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layout-io

layout-io

Routes LayoutParser JSON, CSV, dataframe, PDF, and annotation-loading workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: layout-io description: "Routes LayoutParser JSON, CSV, dataframe, PDF, and annotation-loading workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Layout I/O

Use this sub-skill when the user starts with files, tables, or PDFs and wants a Layout object back.

What belongs here

  • load_json, load_dict
  • load_csv, load_dataframe
  • load_pdf
  • page_data, block_type, and serialized textblock fields
  • COCO-style conversion patterns that become layout objects before rendering

What does not belong here

  • Shape math and Layout transforms: use layout-objects
  • draw_box / draw_text: use visualization
  • OCR engine wrappers and response parsing: use ocr
  • lp:// model zoo paths and model inference: use layout-models

Read these files

  • references/guide.md for file-format rules, PDF behavior, and troubleshooting notes
  • scripts/coco_layout_helpers.py when you need a reusable COCO-to-Layout conversion helper
  • ../layout-objects/SKILL.md when you need to filter, sort, or transform the loaded layout
  • ../visualization/SKILL.md when you need to show the loaded layout on an image

Fast path

  1. Decide whether the source is JSON, CSV, dataframe, or PDF.
  2. Make sure the serialized schema still contains block_type and any needed textblock metadata.
  3. Load the data into a Layout.
  4. If the task is PDF-specific, inspect page dimensions in page_data before drawing or cropping.
  5. Preserve page order when you export page-level layouts.

Common user requests

  • "Load a LayoutParser JSON file"
  • "Convert a CSV of boxes into a layout"
  • "Parse a PDF into token boxes"
  • "Read layout annotations and visualize them"
  • "Turn COCO annotations into LayoutParser blocks"

Minimal smoke

Use the bundled root smoke script to confirm the I/O layer and the synthetic round-trips:

python ../../scripts/smoke_layoutparser_core.py

Failure clues

  • Missing block_type means the serialized data cannot be routed back to a concrete block class.
  • Quadrilateral CSV values in a points column must parse back to Python lists; if they stay as strings, pre-parse or use JSON.
  • A PDF page with no tokens is still a valid result.
  • load_pdf(..., load_images=True) depends on the PDF image renderer stack and may need poppler.

Output discipline

When answering, be explicit about the input format and the fields that must be preserved. If the user wants a local helper for annotation conversion, keep it inside the generated skill tree rather than depending on the original notebook.

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