code/prepare_hf_repo.py

#!/usr/bin/env python3
"""
prepare_hf_repo.py — 生成 HuggingFace 仓库 staging 目录结构。

执行后 staging dir 长这样:
  hf_staging/
  ├── README.md                # HF dataset card (含 YAML front-matter 配 multi-config)
  ├── DATASET_OVERVIEW.md      # 详细 overview (从本仓库 copy)
  ├── LICENSE                  # 多 cohort 协议汇总
  ├── code/                    # 处理代码 (供 reproducibility)
  ├── manifest/                # 3 个 *_images_v1.parquet + studies + sidecar
  ├── captions/                # 3 个 *_captions_v1.parquet
  └── data/                    # pack_for_hf.py 产物 — 本脚本不动 data/
      ├── private_topcon/
      ├── public_fundus/
      └── public_oct/

后续用:
  cd hf_staging && huggingface-cli upload mayberichard/zju-eye-pretrain . --repo-type=dataset
"""
import argparse
import json
import shutil
from pathlib import Path

HF_README_TEMPLATE = """---
license: other
license_name: ophthalmology-mixed
license_link: https://github.com/mayberichard/zju-eye-pretrain/blob/main/LICENSE
task_categories:
- image-classification
- image-segmentation
- image-to-image
- text-to-image
- unconditional-image-generation
language:
- en
- zh
tags:
- ophthalmology
- retina
- oct
- fundus
- slo
- medical-imaging
- segmentation
- pretraining
size_categories:
- 1M<n<10M
pretty_name: ZJU Eye-Pretrain (Private Shanghai Topcon + 25 public cohorts)
configs:
- config_name: all
  data_files:
  - split: train
    path: data/*/*.parquet
- config_name: private_topcon
  data_files:
  - split: train
    path: data/private_topcon/*.parquet
- config_name: public_fundus
  data_files:
  - split: train
    path: data/public_fundus/*.parquet
- config_name: public_oct
  data_files:
  - split: train
    path: data/public_oct/*.parquet
__PER_COHORT_CONFIGS__
---

# ZJU Eye-Pretrain Dataset

> Unified multi-source ophthalmological imaging dataset for foundation model pretraining and downstream tasks.

**1.1M images** spanning **26 cohorts** with a **strict 41-column unified manifest schema**.

## Composition

| Source | Images | Modalities | Cohorts |
|---|---:|---|---|
| Private Shanghai DRI OCT Triton (SS-OCT) | 419,042 | oct_bscan + fundus_color + slo_gray | 1 |
| Public Fundus | 198,629 | fundus_color (+ GAMMA OCT) | 6 |
| Public OCT | 488,705 | oct_bscan | 19 |
| **Total** | **1,106,376** | | **26** |

See [DATASET_OVERVIEW.md](DATASET_OVERVIEW.md) for full details per cohort (devices, regions, masks, demographics).

## Quick Start

```python
from datasets import load_dataset

# Load everything (1.1M images)
ds = load_dataset("mayberichard/zju-eye-pretrain", streaming=True)

# Load one batch
ds = load_dataset("mayberichard/zju-eye-pretrain", "public_oct")

# Load one cohort
ds = load_dataset("mayberichard/zju-eye-pretrain", "kermany")

# Each row:
#   image: PIL.Image
#   {layer/lesion/vessel/disc_cup}_mask: PIL.Image or None
#   image_id, study_id, patient_hash, modality, anatomy, severity, ...
```

## Schema (41-column manifest, identical across all batches)

```
cohort, study_id, patient_hash, visit_date, eye,
device_vendor, device_model, device_serial_hash, device_software_version,
hospital_domain, ethnicity,
image_quality_score, image_quality_band,
diagnosis_group, lesion_tags, lesion_location, layer_involvement, severity,
diagnosis_source, label_confidence, schema_version,
image_id, file_path, file_format,
modality, anatomy, device_technology, scan_protocol,
scan_x_mm, bscan_index,
image_height_px, image_width_px, axial_resolution_um,
has_segmentation, n_layers_visible,
fovea_x_norm, crt_um, choroid_thickness_um,
oct_footprint_bbox_fundus, oct_footprint_bbox_slo,
is_valid
```

Plus per-image `image` bytes and per-cohort mask columns.

## Captions

Each image has 5 captions (4 L1 variants + 1 L3 derived). Total 5.5M captions in `captions/`.

```python
from datasets import load_dataset
caps = load_dataset("mayberichard/zju-eye-pretrain", "captions_oct")
# join on image_id with the images config
```

## Licensing

This dataset aggregates multiple sources with mixed licenses. See [LICENSE](LICENSE) for per-cohort license terms. Users are responsible for compliance with the original license of each cohort.

