---

name: data-preparation description: "Prepare paired blur/sharp datasets and explain DeblurGAN image-folder layouts." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

data-preparation

Use this sub-skill when the task is about preparing training or inference image folders for DeblurGAN rather than running the model itself.

What this route covers

• Turning separate blur/sharp folders into paired AB images.
• Explaining the aligned and single dataset layouts used by the repository.
• Checking supported image extensions and folder naming.
• Understanding why the shipped unaligned loader path is not a supported DeblurGAN route.
• Pointing future agents to the safe bundled pair-combination helper.

Read this when the user asks for

• A training set made from two image trees.
• Help with trainA / trainB / testA / testB / single style layouts.
• A way to merge blur and sharp images into one AB file.
• A quick explanation of what the loaders expect before training or inference.

Primary files

• scripts/combine_pairs.py — safe, portable pair-concatenation helper.
• references/data-layout.md — folder layouts, extension rules, and loader behavior.
• references/troubleshooting.md — paired-data and folder-validation failures.

Workflow summary

1. Decide whether the target is paired training data or single-image inference input.
2. Validate that the input folders contain image files with supported extensions.
3. Use the bundled helper to concatenate A and B images horizontally when paired AB files are needed.
4. Keep the resulting folder layout aligned with the repository's dataset mode:
   • aligned expects composite AB images grouped by phase.
   • single expects one folder of standalone images.
5. If a request mentions unaligned, route it cautiously: the repository contains a loader stub, but it is not initialized by the shipped dataset factory and should not be treated as a supported DeblurGAN workflow.

Decision points

• If the user already has side-by-side AB images, no extra merge step is needed.
• If the user has separate blur/sharp folders, the bundled helper is the preferred route.
• If the user needs to download example data, keep that as reference-only guidance because the download helper performs network access.
• If the user is trying to use the colorization helper or motion-blur experiments, those belong outside this route.

Common outputs

• A tree of AB image files for training.
• A single image folder for model=test inference.
• A short troubleshooting explanation for missing files, unsupported extensions, or mismatched pair names.

Cross-links

• Read the root installation reference first if dependencies are missing.
• Read the training sub-skill if the prepared data is meant for optimization.
• Read the inference sub-skill if the prepared data is meant for restoration or evaluation.