# -*- coding: utf-8 -*-
"""
cascade_match_batch.py — 双区级联(DINOv2-large 上半区召回 + 下半区重排)批量匹配。

交易平台图目录 → 每张出 card_id + 卡名/系列 + fusion/upper/lower 相似度 + Top5 候选，
写 CSV，同时写 <out_csv>.topk.json（每张的 Top-N 全部候选含 upper/lower + img_filename
供 HTML 可视化用）。这是把批量交易图匹配从 A4 单区**切换到双区级联**的生产入口，
取代 scripts/match_transactions.py（旧 A4 768 维）。

用法(pytorch 环境):
  python scripts/cascade_match_batch.py --image-dir <目录> --out-csv <path.csv> [--alpha 0.5] [--top-k-recall 30] [--top-n 5]
"""
import os
import sys
import csv
import json
import time
import hashlib
import argparse

sys.stdout.reconfigure(encoding="utf-8")
_THIS = os.path.dirname(os.path.abspath(__file__))
_ROOT = os.path.dirname(_THIS)
sys.path.insert(0, _ROOT)
sys.path.insert(0, _THIS)

import config
from cascade_match import DualCascadeMatcher

FIELDS = ["id", "predicted_card_id", "card_name_ch", "series", "language", "year",
          "master_card_no", "fusion", "upper_sim", "lower_sim", "high_conf", "top5"]


def build_card_id_to_img(card_master_csv):
    """card_master_all.csv → {card_id: (img_url, md5.jpg)}，同 card_id 取首条。"""
    m = {}
    with open(card_master_csv, encoding="utf-8-sig") as f:
        for r in csv.DictReader(f):
            cid = r.get("card_id", "").strip()
            url = r.get("img_url", "").strip()
            if cid and url and cid not in m:
                m[cid] = (url, hashlib.md5(url.encode()).hexdigest() + ".jpg")
    return m


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--image-dir", required=True)
    ap.add_argument("--out-csv", required=True)
    ap.add_argument("--alpha", type=float, default=None)
    ap.add_argument("--top-k-recall", type=int, default=None)
    ap.add_argument("--top-n", type=int, default=5)
    ap.add_argument("--device", default=None)
    ap.add_argument("--backend", choices=["npy", "milvus"], default=None,
                     help="检索后端，默认取 config.CASCADE_BACKEND(=npy)，不传则跟现状完全一样")
    args = ap.parse_args()

    config.ensure_dirs()
    matcher = DualCascadeMatcher(alpha=args.alpha, top_k_recall=args.top_k_recall,
                                  device=args.device, backend=args.backend)
    id2meta = {m["card_id"]: m for m in matcher.metas}
    print(f"[batch] 读取 card_master_all.csv 建 card_id→img 映射 ...", flush=True)
    id2img = build_card_id_to_img(config.CARD_MASTER_ALL_CSV)
    print(f"[batch] card_master 覆盖 {len(id2img)} 个 card_id", flush=True)

    files = sorted(f for f in os.listdir(args.image_dir)
                   if f.lower().endswith((".jpg", ".jpeg", ".png", ".webp")))
    print(f"[batch] {len(files)} 张待匹配  α={matcher.alpha}  K_recall={matcher.top_k_recall}  top_n={args.top_n}", flush=True)
    thr = config.SIMILARITY_THRESHOLD

    rows = []
    topk_dump = {}
    n_ok = n_fail = n_high = 0
    t0 = time.time()
    for i, fn in enumerate(files, 1):
        stem = os.path.splitext(fn)[0]
        path = os.path.join(args.image_dir, fn)
        raw = matcher.query_raw(path)
        if raw is None:
            rows.append({k: "" for k in FIELDS} | {"id": stem, "predicted_card_id": "FAIL"})
            topk_dump[stem] = []
            n_fail += 1
            continue
        ranked = matcher.rank(raw, matcher.alpha)  # [(card_id, fusion, u, l), ...]
        top1 = ranked[0]
        meta = id2meta.get(top1[0], {})
        high = top1[1] >= thr
        n_high += int(high); n_ok += 1

        # 组装 topk.json 每条
        top_list = []
        for cid, fu, u, l in ranked[:args.top_n]:
            m = id2meta.get(cid, {})
            url, img_fn = id2img.get(cid, ("", ""))
            top_list.append({
                "card_id": cid, "fusion": round(float(fu), 4),
                "upper": round(float(u), 4), "lower": round(float(l), 4),
                "card_name_ch": m.get("card_name_ch", ""),
                "series": m.get("pg_label", ""),
                "language": m.get("language", ""),
                "year": m.get("year", ""),
                "master_card_no": m.get("card_no", ""),
                "img_filename": img_fn,
            })
        topk_dump[stem] = top_list

        rows.append({
            "id": stem,
            "predicted_card_id": top1[0],
            "card_name_ch": meta.get("card_name_ch", ""),
            "series": meta.get("pg_label", ""),
            "language": meta.get("language", ""),
            "year": meta.get("year", ""),
            "master_card_no": meta.get("card_no", ""),
            "fusion": f"{top1[1]:.4f}",
            "upper_sim": f"{top1[2]:.4f}",
            "lower_sim": f"{top1[3]:.4f}",
            "high_conf": "1" if high else "0",
            "top5": " | ".join(f"{t['card_id']}:{t['fusion']:.3f}" for t in top_list),
        })
        if i % 50 == 0 or i == len(files):
            print(f"  [{i}/{len(files)}] ok={n_ok} fail={n_fail} 高置信={n_high} "
                  f"({(time.time()-t0)/i:.2f}s/张)", flush=True)

    with open(args.out_csv, "w", encoding="utf-8-sig", newline="") as f:
        w = csv.DictWriter(f, fieldnames=FIELDS)
        w.writeheader()
        w.writerows(rows)
    topk_path = args.out_csv.rsplit(".", 1)[0] + ".topk.json"
    with open(topk_path, "w", encoding="utf-8") as f:
        json.dump(topk_dump, f, ensure_ascii=False, indent=2)
    print(f"\n[batch] 写出 {args.out_csv}", flush=True)
    print(f"[batch] 写出 {topk_path}", flush=True)
    print(f"[batch] 总 {len(files)}  成功 {n_ok}  失败 {n_fail}  "
          f"高置信(fusion≥{thr}) {n_high} ({n_high/max(1,len(files))*100:.1f}%)", flush=True)


if __name__ == "__main__":
    main()