| """ |
| ================================================================================ |
| 脚本名称 (Script Name): split_octbuckets_octa_review.py |
| 原本用途 (Original Purpose): |
| 启发式多特征混杂 OCT 模态拆分与人工复审流水线。提取纵横比、边缘密度、层状结构评分（Layer score）与血管流信号评分（Vessel score），将打包混杂的 OCT 数据精准解耦分离为 B-scan、OCTA en-face 与 SLO 样图像。 |
| 适用数据集 (Target Dataset): |
| archives/data0410/ 中的 octBuckets.zip（34,519 张多模态混合 OCT 图像）。 |
| 作者与归属 (Author/Provenance): |
| 李思成 (Lisicheng), 浙江大学多模态眼科团队 |
| 输入要求 (Input): |
| 包含混杂扫描模式的 octBuckets 图像集 |
| 输出结果 (Output): |
| 按真实模态分类的 CSV 清单（oct_bscan, octa_enface, oct_enface_or_slo_like）及单张复审 HTML |
| 依赖环境 (Dependencies): |
| numpy, pandas, pillow, tarfile |
| ================================================================================ |
| """ |
|
|
| """Split the mixed octbuckets_octa cohort into reviewable OCT subtypes. |
| |
| This script is read-only with respect to source images and training manifests. |
| It produces: |
| - auto_labels.csv: one row per image with heuristic subtype and features |
| - review_candidates.csv: non-B-scan, uncertain, and sampled B-scan rows |
| - OPEN_THIS_OCTBUCKETS_OCTA_REVIEW.html: single-image manual review page |
| - thumbs/: local thumbnails referenced by the HTML |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import html |
| import io |
| import json |
| import random |
| import re |
| import tarfile |
| from dataclasses import asdict, dataclass |
| from pathlib import Path |
| from typing import Any |
|
|
| import numpy as np |
| import pandas as pd |
| from PIL import Image |
|
|
|
|
| DEFAULT_MANIFEST = Path("/data/team/lisicheng/new_data_manifest/prompt_adapter_mixed_v6/adapter_manifest_mixed_v6.parquet") |
| DEFAULT_OUT = Path("/data/team/lisicheng/Code/OCTFlow/pilot/path1/audit/octbuckets_octa_split_20260708") |
|
|
| LABELS = ["oct_bscan", "octa_enface", "oct_enface_or_slo_like", "unknown"] |
|
|
|
|
| @dataclass(frozen=True) |
| class Classification: |
| label: str |
| confidence: float |
| reason: str |
| aspect_ratio: float |
| edge_density: float |
| horizontal_edge_ratio: float |
| vertical_edge_ratio: float |
| dark_fraction: float |
| bright_fraction: float |
| layer_score: float |
| vessel_score: float |
| enface_score: float |
|
|
|
|
| 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 _to_gray_resized(arr: np.ndarray, size: int = 192) -> tuple[np.ndarray, float]: |
| if arr.ndim == 3: |
| gray = np.dot(arr[..., :3], [0.299, 0.587, 0.114]) |
| else: |
| gray = arr.astype(np.float32) |
| h, w = gray.shape[:2] |
| aspect = max(w / max(h, 1), h / max(w, 1)) |
| im = Image.fromarray(np.clip(gray, 0, 255).astype(np.uint8)).resize((size, size), Image.Resampling.BILINEAR) |
| out = np.asarray(im, dtype=np.float32) / 255.0 |
| p1, p99 = np.percentile(out, [1, 99]) |
| if p99 > p1 + 0.05: |
| out = np.clip((out - p1) / (p99 - p1), 0, 1) |
| return out, float(aspect) |
|
|
|
|
| def classify_array(arr: np.ndarray) -> Classification: |
| gray, aspect = _to_gray_resized(arr) |
| gy, gx = np.gradient(gray) |
| agx = np.abs(gx) |
| agy = np.abs(gy) |
| mag = agx + agy |
| edge_threshold = max(float(np.quantile(mag, 0.86)), 0.035) |
| edges = mag > edge_threshold |
| edge_count = int(edges.sum()) |
| total = int(edges.size) |
| edge_density = edge_count / max(total, 1) |
| if edge_count: |
| horizontal = ((agy > agx * 1.20) & edges).sum() / edge_count |
| vertical = ((agx > agy * 1.20) & edges).sum() / edge_count |
| else: |
| horizontal = 0.0 |
| vertical = 0.0 |
|
|
| dark_fraction = float((gray < 0.12).mean()) |
