| """Build an embedded HTML review page for high-risk training rows. |
| |
| This is a targeted human-QC sampler for prompt-adapter manifests. It does not |
| modify source images or training manifests. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import base64 |
| import csv |
| import html |
| import io |
| import json |
| import math |
| import random |
| import tarfile |
| from pathlib import Path |
| from typing import Any |
|
|
| import pandas as pd |
| from PIL import Image |
|
|
|
|
| DEFAULT_MANIFEST = Path( |
| "/data/team/lisicheng/new_data_manifest/" |
| "prompt_adapter_mixed_v6_exclude_octbuckets_octa/adapter_manifest_mixed_v6.parquet" |
| ) |
| DEFAULT_CAPTIONS = Path( |
| "/data/team/lisicheng/new_data_manifest/" |
| "prompt_adapter_mixed_v6_exclude_octbuckets_octa/captions_v2.parquet" |
| ) |
| DEFAULT_OUT = Path("/data/team/lisicheng/Code/OCTFlow/pilot/path1/audit/v6_quality_risk_review_20260709") |
|
|
| HIGH_RISK_COHORTS = [ |
| "data0410_master_fundus", |
| "chengdu_shiyi_ubm", |
| "eryuan_ubm", |
| "yinhai_ubm", |
| "yinhai_bscan", |
| "xiangya_bscan", |
| "chengdu_bscan", |
| "yinhai_uwf", |
| "eryuan_ffa", |
| "eryuan_t17_fundus", |
| "hcw_fundus", |
| ] |
| DEID_SANITY_COHORTS = [ |
| "fundus90wer", |
| "tongren_fundus", |
| "tongren_95disease_fundus", |
| "tongren_external_fundus", |
| "fq_rawfundus", |
| "fq_testfundus", |
| ] |
| RARE_OR_CONFUSABLE_SUBTYPES = [ |
| "octa_enface", |
| "oct_enface", |
| "slit_lamp_anterior_segment", |
| "reflectance_slo_gray", |
| "fundus_fluorescein_angiography", |
| "uwf_color_fundus", |
| "external_anterior_segment_photo", |
| "ocular_bscan_ultrasound", |
| "ubm_anterior_segment", |
| ] |
|
|
|
|
| class ImageOpener: |
| def __init__(self) -> None: |
| self._tar_cache: dict[str, tarfile.TarFile] = {} |
|
|
| def close(self) -> None: |
| for tf in self._tar_cache.values(): |
| tf.close() |
| self._tar_cache.clear() |
|
|
| def open(self, path: str) -> Image.Image: |
| if "::" not in path: |
| return Image.open(path).convert("RGB") |
| tar_path, member = path.split("::", 1) |
| tf = self._tar_cache.get(tar_path) |
| if tf is None: |
| tf = tarfile.open(tar_path, "r:*") |
| self._tar_cache[tar_path] = tf |
| fh = tf.extractfile(member) |
| if fh is None: |
| raise FileNotFoundError(member) |
| return Image.open(io.BytesIO(fh.read())).convert("RGB") |
|
|
|
|
| def clean(value: Any) -> Any: |
| if value is None: |
| return None |
| if isinstance(value, float) and math.isnan(value): |
| return None |
| return value |
|
|
|
|
| def json_safe(value: Any) -> Any: |
| if isinstance(value, dict): |
| return {str(k): json_safe(v) for k, v in value.items()} |
| if isinstance(value, list | tuple): |
| return [json_safe(v) for v in value] |
| if value is None: |
| return None |
| if isinstance(value, float): |
| return value if math.isfinite(value) else None |
| try: |
| if pd.isna(value): |
| return None |
| except (TypeError, ValueError): |
| pass |
| return value |
|
|
|
|
| def sample_group(group: pd.DataFrame, n: int, seed: int) -> pd.DataFrame: |
| if len(group) <= n: |
| return group |
| return group.sample(n=n, random_state=seed) |
|
|
|
|
| def add_samples( |
| selected: dict[str, dict[str, Any]], |
