Center Crop

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.

Center Crop explained

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.

Parameters

• 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.

Center Crop visualized

Code Implementation

        import albumentations as albu
from PIL import Image
import numpy as np

transform =albu.CenterCrop(200,200,p=0.5)#set height, width, and probability

image = np.array(Image.open('/some/image/file/path'))
image = transform(image=image)['image']

# Now the image is cropped and ready to be accepted by the model
    

Learn more about other augmentations …

• Horizontal Flip;
• Vertical Flip;
• Random Crop;
• Random Sized Crop;
• Rotate;
• Resize;
• Blur;
• Smallest max size;
• Color Jitter;
• Gaussian Noise;
• Shift Scale Rotate;
• Longest Max Size;
• Equalize;
• To gray;
• Shear;
• Mosaic;
• Copy Paste.