| bright_fraction = float((gray > 0.78).mean()) |
| row_profile = gray.mean(axis=1) |
| col_profile = gray.mean(axis=0) |
| row_variation = float(row_profile.std()) |
| col_variation = float(col_profile.std()) |
| anisotropy = abs(horizontal - vertical) |
|
|
| layer_score = ( |
| 0.52 * horizontal |
| + 0.23 * min(row_variation / 0.18, 1.0) |
| + 0.15 * min(dark_fraction / 0.45, 1.0) |
| + 0.10 * min(aspect / 2.0, 1.0) |
| ) |
| vessel_score = ( |
| 0.42 * min(edge_density / 0.18, 1.0) |
| + 0.30 * min(bright_fraction / 0.14, 1.0) |
| + 0.18 * max(0.0, 1.0 - anisotropy / 0.55) |
| + 0.10 * min((row_variation + col_variation) / 0.28, 1.0) |
| ) |
| enface_score = ( |
| 0.40 * max(0.0, 1.0 - min(aspect - 1.0, 1.0)) |
| + 0.25 * max(0.0, 1.0 - min(layer_score / 0.72, 1.0)) |
| + 0.20 * min(edge_density / 0.12, 1.0) |
| + 0.15 * min(float(gray.std()) / 0.24, 1.0) |
| ) |
|
|
| if layer_score >= 0.54 and horizontal >= 0.42 and (dark_fraction >= 0.18 or aspect >= 1.35): |
| label = "oct_bscan" |
| confidence = min(0.98, 0.62 + (layer_score - 0.54) * 1.25 + max(0.0, horizontal - 0.42)) |
| reason = "layer-dominant horizontal OCT cross-section" |
| elif vessel_score >= 0.56 and layer_score < 0.58 and edge_density >= 0.08: |
| label = "octa_enface" |
| confidence = min(0.95, 0.55 + (vessel_score - 0.56) * 1.20 + max(0.0, edge_density - 0.08)) |
| reason = "dense vessel-like en-face network" |
| elif enface_score >= 0.56 and layer_score < 0.62 and aspect <= 1.45: |
| label = "oct_enface_or_slo_like" |
| confidence = min(0.90, 0.52 + (enface_score - 0.56) * 1.10) |
| reason = "fundus-like gray en-face projection without dominant layers" |
| else: |
| label = "unknown" |
| confidence = max(0.30, min(0.58, max(layer_score, vessel_score, enface_score))) |
| reason = "ambiguous OCT/OCTA appearance" |
|
|
| return Classification( |
| label=label, |
| confidence=round(float(confidence), 4), |
| reason=reason, |
| aspect_ratio=round(float(aspect), 4), |
| edge_density=round(float(edge_density), 4), |
| horizontal_edge_ratio=round(float(horizontal), 4), |
| vertical_edge_ratio=round(float(vertical), 4), |
| dark_fraction=round(float(dark_fraction), 4), |
| bright_fraction=round(float(bright_fraction), 4), |
| layer_score=round(float(layer_score), 4), |
| vessel_score=round(float(vessel_score), 4), |
| enface_score=round(float(enface_score), 4), |
| ) |
|
|
|
|
| def choose_review_rows( |
| rows: list[dict[str, Any]], |
| bscan_sample: int, |
| uncertain_threshold: float, |
| seed: int, |
| ) -> list[dict[str, Any]]: |
| selected: list[dict[str, Any]] = [] |
| bscan_pool: list[dict[str, Any]] = [] |
| seen: set[str] = set() |
| for row in rows: |
| label = str(row["auto_subtype"]) |
| conf = float(row["confidence"]) |
| image_id = str(row["image_id"]) |
| needs_review = label != "oct_bscan" or conf < uncertain_threshold |
| if needs_review: |
| selected.append(row) |
| seen.add(image_id) |
| elif bscan_sample > 0: |
| bscan_pool.append(row) |
| rng = random.Random(seed) |
| rng.shuffle(bscan_pool) |
| for row in bscan_pool[:bscan_sample]: |
| image_id = str(row["image_id"]) |
| if image_id not in seen: |
| selected.append(row) |
| seen.add(image_id) |
| return selected |
|
|
|
|
| def save_thumb(im: Image.Image, out: Path, size: int) -> None: |
| thumb = im.copy() |
| thumb.thumbnail((size, size), Image.Resampling.LANCZOS) |
| canvas = Image.new("RGB", (size, size), (245, 245, 245)) |
| canvas.paste(thumb, ((size - thumb.width) // 2, (size - thumb.height) // 2)) |
| canvas.save(out, quality=90) |
|
|
|
|
| def safe_name(value: str) -> str: |
