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 Smallest max size augmentation;
Check out its parameters;
See how Smallest max size affects an image;
And check out how to work with Smallest max size using Python through the Albumentations library.
Let's jump in.
To define the term, Smallest max size is a data augmentation technique that reshapes an image so that the length of an image’s smallest size is equal to a certain number of pixels.
Maximum size of smallest 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.SmallestMaxSize(max_size=1024, 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