| frame: pd.DataFrame, |
| reason: str, |
| n: int, |
| seed: int, |
| group_col: str | None = None, |
| ) -> None: |
| if len(frame) == 0: |
| return |
| if group_col: |
| pieces = [sample_group(g, n, seed) for _, g in frame.groupby(group_col, sort=True)] |
| rows = pd.concat(pieces, ignore_index=True) if pieces else frame.head(0) |
| else: |
| rows = sample_group(frame, n, seed) |
| for row in rows.to_dict("records"): |
| image_id = str(row["image_id"]) |
| if image_id in selected: |
| selected[image_id]["risk_reason"] += f"; {reason}" |
| else: |
| row["risk_reason"] = reason |
| selected[image_id] = row |
|
|
|
|
| def build_review_rows(args: argparse.Namespace) -> tuple[list[dict[str, Any]], dict[str, Any]]: |
| cols = [ |
| "image_id", |
| "cohort", |
| "hospital_domain", |
| "modality", |
| "modality_family", |
| "modality_subtype", |
| "view_or_projection", |
| "field_of_view", |
| "image_quality_band", |
| "image_quality_score", |
| "image_height_px", |
| "image_width_px", |
| "file_path", |
| "prompt_modality_phrase", |
| "diagnosis_group_clean", |
| ] |
| df = pd.read_parquet(args.manifest, columns=cols) |
| rng_seed = args.seed |
| selected: dict[str, dict[str, Any]] = {} |
|
|
| add_samples( |
| selected, |
| df[df["image_quality_band"].fillna("").astype(str).eq("poor")], |
| "显式 poor quality", |
| args.poor_per_modality, |
| rng_seed, |
| "modality_subtype", |
| ) |
| add_samples( |
| selected, |
| df[df["cohort"].astype(str).isin(HIGH_RISK_COHORTS)], |
| "私有原始路径或水印/PII 高风险 cohort", |
| args.high_risk_per_cohort, |
| rng_seed + 1, |
| "cohort", |
| ) |
| add_samples( |
| selected, |
| df[df["cohort"].astype(str).isin(DEID_SANITY_COHORTS)], |
| "已脱敏大 cohort 抽查", |
| args.deid_sanity_per_cohort, |
| rng_seed + 2, |
| "cohort", |
| ) |
| add_samples( |
| selected, |
| df[df["modality_subtype"].astype(str).isin(RARE_OR_CONFUSABLE_SUBTYPES)], |
| "稀有或易混淆 subtype 抽查", |
| args.rare_per_subtype, |
| rng_seed + 3, |
| "modality_subtype", |
| ) |
| top_cohorts = df["cohort"].value_counts().head(args.top_cohorts).index |
| add_samples( |
| selected, |
| df[df["cohort"].astype(str).isin(top_cohorts.astype(str))], |
| "大 cohort 随机兜底抽查", |
| args.top_per_cohort, |
| rng_seed + 4, |
| "cohort", |
| ) |
|
|
| rows = list(selected.values()) |
| random.Random(args.seed).shuffle(rows) |
| if args.max_rows and len(rows) > args.max_rows: |
| rows = rows[: args.max_rows] |
|
|
| ids = [str(r["image_id"]) for r in rows] |
| captions = pd.read_parquet(args.captions, columns=["image_id", "level", "prompt_text"]) |
| captions["image_id"] = captions["image_id"].astype(str) |
| wide = captions[captions["image_id"].isin(ids)].pivot(index="image_id", columns="level", values="prompt_text") |
|
|
| opener = ImageOpener() |
| records: list[dict[str, Any]] = [] |
| try: |
| for idx, row in enumerate(rows, start=1): |
| image_id = str(row["image_id"]) |
| thumb, open_error = thumbnail(opener, str(row["file_path"]), args.thumb_size) |
| record = { |
| "review_seq": idx, |
| **{k: clean(v) for k, v in row.items()}, |
| "short_prompt": clean(wide["short"].get(image_id)) if "short" in wide else None, |
| "medium_prompt": clean(wide["medium"].get(image_id)) if "medium" in wide else None, |
