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데이터 및 연구 · SKILL.md

data-preparation

data-preparation

Prepare and validate easy12306 captcha crops, tiles, perceptual hashes, label vocabulary artifacts, and OCR-assisted labeling workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: data-preparation description: "Prepare and validate easy12306 captcha crops, tiles, perceptual hashes, label vocabulary artifacts, and OCR-assisted labeling workflows." disable-model-invocation: true metadata: disco-role: operating license: Artistic 2.0

easy12306 Data Preparation Router

Load this sub-skill when the task is to recreate, inspect, or validate the data artifacts behind easy12306: captcha acquisition, prompt-text crops, 8 image tiles, perceptual hashes, label vocabulary rows, OCR-assisted text labeling, or hash-label aggregation.

Do not use this sub-skill for pretrained model inference, text-model training, or image-model training. Route those tasks to inference, text-modeling, or image-modeling respectively.

Required references

  • Workflows: acquisition, crop/tile extraction, dataset assembly, OCR-assisted labeling, and hash-label aggregation.
  • Data formats: captcha geometry, crop/tile dimensions, .npz schemas, and label-vocabulary expectations.
  • Hash reference: exact packed 64-bit hash algorithms and the excluded wavelet hash caveat.
  • Baidu OCR labeling: credentialed, network-only reference workflow and import-time hazard.
  • Troubleshooting: common geometry, dependency, label, hash, credential, and compatibility failures.
  • Root label vocabulary should be read from ../../references/label-vocabulary.md after the integrated root skill creates it.
  • Root model/data artifact context should be read from ../../references/model-artifacts.md after integration creates it.

Bundled scripts

  • python scripts/captcha_preprocess_diagnostic.py --help
  • python scripts/captcha_preprocess_diagnostic.py --self-test
  • python scripts/captcha_preprocess_diagnostic.py --image CAPTCHA.jpg --labels-file texts.txt --npz data.npz
  • python scripts/hash_image_tiles.py --help
  • python scripts/hash_image_tiles.py --self-test
  • python scripts/hash_image_tiles.py --image CAPTCHA.jpg --method phash

The scripts are self-contained adaptations of the safe crop, tile, and hash logic. They do not depend on an original source checkout and do not call credentialed OCR or captcha download endpoints.

Safety and boundary rules

  • Treat bulk captcha downloading as an explicitly approved network workflow only. The legacy loop targeted 40,000 images and must not be run as an automatic diagnostic.
  • Do not import or run the credentialed OCR helper from the source project. It requests a Baidu token at import time with placeholder credentials.
  • Preserve the source geometry exactly: text crop rows [3:22], columns [120+offset:177+offset]; eight 67x67 tiles from row starts 40,112 and column starts 5,77,149,221 for a 190x293 captcha.
  • Preserve packed hash semantics: the standard hash artifact is 8 bytes per tile, normally represented as a 16-character hex string or an 8-byte uint8 vector.
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