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 Longest max size augmentation;
Check out its parameters;
See how Longest max size affects an image;
And check out how to work with Longest max size using Python through the Albumentations library.
Let's jump in.
To define the term, Longest max size is a data augmentation technique that fixes the maximum possible length of the image's longest side.
Maximum size of longest side - sets the desired maximal length of the image in pixels. The values can vary from 256 to 3000.
import albumentations as albu from PIL import Image import numpy as np transform = albu.LongestMaxSize(max_size=500, interpolation=1, p=1) #default interpolation is INTER_LINEAR image = np.array(Image.open('/some/image/file/path')) image = transform(image=image)['image'] # Now the image is preprocessed and ready to be accepted by the model