| "dense_prompt": clean(wide["dense"].get(image_id)) if "dense" in wide else None, |
| "thumb_data": thumb, |
| "open_error": open_error, |
| } |
| records.append(record) |
| finally: |
| opener.close() |
|
|
| summary = { |
| "manifest": str(args.manifest), |
| "captions": str(args.captions), |
| "rows_in_manifest": int(len(df)), |
| "review_rows": int(len(records)), |
| "risk_reason_counts": pd.Series([r["risk_reason"] for r in records]).value_counts().astype(int).to_dict(), |
| "review_by_cohort": pd.Series([r["cohort"] for r in records]).value_counts().astype(int).to_dict(), |
| "review_by_modality_subtype": pd.Series([r["modality_subtype"] for r in records]).value_counts().astype(int).to_dict(), |
| "poor_quality_rows": int(df["image_quality_band"].fillna("").astype(str).eq("poor").sum()), |
| "high_risk_cohort_rows": int(df["cohort"].astype(str).isin(HIGH_RISK_COHORTS).sum()), |
| } |
| return records, summary |
|
|
|
|
| def thumbnail(opener: ImageOpener, path: str, size: int) -> tuple[str, str]: |
| try: |
| im = opener.open(path) |
| im.thumbnail((size, size), Image.Resampling.LANCZOS) |
| canvas = Image.new("RGB", (size, size), (0, 0, 0)) |
| canvas.paste(im, ((size - im.width) // 2, (size - im.height) // 2)) |
| err = "" |
| except Exception as exc: |
| canvas = Image.new("RGB", (size, size), (100, 15, 15)) |
| err = repr(exc) |
| buf = io.BytesIO() |
| canvas.save(buf, format="JPEG", quality=88) |
| return "data:image/jpeg;base64," + base64.b64encode(buf.getvalue()).decode("ascii"), err |
|
|
|
|
| def write_csv(records: list[dict[str, Any]], out: Path) -> None: |
| cols = [ |
| "review_seq", |
| "risk_reason", |
| "image_id", |
| "cohort", |
| "hospital_domain", |
| "modality", |
| "modality_family", |
| "modality_subtype", |
| "view_or_projection", |
| "field_of_view", |
| "image_quality_band", |
| "image_quality_score", |
| "image_height_px", |
| "image_width_px", |
| "prompt_modality_phrase", |
| "diagnosis_group_clean", |
| "short_prompt", |
| "medium_prompt", |
| "dense_prompt", |
| "file_path", |
| "open_error", |
| ] |
| with out.open("w", newline="", encoding="utf-8-sig") as handle: |
| writer = csv.DictWriter(handle, fieldnames=cols) |
| writer.writeheader() |
| for row in records: |
| writer.writerow({col: row.get(col) for col in cols}) |
|
|
|
|
| def write_html(records: list[dict[str, Any]], out: Path) -> None: |
| rows_json = json.dumps(json_safe(records), ensure_ascii=False, allow_nan=False).replace("</", "<\\/") |
| css = """:root{--bg:#f5f6f8;--panel:#fff;--line:#d8dee8;--text:#1f2937;--muted:#667085;--blue:#0f62fe;--green:#177245;--red:#b42318;--amber:#9a5b00}*{box-sizing:border-box}body{margin:0;background:var(--bg);color:var(--text);font-family:Arial,"Noto Sans CJK SC","Microsoft YaHei",sans-serif}header{position:sticky;top:0;z-index:10;background:var(--panel);border-bottom:1px solid var(--line);padding:12px 16px;box-shadow:0 1px 8px rgba(15,23,42,.07)}h1{font-size:18px;margin:0 0 8px}.bar{display:flex;gap:8px;align-items:center;flex-wrap:wrap}.progress{font-size:13px;min-width:260px;color:var(--muted)}button,select,input,textarea{font-size:13px;border:1px solid #b8c0cc;border-radius:4px;background:#fff;padding:7px 