# -*- coding: utf-8 -*-
"""
parse_grading_ocr.py - 从 best.pt 检测结果 + OCR 分行文本解析出结构化字段

字段:
  - detected_company: 来自 best.pt label 的前缀(如 BGS-AUTHENTIC -> BGS),多框取最高置信度
  - ocr_score: OCR 识别的评分(如 10, 9.5, 8)
  - score_desc: 评分描述(可选,如 GEM MT/NM-MT/MINT,在分数上方那行)
  - serial_no: 至少 6 个连续数字字符,无则 null
"""
import csv
import json
import re
from pathlib import Path

ROOT = Path(r"D:/顾工交接/wzj/data_all/test/grading_ocr")
OCR_JSON = ROOT / "ocr_result.json"
OCR_CSV = ROOT / "ocr_summary.csv"
import os
# 若目标文件被占用,自动换名输出
OUT_CSV = ROOT / "grading_parsed.csv"
if OUT_CSV.exists():
    # 尝试用 _v2/_v3 之类,直到可用
    for i in range(2, 10):
        cand = ROOT / f"grading_parsed_v{i}.csv"
        if not cand.exists():
            OUT_CSV = cand
            print(f"⚠ grading_parsed.csv 被占用,改用 {OUT_CSV.name}")
            break

# 读 OCR JSON
with open(OCR_JSON, "r", encoding="utf-8") as f:
    ocr_data = json.load(f)

# 读 CSV
rows = []
with open(OCR_CSV, "r", encoding="utf-8-sig") as f:
    for r in csv.DictReader(f):
        rows.append(r)


def detect_company_from_label(box_label):
    """从 best.pt label(如 'BGS-AUTHENTIC','PSA-AUTO','SGC-OLD')提取公司前缀。

    Returns:
        ("PSA", 1) 或 ("BGS", 1) 等有框
        ("", 0) 无框(非评级卡)
    """
    if not box_label or box_label == "(无检测)":
        return "", 0
    return box_label.split("-", 1)[0], 1


# score_desc 关键词 + 对应分数(用于校验,但 score_desc 是 OCR 文本本身,不强制对应)
DESC_TO_SCORE = [
    ("GEM MT", 10),      # PSA: Gem Mint = 10
    ("GEMMINT", 10),
    ("PRISTINE", 10),
    ("GEM", 10),
    ("MINT", 9),         # PSA: Mint = 9
    ("NM-MT", 8.5),
    ("NMMT", 8.5),
    ("NM", 7),           # 近 Mint
    ("EX", 6),
    ("EX+", 6.5),
    ("VG-EX", 4),
    ("VG", 4),
    ("GOOD", 3),
    ("FR", 1),
    ("PR", 1.5),
]


def parse_score_and_desc(lines):
    """从 OCR 分行文本里提取:
        - score: 数字分数(1-10, 或 AU)
        - score_desc: 描述(GEM MT/NM-MT/MINT 等),必须在分数上方一行
    关键改进:
      1. 分数限定范围 1.0-10.0(排除年份/编号)
      2. 描述必须在分数"紧邻的上一行"(不能跨行)
      3. 描述词必须在专门的描述关键词表里(避免"PRIZMW/C"这种卡名误匹配)
    """
    score = None
    score_desc = ""
    score_line_idx = -1

    # 找分数行: 数字必须 1-10 范围内(年份/编号/日期/超大数都排除)
    for i, ln in enumerate(lines):
        ln_clean = ln.strip()
        # 匹配 1-10 范围内的纯数字(支持 .5)
        m = re.match(r"^(\d+(\.\d+)?)$", ln_clean)
        if m:
            val = float(m.group(1))
            if 1.0 <= val <= 10.0:
                score = ln_clean
                score_line_idx = i
                break
        # 单独 "AU" 也算分数(真伪)
        if ln_clean.upper() == "AU":
            score = "AU"
            score_line_idx = i
            break

    # 描述关键词白名单(只在分数"紧邻上一行"找,且必须是这些词)
    DESC_KEYWORDS = ["GEM MT", "GEMMINT", "GEM MINT", "PRISTINE",
                     "MINT", "NM-MT", "NMMT", "NM",
                     "EX", "EX+", "VG-EX", "VG", "GOOD", "PR", "FR",
                     "AUTHENTIC"]  # AUTHENTIC 是签字的真伪描述

    # 找分数紧邻上一行的描述
    if score_line_idx > 0:
        prev_line = lines[score_line_idx - 1].strip().upper()
        # 检查上一行是否纯是描述词
        for kw in DESC_KEYWORDS:
            if kw in prev_line or prev_line == kw.replace(" ", ""):
                score_desc = lines[score_line_idx - 1].strip()
                break

    # 兜底: 如果紧邻上行没找到,扫描所有行找独立的一行(整行就是描述词)
    if not score_desc:
        for ln in lines:
            ln_stripped = ln.strip().upper()
            if ln_stripped in [k.replace(" ", "") for k in DESC_KEYWORDS]:
                score_desc = ln.strip()
                break

    return score or "", score_desc


def parse_serial(lines):
    """找至少 6 个连续数字字符(编号)。"""
    for ln in lines:
        m = re.search(r"\d{6,}", ln)
        if m:
            return m.group(0)
    return None  # 无则 null