**Private Shanghai Topcon data is included for research convenience.** Commercial use is prohibited.

## Citation

If you use this dataset, please cite the original source for each cohort used (see DATASET_OVERVIEW.md).

## Versioning & Updates

This dataset supports incremental updates. New cohorts can be added without touching existing data via additional shards in `data/<batch>/`. Schema migrations preserve old `*_v1.parquet` alongside new versions.
"""


PER_COHORT_CONFIG_TEMPLATE = """- config_name: {cohort_short}
  data_files:
  - split: train
    path: data/{batch}/{cohort}-*.parquet"""


# 19 OCT public + 6 public fundus + 1 private = 26 cohorts
COHORT_TO_BATCH = {
    # private
    "shanghai_drioct_triton": "private_topcon",
    # public fundus
    "public_drive_vessel": "public_fundus",
    "public_messidor2_dr": "public_fundus",
    "public_idrid": "public_fundus",
    "public_refuge2_disc_cup": "public_fundus",
    "public_eyepacs_combo_dr_aug": "public_fundus",
    "public_gamma_multimodal": "public_fundus",
    # public OCT
    "public_oct_kermany": "public_oct",
    "public_oct_octid": "public_oct",
    "public_oct_aroi": "public_oct",
    "public_oct_neh_ut_2021": "public_oct",
    "public_oct_areds2": "public_oct",
    "public_oct_glaucoma": "public_oct",
    "public_oct_nyu_poag": "public_oct",
    "public_oct_olives": "public_oct",
    "public_oct_chiu_dme_2015": "public_oct",
    "public_oct_srinivasan_2014": "public_oct",
    "public_oct_sparsity_sdoct_2012": "public_oct",
    "public_oct_oimhs": "public_oct",
    "public_oct_retouch": "public_oct",
    "public_oct_thoct1800": "public_oct",
    "public_oct_octdl": "public_oct",
    "public_oct_amd_sd": "public_oct",
    "public_oct_c8": "public_oct",
    "public_oct_octa500": "public_oct",
    "public_oct_uestc": "public_oct",
}


def cohort_short_name(cohort: str) -> str:
    """public_oct_kermany -> kermany"""
    if cohort == "shanghai_drioct_triton":
        return "private"
    return (cohort
            .replace("public_oct_", "")
            .replace("public_", ""))


def make_readme() -> str:
    per_cohort_lines = []
    for cohort, batch in sorted(COHORT_TO_BATCH.items()):
        short = cohort_short_name(cohort)
        per_cohort_lines.append(PER_COHORT_CONFIG_TEMPLATE.format(
            cohort_short=short, batch=batch, cohort=cohort))
    # Use .replace() not .format() because README body contains literal {} braces
    return HF_README_TEMPLATE.replace("__PER_COHORT_CONFIGS__", "\n".join(per_cohort_lines))


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--src-code-dir", required=True,
                    help="本地 ZJU/Dataset 代码目录")
    ap.add_argument("--src-private", required=True,
                    help="私有数据集根 (含 manifest/ + captions/)")
    ap.add_argument("--src-public-fundus", required=True,
                    help="公开 fundus 根 (含 manifest/ + captions/)")
    ap.add_argument("--src-public-oct", required=True,
                    help="公开 OCT 根 (含 manifest/ + captions/)")
    ap.add_argument("--output", required=True, help="HF staging 目录")
    args = ap.parse_args()

    out = Path(args.output)
    out.mkdir(parents=True, exist_ok=True)
    (out / "data").mkdir(exist_ok=True)
    (out / "data" / "private_topcon").mkdir(exist_ok=True)
    (out / "data" / "public_fundus").mkdir(exist_ok=True)
    (out / "data" / "public_oct").mkdir(exist_ok=True)

    # 1. README.md
    (out / "README.md").write_text(make_readme(), encoding="utf-8")
    print(f"  ✓ README.md ({(out/'README.md').stat().st_size//1024} KB)")

    # 2. DATASET_OVERVIEW.md (从本地 copy)
    src_overview = Path(args.src_code_dir) / "DATASET_OVERVIEW.md"
    if src_overview.exists():
        shutil.copy(src_overview, out / "DATASET_OVERVIEW.md")
        print(f"  ✓ DATASET_OVERVIEW.md")

