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.
Hello, thank you for using the code provided by Hasty. Please note that some code blocks might not be 100% complete and ready to be run as is. This is done intentionally as we focus on implementing only the most challenging parts that might be tough to pick up from scratch. View our code block as a LEGO block - you can’t use it as a standalone solution, but you can take it and add to your system to complement it. If you have questions about using the tool, please get in touch with us to get direct help from the Hasty team.
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.
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