imgaug

imgaug is a Python library for image augmentation in machine learning that provides a highly flexible, stochastic API for building complex augmentation pipelines — enabling fine-grained control over augmentation parameters through stochastic expressions (rotate between -10° and +10° with truncated normal distribution), deterministic mode for applying identical transforms to images and their annotations (masks, bounding boxes, keypoints), and a rich set of 60+ augmentations with compositional operators (Sequential, SomeOf, OneOf) for building sophisticated augmentation strategies.

What Is imgaug?

Definition: An open-source Python library (pip install imgaug) for augmenting images in machine learning experiments — providing a composable, stochastic pipeline for geometric, color, noise, weather, and artistic augmentations with support for bounding boxes, segmentation maps, heatmaps, and keypoints.
Key Strength: Stochastic parameters — instead of "rotate by exactly 10°", you specify "rotate by a value drawn from Normal(0, 5°) clipped to [-15°, 15°]", giving fine-grained control over the augmentation distribution.
Status Note: imgaug's development has slowed since ~2021. Albumentations is now the more actively maintained and faster alternative. However, imgaug's stochastic parameter API remains more flexible for complex augmentation distributions.

Core Usage

import imgaug.augmenters as iaa

seq = iaa.Sequential([
    iaa.Fliplr(0.5),                    # 50% chance horizontal flip
    iaa.GaussianBlur(sigma=(0, 1.0)),   # Blur with sigma 0-1
    iaa.Affine(
        rotate=(-15, 15),               # Rotate -15 to +15 degrees
        scale=(0.8, 1.2)                # Scale 80% to 120%
    ),
    iaa.AdditiveGaussianNoise(scale=(0, 0.05*255))
])

images_aug = seq(images=images)

Composition Operators

Operator	Behavior	Use Case
Sequential	Apply all transforms in order	Standard pipeline
SomeOf((2, 4), [...])	Randomly select 2-4 from the list	Variable augmentation strength
OneOf([...])	Apply exactly one from the list	Mutually exclusive transforms
Sometimes(0.5, ...)	Apply with 50% probability	Optional augmentations

Stochastic Parameters (imgaug's Unique Feature)

# Normal distribution for rotation
iaa.Affine(rotate=iap.Normal(0, 5))

# Truncated normal (clipped to range)
iaa.Affine(rotate=iap.TruncatedNormal(0, 5, low=-15, high=15))

# Different distributions for different parameters
iaa.Affine(
    rotate=iap.Normal(0, 10),      # Rotation: normal distribution
    scale=iap.Uniform(0.8, 1.2),   # Scale: uniform distribution
    shear=iap.Laplace(0, 3)        # Shear: Laplace distribution
)

imgaug vs Albumentations

Feature	imgaug	Albumentations
Speed	Moderate	2-5× faster (OpenCV optimized)
Stochastic params	Full distribution control	Basic probability only
Development	Slowed (~2021)	Active development
Transform count	60+	70+
Deterministic mode	Built-in	Built-in
Box/mask support	Good	Excellent (native)
PyTorch integration	Manual	ToTensorV2 included
Community	Moderate	Large (Kaggle standard)

When to Use imgaug

Use imgaug	Use Albumentations
Need fine-grained stochastic parameter control	Need maximum speed
Existing pipeline already uses imgaug	Starting a new project
Complex augmentation distributions (truncated normal, Laplace)	Standard augmentation needs
Research requiring precise control over augmentation statistics	Production deployment or competition

imgaug is the flexible, research-oriented image augmentation library — providing unmatched control over augmentation parameter distributions through stochastic expressions, with a rich compositional API for building complex pipelines, while Albumentations has become the faster and more actively maintained alternative for production and competition use cases.

imgaug

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