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name: augmentation-pipelines description: "Use when building, combining, or debugging imgaug image augmentation pipelines, augmenter families, or deterministic image-only transforms." disable-model-invocation: true metadata: disco-role: operating license: MIT

Augmentation Pipelines

Use this sub-skill when the task is about image augmentation pipelines in imgaug: choosing augmenter families, composing them with Sequential/SomeOf/OneOf/Sometimes, applying transforms deterministically, or debugging image-only augmentation behavior.

What this sub-skill covers

• Pipeline composition with iaa.Sequential, iaa.SomeOf, iaa.OneOf, iaa.Sometimes, and iaa.WithChannels.
• Image-only use of common augmenter families: affine/geometric, crop/pad/resize, blur, arithmetic/noise, color, contrast, blend/overlay, segmentation/superpixels, weather, PIL-like effects, and imgcorruptlike when installed.
• Deterministic pipeline reuse with to_deterministic().
• Basic image input/output conventions: NHWC arrays, RGB order, uint8 expectations, and shape preservation when keep_size=True.
• Safe visualization and contact-sheet generation via bundled helpers.

What it does not cover

• Annotation object construction and batch normalization details belong to ../augmentables-and-batches/SKILL.md.
• Random seed, stochastic parameter, dtype, and sample-data details belong to ../parameters-random-and-utilities/SKILL.md.
• Background multiprocessing and Pool behavior belong to ../multicore-and-diagnostics/SKILL.md.

Typical triggers

• “How do I build an imgaug pipeline with flips, affine, blur, and color jitter?”
• “Why did my augmentation change the image shape?”
• “How do I apply the same sampled transform twice?”
• “Which augmenter should I use for cropping, padding, weather, or superpixels?”

Fast path

1. Read references/pipeline-workflows.md for the minimal image-pipeline recipes.
2. Read references/augmenter-family-reference.md for family selection and parameter defaults.
3. Run scripts/generate_augmentation_contact_sheet.py when you need a safe contact sheet or a tiny visual smoke.
4. Read references/troubleshooting.md if the pipeline gives unexpected shapes, dtypes, or color results.

Core usage pattern

Image pipelines usually take numpy.ndarray input of shape (N, H, W, C) or a list of (H, W, C) arrays. Most examples assume RGB uint8 images.

import numpy as np
import imgaug.augmenters as iaa

images = np.zeros((4, 64, 64, 3), dtype=np.uint8)
seq = iaa.Sequential([
    iaa.Fliplr(0.5),
    iaa.Affine(rotate=(-10, 10), translate_px={"x": (-2, 2)}),
    iaa.GaussianBlur(sigma=(0.0, 1.0)),
])
images_aug = seq(images=images)

Use to_deterministic() when you need the same sampled transform in separate calls. Prefer a single call containing all aligned data whenever the task also includes annotations.

Common pipeline decisions

• Use Sequential for ordered pipelines.
• Use SomeOf when a bounded subset of augmenters should run.
• Use OneOf when exactly one branch should run.
• Use Sometimes for probability-gated branches.
• Use WithChannels when only selected channels should change.
• Use fit_output=False and keep_size=True as safe defaults until a task explicitly needs output resizing.
• Keep uint8/RGB in mind when the task starts from OpenCV or camera data.

Handy families and when to reach for them

• Affine, PerspectiveTransform, ElasticTransformation, Jigsaw: geometric distortions.
• Crop, Pad, CropAndPad, Resize, KeepSizeByResize: size and framing.
• GaussianBlur, AverageBlur, MedianBlur, MotionBlur, BilateralBlur, MeanShiftBlur: smoothing/blur.
• Add, Multiply, Dropout, CoarseDropout, Invert, Solarize, JpegCompression: arithmetic/noise/compression.
• WithHueAndSaturation, ChangeColorspace, Grayscale, Posterize, KMeansColorQuantization, UniformColorQuantization: color and palette changes.
• LinearContrast, GammaContrast, HistogramEqualization, CLAHE: contrast shaping.
• BlendAlpha, SimplexNoiseAlpha, FrequencyNoiseAlpha: masked blending and partial effects.
• Superpixels, Voronoi, Snowflakes, Rain, Fog, Clouds, FastSnowyLandscape: segmentation-like and weather effects.

Validation mindset

A good image-pipeline smoke should prove three things:

1. The output image array keeps the expected shape and dtype.
2. The pipeline actually changes pixels when it should.
3. A deterministic replay produces identical results.

Use the bundled script to check those properties with tiny arrays before moving to annotation-aware or multicore workflows.