Random Sized 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 Sized Crop augmentation;

• Check out its parameters;

• See how Random Sized Crop affects an image;

• And check out how to work with Random Sized Crop using Python through the Albumentations library.

Let's get into it!

Random Sized Crop explained

To define the term, Random Sized Crop is a data augmentation technique that helps researchers to crop an image to any size within a certain specified bound.

Parameters

• Min max height - sets the crop size limits;

• Height - sets the height of the desired resized and cropped image in pixels;

• Width - sets the width of the desired resized and cropped image in pixels.

Advanced options

• Width to height ratio - defines an aspect ratio of the crop;
• Interpolation - used to specify the interpolation algorithm:
  • Nearest;
  • Linear;
  • Cubic;
  • Area;
  • LANCZOS4.

Random Sized Crop visualized

Code Implementation

      import albumentations as albu
from PIL import Image

transform = albu.RandomSizedCrop([1500,2000], 100, 100, 1.0, cv2.INTER_NEAREST, 1)
augmented_image = transform(image=figure)['image']

# We have our required cropped image in augmented_image.

# The first argument defines the crop size limits;
# Second and third are the height and width of the image after crop and resize;
# Fourth is the aspect ratio of crop;
# Fifth is the interpolation technique, and the last is the probability of the transform.
    

Learn more about other augmentations…

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