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@@ -0,0 +1,971 @@
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+#!/usr/bin/env python3
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+"""
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+单元格裁剪图预处理参数扫描：去水印 / contrast（clahe/gamma/linear/text_restore）/ upscale / det 阈值 / OCR 模式。
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+
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+支持 contrast 在放大前/后执行两种顺序对比。
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+
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+默认从 **原图**（`*_raw.png`）出发，与 pipeline 二次 OCR 一致，避免对已预处理 debug 图二次去水印。
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+
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+用法:
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+ python cell_sweep.py cell219_empty_empty_raw.png -o ./out -t "ATM存折取款"
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+ python cell_sweep.py /path/to/tablecell_ocr/ -o ./out
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+ python cell_sweep.py cell.png --quick --no-save-images
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+ python cell_sweep.py cell.png --contrast-orders before_upscale,after_upscale
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+ OCR_DET_MODEL_PATH=... OCR_REC_MODEL_PATH=... python cell_sweep.py cell.png
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+
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+
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+ # 统计出的最优参数 tag: threshold_t150_cl_1.0_8_ob_u128_det0.5
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+ # 对目录下所有 *_raw.png 验证适配性
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+ python cell_sweep.py /path/to/tablecell_ocr/ -o ./out --best-only
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+
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+ # 自定义最优参数
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+ python cell_sweep.py /path/to/tablecell_ocr/ -o ./out --best-only \
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+ --best-config threshold_t150_cl_1.0_8_ob_u128_det0.5
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+
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+ # 指定目标文字，自动统计 HIT 命中率
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+ python cell_sweep.py /path/to/tablecell_ocr/ -o ./out --best-only -t "交易类型"
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+"""
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+from __future__ import annotations
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+
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+import argparse
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+import json
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+import os
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+import sys
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+from itertools import product
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+from pathlib import Path
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+from typing import Any, Dict, List, Optional, Sequence, Tuple
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+
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+import cv2
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+import numpy as np
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+
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+_repo_root = Path(__file__).resolve().parents[3]
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+if str(_repo_root) not in sys.path:
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+ sys.path.insert(0, str(_repo_root))
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+
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+from ocr_utils.watermark import WatermarkProcessor, merge_watermark_config
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+from ocr_utils.watermark.contrast import enhance_document_contrast
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+
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+_IMAGE_SUFFIXES = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff", ".webp"}
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+_DEFAULT_MODEL_DIR = Path(
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+ "/Users/zhch158/models/modelscope_cache/models/OpenDataLab/"
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+ "PDF-Extract-Kit-1___0/models/OCR/paddleocr_torch"
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+)
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+
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+
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+def _parse_csv_ints(s: str) -> List[Optional[int]]:
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+ out: List[Optional[int]] = []
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+ for part in s.split(","):
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+ part = part.strip()
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+ if not part or part.lower() in ("none", "d", "default"):
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+ out.append(None)
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+ else:
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+ out.append(int(part))
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+ return out
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+
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+
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+def _parse_csv_floats(s: str) -> List[float]:
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+ return [float(x.strip()) for x in s.split(",") if x.strip()]
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+
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+
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+def _parse_csv_bools(s: str) -> List[bool]:
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+ out: List[bool] = []
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+ for part in s.split(","):
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+ p = part.strip().lower()
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+ if p in ("1", "true", "yes", "on"):
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+ out.append(True)
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+ elif p in ("0", "false", "no", "off"):
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+ out.append(False)
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+ else:
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+ raise ValueError(f"无效的 bool 值: {part!r}")
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+ return out
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+
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+
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+def _default_model_dir() -> Path:
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+ det = os.environ.get("OCR_DET_MODEL_PATH")
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+ if det:
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+ return Path(det).parent
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+ return _DEFAULT_MODEL_DIR
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+
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+
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+def _upscale(img: np.ndarray, min_side: int) -> np.ndarray:
