AlbumentationsX vs PIL/Pillow

What Is Different?

Pillow is an image toolkit. AlbumentationsX is an augmentation pipeline library. That sounds subtle, but it changes the shape of the code: Pillow gives you image operations; AlbumentationsX gives you randomized, reproducible transforms that keep images, masks, bounding boxes, and keypoints synchronized.

• Pillow works with PIL Image objects; AlbumentationsX works with NumPy arrays and returns a dictionary of augmented targets.
• Pillow is great for loading, saving, drawing, and simple image edits; AlbumentationsX is built for training-time augmentation pipelines.
• AlbumentationsX has first-class random composition, probabilities, target synchronization, and bbox/keypoint parameter handling.
• Pillow code usually becomes manual orchestration when masks or labels must follow the image; AlbumentationsX keeps that in the pipeline contract.

Benchmark vs PIL/Pillow

The benchmark below compares single-threaded RGB image augmentation throughput. Pillow receives PIL images; AlbumentationsX receives OpenCV-loaded RGB NumPy arrays. Higher images/second is better.

See the aggregate image benchmark or inspect the benchmark source code.

Conversion Guide

The main conversion is to move from calling Pillow methods one at a time to defining an AlbumentationsX Compose pipeline.

• Convert PIL images to NumPy arrays before augmentation.
• Replace manual randomness with transform-level p values.
• Keep image-adjacent targets in the same Compose call instead of transforming them separately.
• Convert back to PIL only if downstream code specifically needs PIL objects.

from PIL import Image, ImageEnhance
import random

image = Image.open("image.jpg").convert("RGB")

if random.random() < 0.5:
    image = image.transpose(Image.Transpose.FLIP_LEFT_RIGHT)

if random.random() < 0.5:
    image = ImageEnhance.Brightness(image).enhance(1.2)

import albumentations as A
import cv2

transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.0, p=0.5),
])

image = cv2.cvtColor(cv2.imread("image.jpg"), cv2.COLOR_BGR2RGB)
image = transform(image=image)["image"]