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!
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.
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.
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.