| return re.sub(r"[^A-Za-z0-9_.-]+", "_", value)[:160] |
|
|
|
|
| def build_html(records: list[dict[str, Any]], out: Path, title: str) -> None: |
| rows_json = json.dumps(records, ensure_ascii=False).replace("</", "<\\/") |
| labels_json = json.dumps(LABELS, ensure_ascii=False) |
| css = """:root{--bg:#f4f6f8;--panel:#fff;--line:#d8dee8;--text:#1f2937;--muted:#64748b;--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}.muted{color:var(--muted);font-size:13px}.progress{font-size:13px;min-width:260px}.viewer{display:grid;grid-template-columns:minmax(420px,52vw) 1fr;gap:14px;padding:14px}.panel{background:var(--panel);border:1px solid var(--line);border-radius:6px;padding:12px}.imgbox{height:min(70vh,720px);min-height:420px;background:#080808;display:flex;align-items:center;justify-content:center;overflow:hidden;border:1px solid #111}.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}.ok{background:#e8f5e9;color:#1b5e20}.warn{background:#fff7e6;color:#8a4b00}.bad{background:#ffecec;color:#9b1c1c}.meta{font-size:13px;line-height:1.5;word-break:break-word}.features{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:4px 12px;margin-top:8px;font-size:12px;color:#334155}.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}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.good{background:#e9f7ef;color:var(--green);border-color:#a8ddb9}button.drop{background:#fff1f0;color:var(--red);border-color:#ffccc7}textarea{width:100%;height:78px;resize:vertical}.path{font-family:monospace;font-size:12px;color:#334155}.hint{margin-top:6px;font-size:12px;color:var(--muted)}@media(max-width:1000px){.viewer{grid-template-columns:1fr}.imgbox{height:520px}}""" |
| js = f""" |
| const rows = JSON.parse(document.getElementById('rows-data').textContent); |
| const labels = {labels_json}; |
| const storageKey = 'octbuckets_octa_split_review_20260708'; |
| let idx = Number(localStorage.getItem(storageKey + ':idx') || 0); |
| let edits = JSON.parse(localStorage.getItem(storageKey) || '{{}}'); |
| function esc(v) {{ return String(v ?? '').replace(/[&<>"']/g, c => ({{'&':'&','<':'<','>':'>','"':'"',"'":'''}}[c])); }} |
| function edit(id) {{ return edits[id] || {{status:'unreviewed', final_subtype:'', note:''}}; }} |
| function save() {{ localStorage.setItem(storageKey, JSON.stringify(edits)); localStorage.setItem(storageKey + ':idx', String(idx)); }} |
| function counts() {{ const c={{unreviewed:0,reviewed: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.isNaN(n)) {{ idx=Math.max(0,Math.min(rows.length-1,n-1)); save(); render(); }} }} |
| function setLabel(label, advance=true) {{ const r=rows[idx]; const e=edit(r.image_id); edits[r.image_id]={{...e,status:'reviewed',final_subtype:label}}; save(); if (advance) go(1); else render(); }} |
| function dropRow() {{ const r=rows[idx]; const e=edit(r.image_id); edits[r.image_id]={{...e,status:'drop',final_subtype:'exclude'}}; save(); go(1); }} |
| function updateNote(v) {{ const r=rows[idx]; const e=edit(r.image_id); edits[r.image_id]={{...e,note:v}}; save(); }} |
| function mergedRows() {{ return rows.map(r=>{{ const e=edit(r.image_id); return {{...r,status:e.status||'unreviewed',final_subtype:e.final_subtype||r.auto_subtype,note:e.note||''}}; }}); }} |
| 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=['image_id','cohort','current_subtype','auto_subtype','confidence','status','final_subtype','note','reason','file_path']; const csv=[cols.join(',')].concat(out.map(o=>cols.map(c=>'"'+String(o[c]??'').replaceAll('"','""')+'"').join(','))).join('\\n'); download('octbuckets_octa_split_review_edits.csv', csv, 'text/csv'); }} |