    # 3. LICENSE
    (out / "LICENSE").write_text(
        "# License Summary (per-cohort)\n\n"
        "This dataset aggregates ophthalmological data from 26 sources with diverse licenses.\n"
        "Users MUST comply with the license of each cohort they use.\n\n"
        "| Cohort | License | Source |\n"
        "|---|---|---|\n"
        "| shanghai_drioct_triton | Research-only (IRB-approved) | Shanghai Zhongshan |\n"
        "| public_drive_vessel | CC | DRIVE 2004 |\n"
        "| public_messidor2_dr | research-only | ADCIS/Messidor-2 |\n"
        "| public_idrid | CC BY 4.0 | IDRiD Challenge |\n"
        "| public_refuge2_disc_cup | research-only | REFUGE2 Challenge |\n"
        "| public_eyepacs_combo_dr_aug | various Kaggle terms | EyePACS combined |\n"
        "| public_gamma_multimodal | research-only | GAMMA Challenge |\n"
        "| public_oct_kermany | CC BY 4.0 | Kermany 2018 (Cell) |\n"
        "| public_oct_octid | CC BY 4.0 | OCTID 2019 |\n"
        "| public_oct_aroi | research-only | AROI 2021 |\n"
        "| public_oct_neh_ut_2021 | research-only | NEH UT 2021 |\n"
        "| public_oct_areds2 | NEI dbGaP | AREDS2 |\n"
        "| public_oct_glaucoma | research-only | Glaucoma OCT 2020 |\n"
        "| public_oct_nyu_poag | CC BY 4.0 | NYU POAG 2023 |\n"
        "| public_oct_olives | CC BY 4.0 | OLIVES NeurIPS 2022 |\n"
        "| public_oct_chiu_dme_2015 | research-only | Duke 2015 |\n"
        "| public_oct_srinivasan_2014 | research-only | Duke/Harvard/Michigan 2014 |\n"
        "| public_oct_sparsity_sdoct_2012 | research-only | 2012 dataset |\n"
        "| public_oct_oimhs | CC BY 4.0 | OIMHS 2023 |\n"
        "| public_oct_retouch | research-only | RETOUCH MICCAI 2017 |\n"
        "| public_oct_thoct1800 | research-only | Tsinghua THOCT1800 |\n"
        "| public_oct_octdl | CC BY 4.0 | OCTDL 2023 |\n"
        "| public_oct_amd_sd | research-only | AMD-SD Nanchang |\n"
        "| public_oct_c8 | various Kaggle terms | C8 compiled |\n"
        "| public_oct_octa500 | research-only | OCTA-500 Li 2024 |\n"
        "| public_oct_uestc | research-only (cite Wu 2023) | UESTC Tianchi |\n"
    )
    print(f"  ✓ LICENSE")

    # 4. code/
    code_dir = out / "code"
    code_dir.mkdir(exist_ok=True)
    src_code = Path(args.src_code_dir)
    code_files = [
        "public_common.py", "oct_public_common.py",
        "extract_fda.py", "extract_subscan.py", "build_manifest.py",
        "adapter_drive.py", "adapter_messidor2.py", "adapter_idrid.py",
        "adapter_refuge2.py", "adapter_eyepacs.py", "adapter_gamma.py",
        "build_public_manifest.py",
        "build_oct_public.py", "build_oct_public_manifest.py",
        "pack_for_hf.py", "prepare_hf_repo.py",
        "INTEGRATION_GUIDE.md", "run.sh",
    ]
    n_copied = 0
    for f in code_files:
        src = src_code / f
        if src.exists():
            shutil.copy(src, code_dir / f)
            n_copied += 1
    print(f"  ✓ code/ ({n_copied} files)")

    # 5. manifest/
    mf_dir = out / "manifest"
    mf_dir.mkdir(exist_ok=True)
    for src_root, prefix in [
        (Path(args.src_private), "private_topcon"),
        (Path(args.src_public_fundus), "public_fundus"),
        (Path(args.src_public_oct), "oct_public"),
    ]:
        src_mf = src_root / "manifest"
        if not src_mf.exists():
            print(f"  WARN: {src_mf} 不存在")
            continue
        for f in src_mf.glob("*.parquet"):
            shutil.copy(f, mf_dir / f.name)
    print(f"  ✓ manifest/ ({len(list(mf_dir.glob('*.parquet')))} parquet)")

    # 6. captions/
    cap_dir = out / "captions"
    cap_dir.mkdir(exist_ok=True)
    for src_root in [Path(args.src_private), Path(args.src_public_fundus),
                     Path(args.src_public_oct)]:
        src_cap = src_root / "captions"
        if not src_cap.exists():
            continue
        for f in src_cap.glob("*.parquet"):
            shutil.copy(f, cap_dir / f.name)
    print(f"  ✓ captions/ ({len(list(cap_dir.glob('*.parquet')))} parquet)")

    print(f"\n[done] HF staging ready: {out}")
    print(f"\n下一步:")
    print(f"  1. (单独) 跑 pack_for_hf.py 把 image bytes 转 parquet shard 到 {out}/data/{{batch}}/")
    print(f"  2. cd {out} && huggingface-cli upload mayberichard/zju-eye-pretrain . --repo-type=dataset")


if __name__ == "__main__":
    main()