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+ h, w = img.shape[:2]
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+ if h >= min_side and w >= min_side:
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+ return img
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+ s = max(min_side / max(h, 1), min_side / max(w, 1), 1.0)
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+ return cv2.resize(img, None, fx=s, fy=s, interpolation=cv2.INTER_CUBIC)
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+
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+
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+# ── 对比度增强方法（clahe / gamma / linear / text_restore / none）──
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+
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+
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+def _apply_contrast(
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+ gray: np.ndarray,
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+ *,
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+ method: str,
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+ clip_limit: float = 1.0,
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+ tile_grid_size: int = 8,
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+ gamma: float = 0.85,
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+ black_percentile: float = 2.0,
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+ white_percentile: float = 98.0,
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+ text_black_target: int = 85,
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+ background_threshold: int = 248,
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+) -> np.ndarray:
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+ """对灰度图应用对比度增强；method="none" 时原样返回。"""
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+ if method == "none":
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+ return gray
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+ if method == "text_restore":
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+ return enhance_document_contrast(
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+ gray, method="text_restore",
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+ text_black_target=text_black_target,
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+ background_threshold=background_threshold,
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+ )
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+ if method == "clahe":
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+ return enhance_document_contrast(
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+ gray, method="clahe",
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+ clip_limit=clip_limit, tile_grid_size=tile_grid_size,
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+ )
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+ if method == "gamma":
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+ return enhance_document_contrast(gray, method="gamma", gamma=gamma)
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+ if method == "linear":
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+ return enhance_document_contrast(
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+ gray, method="linear",
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+ black_percentile=black_percentile,
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+ white_percentile=white_percentile,
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+ )
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+ return gray
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+
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+
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+def _contrast_tag(cfg: Dict[str, Any]) -> str:
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+ """生成 contrast 配置的短标签。"""
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+ m = cfg.get("method", "none")
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+ if m == "none":
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+ return "c0"
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+ if m == "text_restore":
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+ return f"tr_{cfg.get('text_black_target', 85)}"
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+ if m == "clahe":
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+ return f"cl_{cfg.get('clip_limit', 1.0)}_{cfg.get('tile_grid_size', 8)}"
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+ if m == "gamma":
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+ return f"gm_{cfg.get('gamma', 0.85)}"
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+ if m == "linear":
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+ return f"ln_{cfg.get('black_percentile', 2.0)}_{cfg.get('white_percentile', 98.0)}"
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+ return m
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+
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+
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+def _build_contrast_grid(quick: bool = False) -> List[Dict[str, Any]]:
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+ """构建 contrast 参数网格（对齐 contrast_sweep.py 的设计）。
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+
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+ 返回列表，每个元素是一个 Dict，至少包含 "method" 字段。
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+ """
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+ grid: List[Dict[str, Any]] = [{"method": "none"}] # 对照组：不增强
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+
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+ # text_restore
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+ if quick:
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+ tbt = [60, 85]
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+ bts = [240, 248]
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+ else:
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+ tbt = [60, 85, 100, 120]
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+ bts = [240, 248, 252]
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+ for target, bg_th in product(tbt, bts):
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+ grid.append({"method": "text_restore", "text_black_target": target, "background_threshold": bg_th})
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+
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+ # clahe
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+ if quick:
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+ cl = [1.0, 2.0]
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+ ts = [4, 8]
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+ else:
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+ cl = [0.5, 1.0, 2.0, 3.0, 5.0]
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+ ts = [4, 8]
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+ for clip, tile in product(cl, ts):
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+ grid.append({"method": "clahe", "clip_limit": clip, "tile_grid_size": tile})
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+
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+ # # gamma
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+ # if quick:
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+ # gvs = [0.5, 0.85]
