Augmentor Repo Skill

Use this skill when a task names Augmentor or asks for Augmentor-style image augmentation: stochastic Pipeline construction, Pillow/NumPy operations, class-folder scanning, generated image output, ground-truth/mask pairing, in-memory DataPipeline arrays, Keras-style batches, or torchvision transform callables.

Augmentor is a CPU/Pillow/NumPy package. It does not require CUDA, ROCm, MPS, Keras/TensorFlow, or torch/torchvision for core package workflows.

First checks

1. Install the package in the task environment:

   pip install Augmentor
   

   For Augmentor 0.2.x compatibility-sensitive work, prefer:

   pip install 'Augmentor==0.2.12' 'Pillow<10' 'numpy<2' tqdm
   

2. Verify the import:

   python -c "import Augmentor; print(Augmentor.__version__)"
   

3. For a safe end-to-end check, run the bundled smoke helper:

   python scripts/augmentor_env_smoke.py --samples 2 --size 24
   

4. If the task uses masks, framework generators, or optional pandas/torch/Keras integrations, route to the matching sub-skill before giving final code.

Route by task

User task or signal	Read next
Create a Pipeline from a folder, scan class subfolders, write augmented files, choose sample() vs process(), control output directories, seeds, or multithreading.	sub-skills/pipeline-augmentation/SKILL.md
Choose operations, fix probability/range errors, understand rotate/crop/zoom/skew/distortion/color operations, or write custom Operation subclasses.	sub-skills/operation-reference/SKILL.md
Apply identical transforms to images and masks, use ground_truth(), verify matched filenames/classes/dimensions, or build grouped in-memory original+mask arrays.	sub-skills/masks-and-arrays/SKILL.md
Use keras_generator, keras_generator_from_array, keras_preprocess_func, torch_transform, or DataFramePipeline; debug batch shapes or optional framework dependencies.	sub-skills/generators-and-frameworks/SKILL.md
Diagnose install/import, Pillow/PIL, save-format, dependency, stochastic reproducibility, or optional dependency issues that span several workflows.	references/troubleshooting.md
Check whether this skill matches a local Augmentor checkout or package version.	references/repo-provenance.md

Minimal examples

Disk-backed augmentation

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.sample(100, multi_threaded=False)

Outputs are written under the source directory's output folder. If the source directory has immediate subdirectories, Augmentor treats those subdirectories as class labels. Read pipeline-augmentation before changing layouts or counting class outputs.

Mask-safe augmentation

import Augmentor

p = Augmentor.Pipeline("images")
p.ground_truth("masks")
p.rotate(probability=1.0, max_left_rotation=5, max_right_rotation=5)
p.sample(20)

Use masks-and-arrays to validate matched names, class subfolders, equal dimensions, and multiple masks per image.

Generator batches

import Augmentor

p = Augmentor.Pipeline("train_images")
g = p.keras_generator(batch_size=32, scaled=True, image_data_format="channels_last")
images, labels = next(g)

The direct Augmentor generator APIs return NumPy arrays and do not import Keras/TensorFlow. Read generators-and-frameworks before promising external framework behavior.

Compatibility caveats

• Augmentor 0.2.x predates newer Pillow and NumPy APIs. For legacy behavior, Pillow<10 and numpy<2 are safer than latest-only installs.
• DataFramePipeline is optional and legacy. This checkout's scan_dataframe() path failed with pandas 1.5.3 and 3.0.5 because it calls Categorical.get_values(). Prefer ordinary Pipeline or DataPipeline unless maintaining or patching Augmentor.
• torch_transform() returns a PIL-image callable; torchvision is optional and only needed for torchvision.transforms.Compose or ToTensor().
• Do not assert exact output filenames in tests or examples. Augmentor uses UUID filenames; assert counts, dimensions, formats, labels, and readable images.

Bundled references and scripts

• references/quickstart.md gives a compact route map and common snippets.
• references/troubleshooting.md covers cross-cutting install/import, dependency, save-format, optional dependency, and reproducibility issues.
• references/repo-provenance.md records the source version and evidence baseline for refresh decisions.
• references/repo-routing-metadata.json provides managed repo-skills-router metadata for import tooling.
• scripts/augmentor_env_smoke.py runs a safe generated-fixture smoke check for the active Python environment.

Boundaries

Use this skill for using Augmentor as a package. For maintainer tasks that modify Augmentor source code, packaging, CI, or docs, combine this usage skill with a Python repository maintenance workflow and run focused source tests after editing.