| function exportJSON() {{ download('octbuckets_octa_split_review_edits.json', JSON.stringify(mergedRows(), null, 2), 'application/json'); }} |
| function labelSelect(current) {{ return '<select onchange="setLabel(this.value,false)">' + labels.concat(['exclude']).map(x=>`<option value="${{esc(x)}}" ${{x===current?'selected':''}}>${{esc(x)}}</option>`).join('') + '</select>'; }} |
| function badgeClass(r) {{ if (r.auto_subtype === 'oct_bscan' && Number(r.confidence) >= 0.65) return 'ok'; if (r.auto_subtype === 'unknown' || Number(r.confidence) < 0.60) return 'warn'; return 'bad'; }} |
| 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]; const e=edit(r.image_id); const final=e.final_subtype || r.auto_subtype; const c=counts(); |
| document.getElementById('progress').textContent=`第 ${{idx+1}} / ${{rows.length}} 张；已看 ${{c.reviewed||0}}，排除 ${{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_rel)}}" loading="eager"></div></div><div class="panel"><div><span class="badge ${{badgeClass(r)}}">自动：${{esc(r.auto_subtype)}} (${{Number(r.confidence).toFixed(2)}})</span><span class="badge">当前：${{esc(r.current_subtype)}}</span><span class="badge">最终：${{esc(final)}}</span></div><div class="meta"><b>图像 ID</b>：${{esc(r.image_id)}}<br><b>cohort</b>：${{esc(r.cohort)}}<br><b>理由</b>：${{esc(r.reason)}}<br><b>路径</b>：<span class="path">${{esc(r.file_path)}}</span></div><div class="features"><div>aspect: ${{esc(r.aspect_ratio)}}</div><div>edge_density: ${{esc(r.edge_density)}}</div><div>horizontal: ${{esc(r.horizontal_edge_ratio)}}</div><div>vertical: ${{esc(r.vertical_edge_ratio)}}</div><div>dark: ${{esc(r.dark_fraction)}}</div><div>bright: ${{esc(r.bright_fraction)}}</div><div>layer_score: ${{esc(r.layer_score)}}</div><div>vessel_score: ${{esc(r.vessel_score)}}</div><div>enface_score: ${{esc(r.enface_score)}}</div></div><div class="review"><div class="row"><button class="good" onclick="setLabel('oct_bscan')">1 B-scan</button><button class="good" onclick="setLabel('octa_enface')">2 OCTA en face</button><button class="good" onclick="setLabel('oct_enface_or_slo_like')">3 en face/SLO-like</button><button class="drop" onclick="dropRow()">4 unknown/exclude</button><button onclick="setLabel('${{esc(final)}}', false)">只保存</button></div><div class="row"><label>最终标签：${{labelSelect(final)}}</label></div><textarea placeholder="备注" oninput="updateNote(this.value)">${{esc(e.note || '')}}</textarea></div><div class="hint">快捷键：1/2/3/4 标注并下一张，←/→ 上一张/下一张，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') setLabel('oct_bscan'); |
| else if (ev.key === '2') setLabel('octa_enface'); |
| else if (ev.key === '3') setLabel('oct_enface_or_slo_like'); |
| else if (ev.key === '4') dropRow(); |
| 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>{html.escape(title)}</title><style>{css}</style></head> |
| <body><header><h1>{html.escape(title)}</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">只审核自动非 B-scan、低置信度样本，以及抽样 B-scan。源图和 manifest 不会被修改。</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: |
| p = argparse.ArgumentParser(description=__doc__) |
| p.add_argument("--manifest", type=Path, default=DEFAULT_MANIFEST) |
| p.add_argument("--out", type=Path, default=DEFAULT_OUT) |
| p.add_argument("--cohort", default="octbuckets_octa") |
| p.add_argument("--limit", type=int, default=0, help="Debug limit; 0 means all rows.") |
| p.add_argument("--seed", type=int, default=20260708) |
| p.add_argument("--thumb-size", type=int, default=512) |