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+ # else:
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+ # gvs = [0.4, 0.55, 0.7, 0.85]
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+ # for g in gvs:
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+ # grid.append({"method": "gamma", "gamma": g})
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+
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+ # # linear
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+ # if quick:
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+ # bps = [2.0, 5.0]
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+ # wps = [95.0, 98.0]
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+ # else:
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+ # bps = [2.0, 5.0, 8.0]
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+ # wps = [95.0, 98.0]
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+ # for bp, wp in product(bps, wps):
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+ # grid.append({"method": "linear", "black_percentile": bp, "white_percentile": wp})
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+
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+ return grid
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+
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+
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+def _preprocess(
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+ raw: np.ndarray,
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+ *,
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+ method: str,
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+ thresh: Optional[int],
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+ contrast_cfg: Dict[str, Any],
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+ upscale: int,
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+ contrast_order: str = "before_upscale",
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+) -> np.ndarray:
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+ """预处理管线：去水印 → [contrast] → 放大（或去水印 → 放大 → contrast）。
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+
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+ method="none" 时跳过去水印，直接从原图开始处理。
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+ """
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+ if method == "none":
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+ img = raw.copy() # 不处理水印，直接使用原图
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+ else:
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+ user: Dict[str, Any] = {"enabled": True, "method": method}
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+ if method == "threshold" and thresh is not None:
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+ user["threshold"] = thresh
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+ cfg = merge_watermark_config("cell", user)
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+ img, _ = WatermarkProcessor(cfg, scope="cell").process(raw, force=True)
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+
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+ contrast_method = contrast_cfg.get("method", "none")
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+ if contrast_method != "none" and contrast_order == "before_upscale":
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+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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+ gray = _apply_contrast(gray, **contrast_cfg)
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+ img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
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+
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+ img = _upscale(img, upscale)
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+
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+ if contrast_method != "none" and contrast_order == "after_upscale":
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+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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+ gray = _apply_contrast(gray, **contrast_cfg)
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+ img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
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+
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+ return img
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+
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+
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+def _parse_rec_pair(rec_part: Any) -> Tuple[str, float]:
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+ """从 OCR 返回的 (text, score) 或嵌套结构中解析识别结果。"""
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+ if rec_part is None:
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+ return "", 0.0
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+ if isinstance(rec_part, (list, tuple)) and len(rec_part) >= 2:
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+ if isinstance(rec_part[0], (list, tuple, dict)):
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+ return "", 0.0
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+ txt = str(rec_part[0] or "").strip()
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+ try:
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+ sc = float(rec_part[1] or 0.0)
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+ except (TypeError, ValueError):
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+ sc = 0.0
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+ return txt, sc if txt else 0.0
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+ if isinstance(rec_part, (list, tuple)) and len(rec_part) == 1:
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+ txt = str(rec_part[0] or "").strip()
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+ return txt, 0.0
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+ return "", 0.0
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+
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+
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+def _aggregate_rec_score(boxes: List[Dict[str, Any]]) -> float:
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+ """按字符数加权平均识别分（与 pipeline aggregate_line_ocr 一致）。"""
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+ total_len = sum(len(b.get("text") or "") for b in boxes)
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+ if total_len <= 0:
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+ return 0.0
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+ weighted = sum(
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+ len(b.get("text") or "") * float(b.get("score") or 0.0) for b in boxes
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+ )
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+ return weighted / total_len
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+
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+
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+def _ocr(engine: Any, img: np.ndarray, *, det: bool, rec: bool) -> Dict[str, Any]:
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+ empty: Dict[str, Any] = {
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+ "text": "",
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+ "score": 0.0,
