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name: data-preparation description: "Routes dataset layout checks, Cityscapes preparation, pair assembly, aligned export, and cat-face crop workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

data-preparation

Use this sub-skill when the task is about shaping data for CUT, FastCUT, or SinCUT: building the expected folder layout, converting Cityscapes, assembling paired images, exporting aligned composites, or cropping cat faces for the grumpifycat example.

Read this when

• The user needs to prepare a trainA/trainB or testA/testB dataset.
• The user needs the single-image dataset layout with exactly one image per domain.
• The user wants to convert Cityscapes into CUT-ready folders.
• The user wants to combine two directories into side-by-side pix2pix-style pairs.
• The user wants to build aligned train and test composite images from A/B folders.
• The user needs a cat-face crop helper for the grumpifycat workflow.

What this sub-skill covers

• Dataset layout conventions from docs/datasets.md and the dataset loader modules.
• scripts/prepare_cityscapes_dataset.py for Cityscapes conversion.
• scripts/combine_A_and_B.py for paired A/B assembly.
• scripts/make_dataset_aligned.py for aligned composite exports.
• scripts/detect_cat_face.py for cat-face cropping.

What this sub-skill does not cover

• Training, testing, checkpoint loading, or output inspection. Read ../translation-workflows/ for those tasks.
• Launcher presets or tmux orchestration. Read ../experiment-launchers/ for those tasks.
• Network download shell scripts. They are reference-only because they fetch external datasets.

Use the bundled helpers

• scripts/prepare_cityscapes_dataset.py converts raw Cityscapes exports into CUT/pix2pix-style folders.
• scripts/combine_A_and_B.py builds paired composite images from two parallel trees.
• scripts/make_dataset_aligned.py exports side-by-side aligned images from trainA/trainB and testA/testB.
• scripts/detect_cat_face.py crops detected cat faces for the grumpifycat-style example.

Recommended reading order

• references/data-formats.md for the exact folder structures and naming rules.
• references/workflows.md for end-to-end preparation recipes.
• references/troubleshooting.md for missing images, filename mismatches, and OpenCV/cascade issues.

Common routing choices

• If the user only needs a data folder layout explanation, stay here and read references/data-formats.md.
• If the user also needs to train or test afterward, hand off to ../translation-workflows/ after the data is prepared.
• If the user wants to print experiment command strings, hand off to ../experiment-launchers/.

Quick reminders

• data.unaligned_dataset.UnalignedDataset expects unpaired folders such as trainA and trainB.
• data.singleimage_dataset.SingleImageDataset expects exactly one image in trainA and one in trainB.
• The bundled helpers are intentionally deterministic and self-contained so they do not depend on the original checkout.
• The network download scripts stay out of the runtime tree because they fetch external data and are not safe default helpers.