| p.add_argument("--review-bscan-sample", type=int, default=300) |
| p.add_argument("--uncertain-threshold", type=float, default=0.65) |
| return p.parse_args() |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| args.out.mkdir(parents=True, exist_ok=True) |
| thumb_dir = args.out / "thumbs" |
| thumb_dir.mkdir(parents=True, exist_ok=True) |
|
|
| cols = ["image_id", "cohort", "modality", "modality_subtype", "file_path"] |
| rows = pd.read_parquet(args.manifest, columns=cols) |
| rows = rows[rows["cohort"].astype(str).eq(args.cohort)].copy() |
| rows = rows.sort_values(["file_path", "image_id"]).reset_index(drop=True) |
| if args.limit: |
| rows = rows.head(args.limit).copy() |
| print(f"[load] {len(rows)} rows from {args.manifest} cohort={args.cohort}", flush=True) |
|
|
| opener = ImageOpener() |
| records: list[dict[str, Any]] = [] |
| try: |
| for i, row in enumerate(rows.itertuples(index=False), start=1): |
| try: |
| im = opener.open(str(row.file_path)) |
| arr = np.asarray(im) |
| result = classify_array(arr) |
| rec = { |
| "image_id": str(row.image_id), |
| "cohort": str(row.cohort), |
| "modality": str(row.modality), |
| "current_subtype": str(row.modality_subtype), |
| "file_path": str(row.file_path), |
| "auto_subtype": result.label, |
| **asdict(result), |
| "open_error": "", |
| } |
| except Exception as exc: |
| rec = { |
| "image_id": str(row.image_id), |
| "cohort": str(row.cohort), |
| "modality": str(row.modality), |
| "current_subtype": str(row.modality_subtype), |
| "file_path": str(row.file_path), |
| "auto_subtype": "unknown", |
| "label": "unknown", |
| "confidence": 0.0, |
| "reason": "open/classify error", |
| "open_error": repr(exc), |
| } |
| records.append(rec) |
| if i % 1000 == 0 or i == len(rows): |
| print(f"[classify] {i}/{len(rows)}", flush=True) |
| finally: |
| opener.close() |
|
|
| auto = pd.DataFrame(records) |
| auto_path = args.out / "auto_labels.csv" |
| auto.to_csv(auto_path, index=False) |
| summary = { |
| "rows": len(auto), |
| "auto_subtype_counts": auto["auto_subtype"].value_counts(dropna=False).to_dict(), |
| "open_errors": int((auto.get("open_error", "") != "").sum()) if "open_error" in auto else 0, |
| "uncertain_threshold": args.uncertain_threshold, |
| "review_bscan_sample": args.review_bscan_sample, |
| } |
| (args.out / "summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") |
| print("[summary]", json.dumps(summary, ensure_ascii=False), flush=True) |
|
|
| review = choose_review_rows(records, args.review_bscan_sample, args.uncertain_threshold, args.seed) |
| opener = ImageOpener() |
| try: |
| for i, rec in enumerate(review, start=1): |
| thumb_name = f"{i:05d}_{safe_name(rec['image_id'])}.jpg" |
| thumb_path = thumb_dir / thumb_name |
| rec["thumb_rel"] = f"thumbs/{thumb_name}" |
| if not thumb_path.exists(): |
| try: |
| save_thumb(opener.open(str(rec["file_path"])), thumb_path, args.thumb_size) |
| except Exception as exc: |
| rec["thumb_rel"] = "" |
| rec["open_error"] = repr(exc) |
| if i % 500 == 0 or i == len(review): |
| print(f"[thumbs] {i}/{len(review)}", flush=True) |
| finally: |
| opener.close() |
|
|
| review_path = args.out / "review_candidates.csv" |
| pd.DataFrame(review).to_csv(review_path, index=False) |
| html_path = args.out / "OPEN_THIS_OCTBUCKETS_OCTA_REVIEW.html" |
| build_html(review, html_path, "octbuckets_octa 自动拆分人工审核") |
| print(json.dumps({"out": str(args.out), "auto_labels": str(auto_path), "review_rows": len(review), "html": str(html_path)}, ensure_ascii=False), flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