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+ "boxes": [],
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+ "det": det,
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+ "rec": rec,
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+ "n_boxes": 0,
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+ }
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+ try:
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+ res = engine.ocr(img, det=det, rec=rec)
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+ items = res[0] if res and res[0] is not None else []
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+ boxes_out: List[Dict[str, Any]] = []
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+
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+ if det:
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+ for item in items:
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+ if not item or len(item) < 2:
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+ continue
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+ text, score = _parse_rec_pair(item[1])
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+ bbox = item[0]
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+ if hasattr(bbox, "tolist"):
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+ bbox = bbox.tolist()
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+ entry: Dict[str, Any] = {
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+ "text": text,
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+ "score": round(score, 6),
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+ }
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+ if bbox is not None:
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+ entry["det_bbox"] = bbox
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+ boxes_out.append(entry)
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+ else:
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+ for item in items:
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+ text, score = _parse_rec_pair(item)
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+ if not text and isinstance(item, (list, tuple)) and len(item) >= 1:
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+ text, score = _parse_rec_pair(item[0])
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+ boxes_out.append({"text": text, "score": round(score, 6)})
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+
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+ text = "".join(b["text"] for b in boxes_out if b.get("text")).strip()
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+ agg_score = _aggregate_rec_score(boxes_out)
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+ return {
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+ "text": text,
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+ "score": round(agg_score, 6),
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+ "boxes": boxes_out,
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+ "det": det,
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+ "rec": rec,
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+ "n_boxes": len(boxes_out),
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+ }
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+ except Exception as e:
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+ out = dict(empty)
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+ out["error"] = str(e)
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+ return out
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+
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+
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+def _make_engine(det_thresh: float, model_dir: Path) -> Any:
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+ from ocr_tools.pytorch_models.pytorch_paddle import PytorchPaddleOCR
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+
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+ det_path = os.environ.get("OCR_DET_MODEL_PATH") or str(
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+ model_dir / "ch_PP-OCRv5_det_infer.pth"
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+ )
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+ rec_path = os.environ.get("OCR_REC_MODEL_PATH") or str(
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+ model_dir / "ch_PP-OCRv4_rec_server_doc_infer.pth"
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+ )
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+ return PytorchPaddleOCR(
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+ lang="ch",
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+ det_model_path=det_path,
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+ rec_model_path=rec_path,
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+ det_db_box_thresh=det_thresh,
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+ )
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+
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+
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+def resolve_input_image(path: Path, *, prefer_raw: bool) -> Path:
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+ """优先使用与 pipeline debug 配套的 *_raw.png。"""
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+ if not prefer_raw or path.stem.endswith("_raw"):
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+ return path
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+ raw_path = path.parent / f"{path.stem}_raw{path.suffix}"
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+ if raw_path.is_file():
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+ print(f" 使用原图: {raw_path.name}（跳过 {path.name}）")
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+ return raw_path
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+ return path
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+
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+
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+def collect_inputs(path: Path, *, prefer_raw: bool) -> List[Path]:
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+ if path.is_file():
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+ if path.suffix.lower() not in _IMAGE_SUFFIXES:
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+ raise ValueError(f"不支持的图像格式: {path}")
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+ return [resolve_input_image(path, prefer_raw=prefer_raw)]
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+
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+ if not path.is_dir():
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+ raise FileNotFoundError(path)
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+
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+ all_images = sorted(
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+ p
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+ for p in path.iterdir()
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+ if p.is_file() and p.suffix.lower() in _IMAGE_SUFFIXES
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+ )
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+ if not all_images:
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+ raise FileNotFoundError(f"目录内无图像: {path}")
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+
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+ if prefer_raw:
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+ raws = [p for p in all_images if p.stem.endswith("_raw")]
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+ if raws:
|
|
|
+ return raws
|
|
|
+
|
|
|
+ chosen: List[Path] = []
|
|
|
+ for p in all_images:
|
|
|
+ if p.stem.endswith("_raw"):
|
|
|
+ continue
|
|
|
+ raw_sibling = p.parent / f"{p.stem}_raw{p.suffix}"
|
|
|
+ if prefer_raw and raw_sibling.is_file():
|
|
|
+ continue
|
|
|
+ chosen.append(p)
|
|
|
+ return chosen or all_images
|
|
|
+
|
|
|
+
|
|
|
+def _match_hit(text: str, target: Optional[str]) -> Optional[str]:
|
|
|
+ if not text:
|
|
|
+ return None
|
|
|
+ if not target:
|
|
|
+ return "nonempty"
|
|
|
+ if target in text:
|
|
|
+ return "full"
|
|
|
+ if len(target) >= 6 and target.isdigit() and len(text) >= 6 and text.isdigit():
|
|
|
+ return "partial"
|
|
|
+ return None
|
|
|
+
|
|
|
+
|
|
|
+def run_sweep(
|
|
|
+ input_path: Path,
|
|
|
+ out_dir: Path,
|
|
|
+ *,
|
|
|
+ prefer_raw: bool,
|
|
|
+ target: Optional[str],
|
|
|
+ model_dir: Path,
|
|
|
+ methods: Sequence[str],
|
|
|
+ thresholds: Sequence[Optional[int]],
|
|
|
+ contrast_grid: List[Dict[str, Any]],
|
|
|
+ contrast_orders: Sequence[str],
|
|
|
+ upscales: Sequence[int],
|
|
|
+ det_threshs: Sequence[float],
|
|
|
+ save_images: bool,
|
|
|
+ run_baseline: bool,
|
|
|
+ baseline_upscale: int,
|
|
|
+) -> Dict[str, Any]:
|
|
|
+ resolved = resolve_input_image(input_path, prefer_raw=prefer_raw)
|
|
|
+ raw = cv2.imread(str(resolved))
|
|
|
+ if raw is None:
|
|
|
+ raise RuntimeError(f"无法读取图像: {resolved}")
|
|
|
+
|
|
|
+ stem = resolved.stem.removesuffix("_raw") if resolved.stem.endswith("_raw") else resolved.stem
|
|
|
+ cell_out = out_dir / stem
|
|
|
+ cell_out.mkdir(parents=True, exist_ok=True)
|
|
|
+
|
|
|
+ ocr_modes: List[Tuple[str, bool, bool]] = [
|
|
|
+ ("det_rec", True, True),
|
|
|
+ ("whole_rec", False, True),
|
|
|
+ ]
|
|
|
+
|
|
|
+ results: List[Dict[str, Any]] = []
|
|
|
+ hits: List[Dict[str, Any]] = []
|
|
|
+ engines: Dict[float, Any] = {}
|
|
|
+ total = 0
|
|
|
+
|
|
|
+ for method, thresh, contrast_cfg, c_order, upscale, det_th in product(
|
|
|
+ methods, thresholds, contrast_grid, contrast_orders, upscales, det_threshs
|
|
|
+ ):
|
|
|
+ # 过滤无效组合：非 threshold 方法不需要阈值
|
|
|
+ if method not in ("threshold",):
|
|
|
+ if thresh is not None:
|
|
|
+ continue
|
|
|
+ if det_th not in engines:
|
|
|
+ print(f" [{stem}] 加载 OCR det_db_box_thresh={det_th} ...")
|
|
|
+ engines[det_th] = _make_engine(det_th, model_dir)
|
|
|
+
|
|
|
+ img = _preprocess(
|
|
|
+ raw,
|
|
|
+ method=method,
|
|
|
+ thresh=thresh,
|
|
|
+ contrast_cfg=contrast_cfg,
|
|
|
+ upscale=upscale,
|
|
|
+ contrast_order=c_order,
|
|
|
+ )
|
|
|
+ c_tag = _contrast_tag(contrast_cfg)
|
|
|
+ o_tag = "b" if c_order == "before_upscale" else "a"
|
|
|
+ tag = f"{method}_t{thresh or 'd'}_{c_tag}_o{o_tag}_u{upscale}_det{det_th}"
|
|
|
+ if save_images:
|
|
|
+ cv2.imwrite(str(cell_out / f"{tag}.png"), img)
|
|
|
+
|
|
|
+ for mode_name, det, rec in ocr_modes:
|
|
|
+ total += 1
|
|
|
+ ocr = _ocr(engines[det_th], img, det=det, rec=rec)
|
|
|
+ row: Dict[str, Any] = {
|
|
|
+ "tag": tag,
|
|
|
+ "method": method,
|
|
|
+ "threshold": thresh,
|
|
|
+ "contrast_method": contrast_cfg.get("method", "none"),
|
|
|
+ "contrast_order": c_order,
|
|
|
+ "contrast_cfg": contrast_cfg,
|
|
|
+ "upscale": upscale,
|
|
|
+ "det_db_box_thresh": det_th,
|
|
|
+ "ocr_mode": mode_name,
|
|
|
+ **ocr,
|
|
|
+ }
|
|
|
+ results.append(row)
|
|
|
+ m = _match_hit(row.get("text", ""), target)
|
|
|
+ if m:
|
|
|
+ row["match"] = m
|
|
|
+ hits.append(row)
|
|
|
+ print(
|
|
|
+ f" HIT [{m}] {mode_name} {tag} "
|
|
|
+ f"score={row.get('score')} -> {row.get('text')!r}"
|
|
|
+ )
|
|
|
+
|
|
|
+ if run_baseline:
|
|
|
+ for det_th in det_threshs:
|
|
|
+ if det_th not in engines:
|
|
|
+ engines[det_th] = _make_engine(det_th, model_dir)
|
|
|
+ base_img = _upscale(raw, baseline_upscale)
|
|
|
+ if save_images:
|
|
|
+ cv2.imwrite(str(cell_out / f"baseline_upscale{baseline_upscale}.png"), base_img)
|
|
|
+ for mode_name, det, rec in ocr_modes:
|
|
|
+ ocr = _ocr(engines[det_th], base_img, det=det, rec=rec)
|
|
|
+ row = {
|
|
|
+ "tag": f"baseline_upscale{baseline_upscale}",
|
|
|
+ "det_db_box_thresh": det_th,
|
|
|
+ "ocr_mode": mode_name,
|
|
|
+ **ocr,
|
|
|
+ }
|
|
|
+ results.append(row)
|
|
|
+ m = _match_hit(row.get("text", ""), target)
|
|
|
+ if m:
|
|
|
+ row["match"] = m
|
|
|
+ hits.append(row)
|
|
|
+
|
|
|
+ report = {
|
|
|
+ "input": str(resolved),
|
|
|
+ "input_requested": str(input_path),
|
|
|
+ "output_dir": str(cell_out),
|
|
|
+ "target": target,
|
|
|
+ "total_trials": total,
|
|
|
+ "hits": hits,
|
|
|
+ "all_results": results,
|
|
|
+ }
|
|
|
+ report_path = cell_out / "sweep_report.json"
|
|
|
+ report_path.write_text(
|
|
|
+ json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8"
|
|
|
+ )
|
|
|
+
|
|
|
+ # ── 结论报告：按 OCR score 排序，分组对比 ──
|
|
|
+ _print_conclusions(stem, results, target)
|
|
|
+
|
|
|
+ return report
|
|
|
+
|
|
|
+
|
|
|
+def _print_conclusions(
|
|
|
+ stem: str,
|
|
|
+ results: List[Dict[str, Any]],
|
|
|
+ target: Optional[str],
|
|
|
+) -> None:
|
|
|
+ """打印实验结论：按 OCR score 排序，分组展示最优组合。"""
|
|
|
+ if not results:
|
|
|
+ return
|
|
|
+
|
|
|
+ print(f"\n{'='*70}")
|
|
|
+ print(f" 实验结论: {stem}")
|
|
|
+ if target:
|
|
|
+ print(f" 目标文字: {target}")
|
|
|
+ print(f"{'='*70}")
|
|
|
+
|
|
|
+ # 取 det_rec 模式的结果（优先用检测+识别完整结果）
|
|
|
+ dr_results = [r for r in results if r.get("ocr_mode") == "det_rec" and r.get("text")]
|
|
|
+ if not dr_results:
|
|
|
+ dr_results = [r for r in results if r.get("text")]
|
|
|
+
|
|
|
+ if not dr_results:
|
|
|
+ print(" (无有效 OCR 结果)")
|
|
|
+ return
|
|
|
+
|
|
|
+ # ── 1. 全局 Top-5 ──
|
|
|
+ scored = sorted(dr_results, key=lambda r: -(r.get("score") or 0))
|
|
|
+ print("\n 全局 OCR 得分 Top-5:")
|
|
|
+ for i, r in enumerate(scored[:5], 1):
|
|
|
+ print(f" {i}. score={r.get('score', 0):.4f} text={r.get('text', '')!r}")
|
|
|
+ print(f" tag={r.get('tag', '')}")
|
|
|
+
|
|
|
+ # ── 2. 按 contrast 方法分组最佳 ──
|
|
|
+ print("\n 按 contrast 方法分组最优（score 最高）:")
|
|
|
+ groups: Dict[str, List[Dict[str, Any]]] = {}
|
|
|
+ for r in scored:
|
|
|
+ cm = r.get("contrast_method", "?")
|
|
|
+ groups.setdefault(cm, []).append(r)
|
|
|
+
|
|
|
+ for cm in sorted(groups.keys()):
|
|
|
+ best = groups[cm][0]
|
|
|
+ wm = best.get("method", "?")
|
|
|
+ print(f" [{cm}] 最佳: score={best.get('score', 0):.4f} "
|
|
|
+ f"wm={wm} upscale={best.get('upscale')} "
|
|
|
+ f"text={best.get('text', '')!r}")
|
|
|
+
|
|
|
+ # ── 3. 有 watermark 处理 vs 无 watermark 处理对比 ──
|
|
|
+ print("\n 去水印开关对比（同 contrast 方法，最高 score）:")
|
|
|
+ wm_groups: Dict[str, Dict[str, Any]] = {}
|
|
|
+ for r in scored:
|
|
|
+ cm = r.get("contrast_method", "?")
|
|
|
+ wm = r.get("method", "?") if r.get("method") != "none" else "无去水印"
|
|
|
+ key = f"{cm}|{wm}"
|
|
|
+ cur_score = r.get("score") or 0
|
|
|
+ prev_score = (wm_groups.get(key) or {}).get("score") or 0
|
|
|
+ if key not in wm_groups or cur_score > prev_score:
|
|
|
+ wm_groups[key] = r
|
|
|
+
|
|
|
+ for cm in sorted(set(r.get("contrast_method", "?") for r in scored)):
|
|
|
+ wm_rows = [r for k, r in wm_groups.items() if k.startswith(cm + "|")]
|
|
|
+ if wm_rows:
|
|
|
+ best_row = max(wm_rows, key=lambda r: r.get("score") or 0)
|
|
|
+ wm_label = "无去水印" if best_row.get("method") == "none" else best_row.get("method", "?")