9px}button{cursor:pointer}button.primary{background:var(--blue);border-color:var(--blue);color:#fff}button.ok{background:#e9f7ef;color:var(--green);border-color:#a8ddb9}button.bad{background:#fff1f0;color:var(--red);border-color:#ffccc7}button.warn{background:#fff7e6;color:var(--amber);border-color:#ffd591}.hint{font-size:12px;color:var(--muted);margin-top:6px}.viewer{display:grid;grid-template-columns:minmax(440px,52vw) 1fr;gap:14px;padding:14px}.panel{background:var(--panel);border:1px solid var(--line);border-radius:6px;padding:12px}.imgbox{height:min(68vh,720px);min-height:420px;background:#050505;display:flex;align-items:center;justify-content:center;overflow:hidden}.imgbox img{max-width:100%;max-height:100%;object-fit:contain}.badge{display:inline-block;border-radius:999px;padding:3px 8px;margin:0 5px 6px 0;font-size:12px;background:#edf2ff;color:#173b8f}.badge.warn{background:#fff7e6;color:#8a4b00}.badge.bad{background:#ffecec;color:#9b1c1c}.meta{font-size:13px;line-height:1.5;word-break:break-word}.prompt{font-size:13px;line-height:1.45;margin-top:8px;padding:8px;background:#f8fafc;border:1px solid #e2e8f0;border-radius:4px}.review{border-top:1px solid var(--line);margin-top:10px;padding-top:10px}.row{display:flex;gap:8px;align-items:center;flex-wrap:wrap;margin:8px 0}textarea{width:100%;height:72px;resize:vertical}.path{font-family:monospace;font-size:12px;color:#334155}@media(max-width:1000px){.viewer{grid-template-columns:1fr}.imgbox{height:520px}}""" |
| js = """ |
| const rows = JSON.parse(document.getElementById('rows-data').textContent); |
| const storageKey = 'octflow_v6_quality_risk_review_20260709'; |
| function loadState(key, fallback) { try { return localStorage.getItem(key) ?? fallback; } catch (_) { return fallback; } } |
| let idx = Number(loadState(storageKey + ':idx', '0') || 0); |
| let edits = JSON.parse(loadState(storageKey, '{}') || '{}'); |
| idx = Math.max(0, Math.min(idx, rows.length - 1)); |
| function esc(v) { return String(v ?? '').replace(/[&<>"']/g, c => ({'&':'&','<':'<','>':'>','"':'"',"'":'''}[c])); } |
| function edit(id) { return edits[id] || {status:'unreviewed', issue:'', note:''}; } |
| function save() { try { localStorage.setItem(storageKey, JSON.stringify(edits)); localStorage.setItem(storageKey + ':idx', String(idx)); } catch (_) {} } |
| function counts() { const c={unreviewed:0,keep:0,watermark:0,pii:0,wrong_modality:0,low_quality:0,drop:0}; rows.forEach(r=>{ const s=edit(r.image_id).status || 'unreviewed'; c[s]=(c[s]||0)+1; }); return c; } |
| function go(delta) { idx=Math.max(0, Math.min(rows.length-1, idx+delta)); save(); render(); } |
| function gotoIndex(v) { const n=Number(v); if (Number.isFinite(n)) { idx=Math.max(0, Math.min(rows.length-1, n-1)); save(); render(); } } |
| function setStatus(status, advance=true) { const r=rows[idx]; edits[r.image_id]={...edit(r.image_id),status}; save(); if (advance) go(1); else render(); } |
| function updateNote(v) { const r=rows[idx]; edits[r.image_id]={...edit(r.image_id),note:v}; save(); } |
| function mergedRows() { return rows.map(r=>{ const x={...r, ...edit(r.image_id)}; delete x.thumb_data; return x; }); } |
