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name: pipeline-augmentation description: "Directory-backed Augmentor Pipeline workflows for folder scanning, class-aware disk augmentation, output control, sampling, status, seeding, and threading." disable-model-invocation: true metadata: disco-role: operating license: MIT

Pipeline Augmentation

Use this sub-skill when the task is a directory-backed Augmentor.Pipeline workflow: scan image folders, interpret class subfolders, add pipeline-level operations, write augmented samples to disk, process every image once, set output save formats, inspect status, set seeds, or decide whether to disable multithreading.

Fast routing

• For the operation catalog, probability/range validation, or custom Operation subclasses, route to operation-reference.
• For ground-truth masks, paired mask directories, or in-memory grouped image/mask arrays, route to masks-and-arrays.
• For Keras-style generators, PyTorch/torchvision transforms, DataFrame input, or framework integration, route to generators-and-frameworks.
• For disk pipeline layout, sampling, output folders, class subfolders, reproducibility, and per-class strategy, stay here and use the bundled references.

Core API facts

• Constructor: Augmentor.Pipeline(source_directory=None, output_directory="output", save_format=None).
• Disk execution: sample(n, multi_threaded=True) writes n sampled augmented images; process() processes every image once and internally uses sample(0, multi_threaded=True).
• Output/save controls: set_save_format(save_format) accepts formats understood by Pillow; pass "auto" to return to per-input extension behavior.
• Inspection/control: status() prints operation/image/class/dimension/format status; set_seed(seed) seeds Python random and NumPy random state.
• Directory switching: add_further_directory(new_source_directory, new_output_directory="output") reruns the same population path for another source directory; inspect status() after calling before assuming what images are active.
• ImageUtilities.scan() ignores the configured output directory and treats immediate source subdirectories as class labels.

Minimal disk pattern

import Augmentor

p = Augmentor.Pipeline("train_images", output_directory="output")
p.rotate(probability=0.7, max_left_rotation=10, max_right_rotation=10)
p.flip_left_right(probability=0.5)
p.resize(probability=1.0, width=224, height=224)
p.set_save_format("PNG")

p.set_seed(123)
p.sample(500, multi_threaded=False)  # deterministic transform order is easiest single-threaded

Use p.process() when each source image should be processed once, usually with every required operation set to probability=1. If exact single-threaded execution matters, prefer p.sample(0, multi_threaded=False).

Bundled references and helper

• Workflows: end-to-end disk recipes, sample() versus process(), add_further_directory(), and per-class augmentation strategy.
• Data layouts: class-subfolder scanning, output directory placement, file extensions, and class labels.
• Troubleshooting: empty inputs, output surprises, save-format errors, missing operations, and reproducibility.
• Disk smoke script: generates temporary PIL images, builds a class-aware disk pipeline, samples tiny PNG outputs, and asserts files were written.