#!/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 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//`. 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()