|
|
|
+ print(f" [{cm}] 最优: wm={wm_label} score={best_row.get('score', 0):.4f} "
|
|
|
+ f"text={best_row.get('text', '')!r}")
|
|
|
+
|
|
|
+ # ── 4. 放大顺序对比 ──
|
|
|
+ print("\n 放大前/后对比（同方法，最高 score）:")
|
|
|
+ order_data: Dict[str, Dict[str, Any]] = {}
|
|
|
+ for r in scored:
|
|
|
+ cm = r.get("contrast_method", "?")
|
|
|
+ co = r.get("contrast_order", "?")
|
|
|
+ key = f"{cm}|{co}"
|
|
|
+ cur_score = r.get("score") or 0
|
|
|
+ prev_score = (order_data.get(key) or {}).get("score") or 0
|
|
|
+ if key not in order_data or cur_score > prev_score:
|
|
|
+ order_data[key] = r
|
|
|
+
|
|
|
+ for cm in sorted(set(r.get("contrast_method", "?") for r in scored)):
|
|
|
+ b_score = (order_data.get(f"{cm}|before_upscale") or {}).get("score") or 0
|
|
|
+ a_score = (order_data.get(f"{cm}|after_upscale") or {}).get("score") or 0
|
|
|
+ better = "放大前" if b_score > a_score else ("放大后" if a_score > b_score else "持平")
|
|
|
+ if b_score or a_score:
|
|
|
+ print(f" [{cm}] 放大前={b_score:.4f} 放大后={a_score:.4f} 更优: {better}")
|
|
|
+
|
|
|
+ # ── 5. HIT 命中率统计 ──
|
|
|
+ if target:
|
|
|
+ hit_count = sum(1 for r in results if r.get("match"))
|
|
|
+ hit_by_cm: Dict[str, int] = {}
|
|
|
+ for r in results:
|
|
|
+ if r.get("match"):
|
|
|
+ cm = r.get("contrast_method", "?")
|
|
|
+ hit_by_cm[cm] = hit_by_cm.get(cm, 0) + 1
|
|
|
+ print(f"\n HIT 命中率 (target={target}): {hit_count}/{len(results)}")
|
|
|
+ for cm in sorted(hit_by_cm.keys()):
|
|
|
+ print(f" [{cm}] HIT={hit_by_cm[cm]}")
|
|
|
+
|
|
|
+ print(f"{'='*70}\n")
|
|
|
+
|
|
|
+
|
|
|
+def _parse_best_config(tag: str) -> Dict[str, Any]:
|
|
|
+ """解析最优参数 tag，如 threshold_t150_cl_1.0_8_ob_u128_det0.5。
|
|
|
+
|
|
|
+ tag 格式: {method}_t{thresh}_{c_tag}_o{b|a}_u{upscale}_det{det_th}
|
|
|
+ """
|
|
|
+ import re
|
|
|
+ cfg: Dict[str, Any] = {}
|
|
|
+ tag = tag.strip()
|
|
|
+
|
|
|
+ # 解析 method: threshold | masked_adaptive | none
|
|
|
+ m = re.match(r"(threshold|masked_adaptive|none)_t(\w+?)_(.+?)_o([ba])_u(\d+)_det([\d.]+)$", tag)
|
|
|
+ if not m:
|
|
|
+ raise ValueError(f"无法解析 best-config tag: {tag!r}")
|
|
|
+ method, thresh_str, c_part, order_char, upscale, det_th = m.groups()
|
|
|
+ cfg["method"] = method
|
|
|
+ cfg["threshold"] = int(thresh_str) if thresh_str.isdigit() else None
|
|
|
+ cfg["contrast_order"] = "before_upscale" if order_char == "b" else "after_upscale"
|
|
|
+ cfg["upscale"] = int(upscale)
|
|
|
+ cfg["det_db_box_thresh"] = float(det_th)
|
|
|
+
|
|
|
+ # 解析 contrast 部分: cl_1.0_8 | tr_85 | gm_0.85 | ln_2.0_98.0 | c0
|
|
|
+ if c_part == "c0":
|
|
|
+ cfg["contrast_cfg"] = {"method": "none"}
|
|
|
+ elif c_part.startswith("cl_"):
|
|
|
+ parts = c_part.split("_")
|
|
|
+ cfg["contrast_cfg"] = {"method": "clahe", "clip_limit": float(parts[1]), "tile_grid_size": int(parts[2])}
|
|
|
+ elif c_part.startswith("tr_"):
|
|
|
+ parts = c_part.split("_")
|
|
|
+ cfg["contrast_cfg"] = {"method": "text_restore", "text_black_target": int(parts[1])}
|
|
|
+ elif c_part.startswith("gm_"):
|
|
|
+ parts = c_part.split("_")
|
|
|
+ cfg["contrast_cfg"] = {"method": "gamma", "gamma": float(parts[1])}
|
|
|
+ elif c_part.startswith("ln_"):
|
|
|
+ parts = c_part.split("_")
|
|
|
+ cfg["contrast_cfg"] = {"method": "linear", "black_percentile": float(parts[1]), "white_percentile": float(parts[2])}
|
|
|
+ else:
|
|
|
+ raise ValueError(f"无法解析 contrast tag: {c_part!r} (in {tag})")
|
|
|
+
|
|
|
+ return cfg
|
|
|
+
|
|
|
+
|
|
|
+def run_best_config(
|
|
|
+ input_path: Path,
|
|
|
+ out_dir: Path,
|
|
|
+ *,
|
|
|
+ prefer_raw: bool,
|
|
|
+ best_cfg: Dict[str, Any],
|
|
|
+ model_dir: Path,
|
|
|
+ save_images: bool,
|
|
|
+) -> Dict[str, Any]:
|
|
|
+ """对单图用指定最优参数跑一次 OCR。"""
|
|
|
