What it does This repo is the companion code for a Paperspace blog series on data augmentation for object detection. It implements transforms—flip, scale, translate, rotate, shear, resize—that update bounding box coordinates alongside the image pixels, so your labels don’t quietly lie to your model.

The interesting bit The hard part isn’t warping an image; it’s recalculating where the object actually ended up. The project treats bounding boxes as first-class citizens, not afterthoughts, which is rarer than it should be in augmentation libraries.

Key highlights

• Six core transforms: horizontal flip, scaling, translation, rotation, shearing, resizing
• Pure OpenCV + NumPy + Matplotlib; no heavy framework dependency
• Ships with a quick-start.ipynb notebook for immediate experimentation
• Documentation hosted at augmentationlib.paperspace.com
• Directly tied to a detailed tutorial series, so the “why” is explained, not just the “how”

Caveats

• OpenCV 3.x is specified; compatibility with 4.x is unclear from the README
• The repo appears to be tutorial-grade code, not a maintained package—no install instructions or versioning mentioned

Verdict Worth a look if you’re building an object detection pipeline from scratch and want to understand augmentation mechanics. Skip it if you need a battle-hardened, pip-installable library like Albumentations.