# best.pt 的检测结果在 infer_test.py 生成的 test_predictions.json 里
# 我们之前下载过 test_predict/test_predictions.json,但那是 infer_test 的输出
# 这里用 OCR_CSV 里的 filename 作为 key,通过另一份 best.pt 检测数据来获取 label
# 实际: best.pt 的 label 在 grading_ocr.py 第一步裁剪时记录了,但 crop_records.json 里只有 box/conf,没有 label
# 解决方案: 重新跑一遍 best.pt 拿到 label,或从测试结果里取

# 检查有没有 best.pt label 的数据
# 方案: 通过 test_predictions.json (infer_test 生成的) 拿到 detected label
PRED_JSON = Path(r"D:/顾工交接/wzj/data_all/test/test_predict/test_predictions.json")
pred_data = {}
if PRED_JSON.exists():
    with open(PRED_JSON, "r", encoding="utf-8") as f:
        pred_data = json.load(f)
    # pred_data 格式: {"records": [{true, pred, conf, file}, ...]}
    records = pred_data.get("records", [])
    pred_label = {r["file"]: r["pred"] for r in records}
    print(f"✓ 从 test_predictions.json 加载 best.pt 预测结果: {len(pred_label)} 条")
else:
    pred_label = {}
    print("⚠ test_predictions.json 不存在,detected_company 字段将为空")

# 解析
out_rows = []
for r in rows:
    name = r["filename"]
    lines = ocr_data.get(name, {}).get("lines", [])
    box_label = pred_label.get(name, "")

    detected_company, is_graded_int = detect_company_from_label(box_label)
    score, score_desc = parse_score_and_desc(lines)
    serial_no = parse_serial(lines)

    # 评级卡但 OCR 没识别出分数 -> 填 "authentic"(说明卡是真的评分过,但具体分数OCR漏识)
    if is_graded_int == 1 and not score:
        score = "authentic"
    # 非评级卡 -> 一律清空(OCR在原图上抓到的数字是噪音,不是评级分数)
    if is_graded_int == 0:
        score = ""

    out_rows.append({
        "filename": name,
        "true_label": r["true_label"],
        "detected_company": detected_company,       # 来自 best.pt label 前缀
        "is_graded": str(is_graded_int),            # 1=评级卡, 0=非评级卡
        "best_pt_conf": r.get("conf", ""),          # best.pt 置信度
        "ocr_score": score,                          # OCR 识别的分数(无则填 authentic)
        "score_desc": score_desc,                    # 分数描述(校验用)
        "serial_no": serial_no if serial_no else "",  # 编号(无则空)
        "ocr_raw": " | ".join(lines)[:200],
    })

# 按文件名数字值排序(7位数<8位数,而不是字符串比较)
def _name_sort_key(row):
    import re as _re
    nums = _re.findall(r"\d+", row["filename"])
    return (int(nums[0]), row["filename"]) if nums else (9999999999, row["filename"])

out_rows.sort(key=_name_sort_key)

# 写 CSV
with open(OUT_CSV, "w", encoding="utf-8-sig", newline="") as f:
    w = csv.DictWriter(f, fieldnames=[
        "filename", "true_label", "detected_company", "is_graded", "best_pt_conf",
        "ocr_score", "score_desc", "serial_no", "ocr_raw"
    ])
    w.writeheader()
    w.writerows(out_rows)

print(f"\n✓ 解析完成: {OUT_CSV}  共 {len(out_rows)} 条")

# 评级卡统计
n_graded = sum(1 for r in out_rows if r["is_graded"] == "1")
n_ungraded = sum(1 for r in out_rows if r["is_graded"] == "0")
print(f"评级卡: {n_graded}, 非评级卡: {n_ungraded}")

# 公司判断准确率(只算评级卡)
from collections import Counter
graded_rows = [r for r in out_rows if r["is_graded"] == "1"]
correct = sum(1 for r in graded_rows if r["detected_company"] == r["true_label"])
print(f"\n公司判断准确率(仅评级卡 {len(graded_rows)} 张): {correct}/{len(graded_rows)} = {correct/len(graded_rows)*100:.2f}%")
cnt = Counter(r["detected_company"] for r in out_rows)
print("detected_company 分布(全量):")
for k, v in cnt.most_common():
    print(f"  {k or '(空=非评级卡)'}: {v}")

# 分真类别的准确率(仅评级卡)
from collections import defaultdict
acc_by_label = defaultdict(lambda: [0, 0])  # [正确, 总]
for r in graded_rows:
    acc_by_label[r["true_label"]][1] += 1
    if r["detected_company"] == r["true_label"]:
        acc_by_label[r["true_label"]][0] += 1
print("\n分真实类别准确率(仅评级卡):")
for lbl, (c, t) in sorted(acc_by_label.items()):
    print(f"  {lbl}: {c}/{t} = {c/t*100:.2f}%")

# 分数和编号统计(全量 + 评级卡)
n_score = sum(1 for r in out_rows if r["ocr_score"])
n_desc = sum(1 for r in out_rows if r["score_desc"])
n_serial = sum(1 for r in out_rows if r["serial_no"])
print(f"\n全量统计:")
print(f"  OCR 分数识别: {n_score}/{len(out_rows)} = {n_score/len(out_rows)*100:.1f}%")
print(f"  分数描述: {n_desc}/{len(out_rows)} = {n_desc/len(out_rows)*100:.1f}%")
print(f"  编号识别: {n_serial}/{len(out_rows)} = {n_serial/len(out_rows)*100:.1f}%")