+ resolved = resolve_input_image(input_path, prefer_raw=prefer_raw)
|
|
|
+ raw = cv2.imread(str(resolved))
|
|
|
+ if raw is None:
|
|
|
+ raise RuntimeError(f"无法读取图像: {resolved}")
|
|
|
+
|
|
|
+ stem = resolved.stem.removesuffix("_raw") if resolved.stem.endswith("_raw") else resolved.stem
|
|
|
+ cell_out = out_dir / stem
|
|
|
+ cell_out.mkdir(parents=True, exist_ok=True)
|
|
|
+
|
|
|
+ engine = _make_engine(best_cfg["det_db_box_thresh"], model_dir)
|
|
|
+ img = _preprocess(
|
|
|
+ raw,
|
|
|
+ method=best_cfg["method"],
|
|
|
+ thresh=best_cfg.get("threshold"),
|
|
|
+ contrast_cfg=best_cfg["contrast_cfg"],
|
|
|
+ upscale=best_cfg["upscale"],
|
|
|
+ contrast_order=best_cfg["contrast_order"],
|
|
|
+ )
|
|
|
+ tag = best_cfg.get("_tag", "best")
|
|
|
+ if save_images:
|
|
|
+ cv2.imwrite(str(cell_out / f"{tag}.png"), img)
|
|
|
+
|
|
|
+ ocr = _ocr(engine, img, det=True, rec=True)
|
|
|
+ row: Dict[str, Any] = {
|
|
|
+ "tag": tag,
|
|
|
+ "method": best_cfg["method"],
|
|
|
+ "threshold": best_cfg.get("threshold"),
|
|
|
+ "contrast_method": best_cfg["contrast_cfg"].get("method", "none"),
|
|
|
+ "contrast_order": best_cfg["contrast_order"],
|
|
|
+ "contrast_cfg": best_cfg["contrast_cfg"],
|
|
|
+ "upscale": best_cfg["upscale"],
|
|
|
+ "det_db_box_thresh": best_cfg["det_db_box_thresh"],
|
|
|
+ "ocr_mode": "det_rec",
|
|
|
+ **ocr,
|
|
|
+ }
|
|
|
+
|
|
|
+ report = {
|
|
|
+ "input": str(resolved),
|
|
|
+ "input_requested": str(input_path),
|
|
|
+ "output_dir": str(cell_out),
|
|
|
+ "result": row,
|
|
|
+ }
|
|
|
+ report_path = cell_out / "best_result.json"
|
|
|
+ report_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
|
|
|
+ return report
|
|
|
+
|
|
|
+
|
|
|
+def _build_arg_parser() -> argparse.ArgumentParser:
|
|
|
+ p = argparse.ArgumentParser(
|
|
|
+ description="单元格图预处理 + OCR 参数网格扫描（对齐 pipeline 格级二次 OCR）",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "input",
|
|
|
+ type=Path,
|
|
|
+ help="单元格裁剪图路径，或 tablecell_ocr 目录（批量扫描）",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "-o",
|
|
|
+ "--output",
|
|
|
+ type=Path,
|
|
|
+ default=None,
|
|
|
+ help="输出目录，默认 <input_dir|input_parent>/sweep_out/<stem>",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "-t",
|
|
|
+ "--target",
|
|
|
+ default=None,
|
|
|
+ help="期望 OCR 文本；用于标记 HIT（子串匹配）。省略则任意非空为 HIT",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--model-dir",
|
|
|
+ type=Path,
|
|
|
+ default=None,
|
|
|
+ help="PaddleOCR torch 模型目录（含 det/rec .pth），也可用 OCR_*_MODEL_PATH",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--no-prefer-raw",
|
|
|
+ action="store_true",
|
|
|
+ help="不自动选用同名的 *_raw.png",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--quick",
|
|
|
+ action="store_true",
|
|
|
+ help="缩小网格（threshold 155,165 × upscale 128,192 × det 0.5 × contrast 精简）",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--methods",
|
|
|
+ default="threshold,masked_adaptive,none",
|
|
|
+ help="去水印方式，逗号分隔；none=不去水印",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--thresholds",
|
|
|
+ default="155,165,none",
|
|
|
+ help="threshold 法的阈值；none=预设默认",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--contrast-orders",
|
|
|
+ default="before_upscale,after_upscale",
|
|
|
+ help="contrast 执行顺序: before_upscale（放大前）, after_upscale（放大后）, 逗号组合",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--upscales",
|
|
|
+ default="128,192",
|
|
|
+ help="最短边放大目标，逗号分隔整数",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--det-threshs",
|
|
|
+ # default="0.2,0.3,0.4,0.5",
|
|
|
+ default="0.5",
|
|
|
+ help="det_db_box_thresh，逗号分隔",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--no-save-images",
|
|
|
+ action="store_true",
|
|
|
