Random Crop

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

Let's jump in.

Random Crop explained

To define the term, Random Crop is a data augmentation technique that helps researchers to crop the images into a particular dimension, creating synthetic data. The cropping could result in any patch of the image and is therefore called "Random Crop."

Parameters

• Height - sets the height of the desired cropped image in pixels;
• Width - sets the width of the desired cropped image in pixels.

Random Crop visualized

Code Implementation

    import albumentations as albu
from PIL import Image

transform = albu.RandomCrop(200,200)
augmented_image = transform(image=figure)['image']

# We have our required cropped image in augmented_image.
    Copy
  

Learn more about other augmentations …

• Horizontal Flip;
• Vertical Flip;
• Random Sized Crop;
• Rotate;
• Resize;
• Blur;
• Smallest max size;
• Center Crop;
• Color Jitter;
• Shift Scale Rotate;
• Longest Max Size;
• Equalize;
• To gray;
• Shear;
• Mosaic;
• Copy Paste.