Horizontal Flip

If you have ever worked on a Computer Vision project, you might know that using augmentations to diversify the dataset is the best practice. On this page, we will:

  • Сover the Horizontal Flip augmentation;
  • Check out its parameters;
  • See how Horizontal Flip affects an image;
  • And check out how to work with Horizontal Flip using Python through the Albumentations library.

Let’s jump in.

As you might know, every image can be viewed as a matrix of pixels, with each pixel containing some specific information, for example, color or brightness.

Pixel matrix representation of an image
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To define the term, Horizontal Flip is a data augmentation technique that takes both rows and columns of such a matrix and flips them horizontally. As a result, you will get an image flipped horizontally along the y-axis.

  • Probability of applying transform - defines the likelihood of applying Horizontal Flip to an image.
If a large fraction of training images needs to be flipped, set a high probability.

In the real world, people regularly confuse Horizontal and Vertical Flip as they feel alike. Still, there is a clear-cut difference:

  • Horizontal Flip flips an image along the y-axis;
  • Vertical Flip flips an image along the x-axis.
Horizontal Flip Vs. Vertical Flip
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That is it. Keep this info in mind, and you will never find yourself stuck on a thought of which augmentation to choose.

For a deeper dive please check out our Vertical Flip page.
Original image (before Horizontal Flip)
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Image after Horizontal Flip was applied
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python 
      import albumentations as albu
from PIL import Image
import numpy as np

transform =albu.HorizontalFlip(p=0.5)
image = np.array(Image.open('/some/random/image.png'))
augmented_image = transform(image=image)['image']

# we have our required flipped image in augmented_image.

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