| function download(name, text, type) { const blob=new Blob([text],{type}); const url=URL.createObjectURL(blob); const a=document.createElement('a'); a.href=url; a.download=name; document.body.appendChild(a); a.click(); a.remove(); URL.revokeObjectURL(url); } |
| function exportCSV() { const out=mergedRows(); const cols=['review_seq','status','note','risk_reason','image_id','cohort','hospital_domain','modality','modality_family','modality_subtype','view_or_projection','field_of_view','image_quality_band','image_quality_score','prompt_modality_phrase','diagnosis_group_clean','short_prompt','file_path','open_error']; const csv=[cols.join(',')].concat(out.map(o=>cols.map(c=>'"'+String(o[c]??'').replaceAll('"','""')+'"').join(','))).join('\\n'); download('octflow_v6_quality_risk_review_edits.csv', csv, 'text/csv'); } |
| function exportJSON() { download('octflow_v6_quality_risk_review_edits.json', JSON.stringify(mergedRows(), null, 2), 'application/json'); } |
| function render() { |
| if (!rows.length) { document.getElementById('root').innerHTML='<div class=viewer><div class=panel>没有审核样本。</div></div>'; return; } |
| const r=rows[idx], e=edit(r.image_id), c=counts(); |
| document.getElementById('progress').textContent=`第 ${idx+1} / ${rows.length} 张；保留 ${c.keep||0}，水印 ${c.watermark||0}，PII ${c.pii||0}，错模态 ${c.wrong_modality||0}，低质 ${c.low_quality||0}，丢弃 ${c.drop||0}，未看 ${c.unreviewed||0}`; |
| document.getElementById('jump').value=idx+1; |
| document.getElementById('root').innerHTML=`<section class="viewer"><div class="panel"><div class="imgbox"><img src="${esc(r.thumb_data)}" loading="eager"></div></div><div class="panel"><div><span class="badge warn">${esc(r.risk_reason)}</span><span class="badge">${esc(r.modality_subtype)}</span><span class="badge">${esc(r.cohort)}</span>${r.open_error ? '<span class="badge bad">读取错误</span>' : ''}</div><div class="meta"><b>图像 ID</b>：${esc(r.image_id)}<br><b>cohort</b>：${esc(r.cohort)}；<b>center</b>：${esc(r.hospital_domain)}<br><b>modality</b>：${esc(r.modality)}；<b>family</b>：${esc(r.modality_family)}；<b>subtype</b>：${esc(r.modality_subtype)}<br><b>view/fov</b>：${esc(r.view_or_projection)} / ${esc(r.field_of_view)}<br><b>quality</b>：${esc(r.image_quality_band)} / ${esc(r.image_quality_score)}；<b>size</b>：${esc(r.image_width_px)} x ${esc(r.image_height_px)}<br><b>diagnosis</b>：${esc(r.diagnosis_group_clean)}<br><b>路径</b>：<span class="path">${esc(r.file_path)}</span>${r.open_error ? '<br><b>读取错误</b>：'+esc(r.open_error) : ''}</div><div class="prompt"><b>短 prompt</b>：${esc(r.short_prompt)}</div><div class="prompt"><b>中 prompt</b>：${esc(r.medium_prompt)}</div><div class="review"><div class="row"><button class="ok" onclick="setStatus('keep')">1 保留</button><button class="warn" onclick="setStatus('watermark')">2 水印</button><button class="bad" onclick="setStatus('pii')">3 PII</button><button class="warn" onclick="setStatus('wrong_modality', false)">4 错模态</button><button class="warn" onclick="setStatus('low_quality')">5 低质</button><button class="bad" onclick="setStatus('drop')">6 丢弃</button><button onclick="setStatus('unreviewed', false)">清空</button></div><textarea placeholder="备注" oninput="updateNote(this.value)">${esc(e.note || '')}</textarea></div><div class="hint">快捷键：1 保留，2 水印，3 PII，4 错模态，5 低质，6 丢弃，←/→ 翻页，C 导出 CSV，J 导出 JSON。备注框内不会触发快捷键。</div></div></section>`; |
