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 Center Crop augmentation;
- Check out its parameters;
- See how Center Crop affects an image;
- And check out how to work with Center Crop using Python through the Albumentations library.
Let’s jump in.
To define the term, Center Crop is a data augmentation technique that helps researchers to crop images to a specified height and width with a certain probability.
The key difference between Random Crop
and Center Crop is that the latter crops the central part of an image.
- Height - defines the height of the newly cropped image in pixels;
- Width - defines the width of the newly cropped image in pixels;
- Probability - the probability that the transformation will be applied to an image.
import albumentations as albu
from PIL import Image
import numpy as np
image = np.array(Image.open('/some/image/file/path'))
image = transform(image=image)['image']