenerator instead of rotation? You could use rotation_range with a small value, like up to 10 degrees ... $\endgroup$
    – Antonio Jurić
    Commented Dec 17, 2018 at 13:05
  • $\begingroup$ Other data augmentation techniques provided in ImageDataGenerator should not result in a class change. $\endgroup$
    – Antonio Jurić
    Commented Dec 17, 2018 at 13:08
  • $\begingroup$ that's what I'm emphasizing. I want to augment data in an aware way to other classes. In my case, augmenting each class (horizontal and vertical) by rotation of 90° to the other opposite labeled class. how could I achieve this ? $\endgroup$
    – bacloud14
    Commented Dec 17, 2018 at 21:09

1 Answer 1

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You could rotate images manually (without using ImageDataGenerator) and save it to disk. That way you would know which images you have rotated - so you would know which images have changed the class.

After it, when using ImageDataGenerator, you need to set rotation_range to small value in order to be sure that it won't change the classes of images.

Examples how to rotate image manually can be found here: https://stackoverflow.com/a/43864937/5997950

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