---

name: parameters-random-and-utilities description: "Use when controlling imgaug stochastic parameters, seeds, deterministic replay, dtype conversion, sample data, grids, or utility helpers." disable-model-invocation: true metadata: disco-role: operating license: MIT

Parameters, Randomness, and Utilities

Use this sub-skill when the task is about how imgaug samples augmentation parameters, controls reproducibility, handles dtype/range conversion, loads built-in example data, or uses utility functions such as resizing, grids, and display helpers.

What this sub-skill covers

• Parameter shortcuts: scalar values, (a, b) tuples, lists, and imgaug.parameters.StochasticParameter objects.
• Distribution objects such as Choice, Uniform, Normal, and Clip.
• Seeds, RNG, deterministic replay, and deprecated random_state/deterministic API warnings.
• Dtype conversion, clipping, range checks, and compatibility with current NumPy.
• Sample quokka images and annotations from imgaug.data.
• Utility helpers: resize, draw grids, and headless-safe visualization alternatives.

What it does not cover

• Building high-level image pipelines belongs to ../augmentation-pipelines/SKILL.md.
• Constructing keypoints, boxes, dense maps, and mixed batches belongs to ../augmentables-and-batches/SKILL.md.
• Background/multicore execution belongs to ../multicore-and-diagnostics/SKILL.md.

Typical triggers

• “How do tuple parameters work in imgaug?”
• “Make this imgaug pipeline reproducible.”
• “Why does imgaug warn about random_state?”
• “Use the built-in quokka image as a tiny fixture.”
• “Fix dtype clipping or NumPy import errors.”

Fast path

1. Read references/parameters-and-rng.md for stochastic parameters and reproducibility.
2. Read references/data-and-dtype-utilities.md for dtype helpers, resizing, grids, and sample data.
3. Run scripts/smoke_parameters_and_data.py to verify parameter sampling, quokka data, and dtype conversion.
4. Read references/troubleshooting.md for NumPy 2, deprecations, dtype range, and display failures.

Core parameter pattern

Many augmenter parameters accept flexible forms:

import imgaug.augmenters as iaa
import imgaug.parameters as iap

# Shortcut for a uniform blur range.
blur = iaa.GaussianBlur(sigma=(0.0, 3.0))

# Explicit stochastic distribution clipped to a safe range.
param = iap.Clip(iap.Normal(1.0, 0.1), 0.1, 3.0)
blur2 = iaa.GaussianBlur(sigma=param)

Reproducibility pattern

Use a seed on an augmenter or convert a pipeline to deterministic form when the same sampled transform must be replayed.

seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.Add((0, 5))], seed=1)
det = seq.to_deterministic()
out_a = det(images=images)
out_b = det(images=images)

For aligned images and annotations, a single call containing every augmentable remains the safest pattern.

Utility warning signs

• AttributeError involving np.sctypes: install numpy<2 for imgaug 0.4.0.
• Unexpected clipping or rounding: inspect dtype helpers and value ranges before conversion.
• Display failure on a server: avoid ia.imshow; write grids to image files instead.
• Deprecation warnings around random_state or deterministic: prefer seed, RNG, and to_deterministic() patterns.