| } |
| document.addEventListener('keydown', ev => { |
| const tag=(ev.target && ev.target.tagName || '').toLowerCase(); |
| if (tag === 'input' || tag === 'textarea' || tag === 'select') return; |
| if (ev.key === 'ArrowRight') go(1); |
| else if (ev.key === 'ArrowLeft') go(-1); |
| else if (ev.key === '1') setStatus('keep'); |
| else if (ev.key === '2') setStatus('watermark'); |
| else if (ev.key === '3') setStatus('pii'); |
| else if (ev.key === '4') setStatus('wrong_modality', false); |
| else if (ev.key === '5') setStatus('low_quality'); |
| else if (ev.key === '6') setStatus('drop'); |
| else if (ev.key.toLowerCase() === 'c') exportCSV(); |
| else if (ev.key.toLowerCase() === 'j') exportJSON(); |
| }); |
| render(); |
| """ |
| doc = f"""<!doctype html><html><head><meta charset="utf-8"><title>OCTFlow v6 风险样本审核</title><style>{css}</style></head> |
| <body><header><h1>OCTFlow v6 风险样本审核</h1><div class="bar"><span id="progress" class="progress"></span><button onclick="go(-1)">上一张</button><button class="primary" onclick="go(1)">下一张</button><label>跳转 <input id="jump" type="number" min="1" max="{len(records)}" onchange="gotoIndex(this.value)" style="width:80px"></label><button onclick="exportCSV()">导出 CSV</button><button onclick="exportJSON()">导出 JSON</button></div><div class="hint">风险导向抽样：poor quality、私有原始路径/水印高风险 cohort、已脱敏大 cohort 抽查、稀有或易混淆 subtype、大 cohort 兜底抽查。这里只用于人工 QC，不修改训练数据。</div></header><main id="root"></main><script type="application/json" id="rows-data">{rows_json}</script><script>{js}</script></body></html>""" |
| out.write_text(doc, encoding="utf-8") |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--manifest", type=Path, default=DEFAULT_MANIFEST) |
| parser.add_argument("--captions", type=Path, default=DEFAULT_CAPTIONS) |
| parser.add_argument("--out", type=Path, default=DEFAULT_OUT) |
| parser.add_argument("--seed", type=int, default=20260709) |
| parser.add_argument("--thumb-size", type=int, default=512) |
| parser.add_argument("--poor-per-modality", type=int, default=20) |
| parser.add_argument("--high-risk-per-cohort", type=int, default=6) |
| parser.add_argument("--deid-sanity-per-cohort", type=int, default=4) |
| parser.add_argument("--rare-per-subtype", type=int, default=8) |
| parser.add_argument("--top-cohorts", type=int, default=20) |
| parser.add_argument("--top-per-cohort", type=int, default=2) |
| parser.add_argument("--max-rows", type=int, default=180) |
| return parser.parse_args() |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| args.out.mkdir(parents=True, exist_ok=True) |
| records, summary = build_review_rows(args) |
| write_csv(records, args.out / "risk_review_rows.csv") |
| (args.out / "risk_review_rows.json").write_text( |
| json.dumps(json_safe(records), ensure_ascii=False, indent=2, allow_nan=False), |
| encoding="utf-8", |
| ) |
| (args.out / "summary.json").write_text( |
| json.dumps(summary, ensure_ascii=False, indent=2, allow_nan=False), |
| encoding="utf-8", |
| ) |
| write_html(records, args.out / "OPEN_THIS_V6_QUALITY_RISK_REVIEW.html") |
| print(json.dumps({"out": str(args.out), **summary}, ensure_ascii=False), flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
