---

name: operation-reference description: "Choose and configure Augmentor operations, validation ranges, and custom Operation subclasses." disable-model-invocation: true metadata: disco-role: operating license: MIT

Augmentor operation reference

Use this sub-skill when a task is about selecting image augmentation operations, setting operation parameters, recovering from operation validation errors, or adding a custom Augmentor.Operations.Operation subclass to a pipeline.

Fast routing

• Need method signatures, valid ranges, and exact recovery hints? Open references/api-reference.md.
• Need to choose transformations for an augmentation goal? Open references/operation-selection.md.
• Need to debug validation errors, Pillow resize filters, or custom operations? Open references/troubleshooting.md.
• Need a safe runtime smoke check? Run scripts/augmentor_operation_probe.py in an environment where Augmentor, Pillow, and NumPy are installed.

Operating rules

1. Add operations to an Augmentor.Pipeline before sampling or processing; operation order is the execution order.
2. Treat every operation probability as 0 < probability <= 1. A probability of 1 is the normal choice for deterministic resizing, greyscale conversion, and other required transforms.
3. Keep geometry parameters conservative: arbitrary rotation and shear are designed for at most 25 degrees; larger values are rejected or produce unusable images.
4. Operation.perform_operation(images) receives a list of PIL Images and must return a list of PIL Images. This list contract matters for future mask/ground-truth workflows.
5. Prefer Pipeline convenience methods for built-in operations. Mention lower-level classes such as HSVShifting and Mixup only as manual Operation classes; they are not top-level Pipeline convenience workflows.

Boundaries

• Route disk scanning, output directories, class subfolders, sample(), process(), seeding, and multithreading to pipeline-augmentation.
• Route ground-truth masks, identical transforms across images and masks, and in-memory grouped arrays to masks-and-arrays.
• Route Keras-style generators, PyTorch transforms, and DataFrame-backed inputs to generators-and-frameworks.