+ help="不写出中间预处理 png（仅报告）",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--no-baseline",
|
|
|
+ action="store_true",
|
|
|
+ help="跳过「仅放大、不去水印」对照组",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--baseline-upscale",
|
|
|
+ type=int,
|
|
|
+ default=192,
|
|
|
+ help="baseline 对照组的最短边放大",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--best-only",
|
|
|
+ action="store_true",
|
|
|
+ help="不跑参数网格，对目录下所有图用 --best-config 指定参数跑一次，验证适配性",
|
|
|
+ )
|
|
|
+ p.add_argument(
|
|
|
+ "--best-config",
|
|
|
+ default="threshold_t150_cl_1.0_8_ob_u128_det0.5",
|
|
|
+ help="最优参数 tag，如 threshold_t150_cl_1.0_8_ob_u128_det0.5",
|
|
|
+ )
|
|
|
+ return p
|
|
|
+
|
|
|
+
|
|
|
+def main(argv: Optional[Sequence[str]] = None) -> None:
|
|
|
+ args = _build_arg_parser().parse_args(argv)
|
|
|
+ inputs = collect_inputs(args.input, prefer_raw=not args.no_prefer_raw)
|
|
|
+ if not inputs:
|
|
|
+ raise SystemExit("未找到可扫描的图像")
|
|
|
+
|
|
|
+ if args.output is not None:
|
|
|
+ out_root = args.output
|
|
|
+ elif args.input.is_file():
|
|
|
+ out_root = args.input.parent / "sweep_out"
|
|
|
+ else:
|
|
|
+ out_root = args.input / "sweep_out"
|
|
|
+ out_root.mkdir(parents=True, exist_ok=True)
|
|
|
+
|
|
|
+ model_dir = args.model_dir or _default_model_dir()
|
|
|
+
|
|
|
+ if args.best_only:
|
|
|
+ # 验证适配性模式：对目录下所有图用最优参数跑一次
|
|
|
+ best_cfg = _parse_best_config(args.best_config)
|
|
|
+ best_cfg["_tag"] = args.best_config
|
|
|
+ print(f"最佳参数验证模式: {args.best_config}")
|
|
|
+ print(f" 解析: method={best_cfg['method']} contrast={best_cfg['contrast_cfg'].get('method')} "
|
|
|
+ f"upscale={best_cfg['upscale']} order={best_cfg['contrast_order']}")
|
|
|
+ print(f" 共 {len(inputs)} 张图")
|
|
|
+
|
|
|
+ all_texts: List[Dict[str, Any]] = []
|
|
|
+ hit_count = 0
|
|
|
+ for img_path in inputs:
|
|
|
+ report = run_best_config(
|
|
|
+ img_path, out_root,
|
|
|
+ prefer_raw=not args.no_prefer_raw,
|
|
|
+ best_cfg=best_cfg,
|
|
|
+ model_dir=model_dir,
|
|
|
+ save_images=not args.no_save_images,
|
|
|
+ )
|
|
|
+ result = report["result"]
|
|
|
+ text = result.get("text", "")
|
|
|
+ score = result.get("score", 0)
|
|
|
+ all_texts.append({
|
|
|
+ "input": img_path.name,
|
|
|
+ "text": text,
|
|
|
+ "score": score,
|
|
|
+ "report": str(Path(report["output_dir"]) / "best_result.json"),
|
|
|
+ })
|
|
|
+ m = _match_hit(text, args.target)
|
|
|
+ hit_info = f" [HIT: {m}]" if m else ""
|
|
|
+ print(f" {img_path.name}: score={score:.4f} text={text!r}{hit_info}")
|
|
|
+ if m:
|
|
|
+ hit_count += 1
|
|
|
+
|
|
|
+ # 汇总
|
|
|
+ summary_path = out_root / "best_summary.json"
|
|
|
+ summary_data = {
|
|
|
+ "best_config": args.best_config,
|
|
|
+ "total": len(all_texts),
|
|
|
+ "hits": hit_count,
|
|
|
+ "target": args.target,
|
|
|
+ "results": all_texts,
|
|
|
+ }
|
|
|
+ summary_path.write_text(json.dumps(summary_data, ensure_ascii=False, indent=2), encoding="utf-8")
|
|
|
+ print(f"\n汇总: {hit_count}/{len(all_texts)} HIT -> {summary_path}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 正常参数网格扫描模式
|
|
|
+ methods = [m.strip() for m in args.methods.split(",") if m.strip()]
|
|
|
+ contrast_orders = [o.strip() for o in args.contrast_orders.split(",") if o.strip()]
|
|
|
+
|
|
|
+ if args.quick:
|
|
|
+ thresholds = [150, 155]
|
|
|
+ upscales = [128, 192]
|
|
|
+ det_threshs = [0.5]
|
|
|
+ else:
|
|
|
+ thresholds = _parse_csv_ints(args.thresholds)
|
|
|
+ upscales = [int(x) for x in args.upscales.split(",") if x.strip()]
|
|
|
+ det_threshs = _parse_csv_floats(args.det_threshs)
|
|
|
+
|
|
|
+ contrast_grid = _build_contrast_grid(quick=args.quick)
|
|
|
+
|
|
|
+ print(f"扫描 {len(inputs)} 张图 -> {out_root}")
|
|
|
+ print(f" methods={methods} thresholds={thresholds} upscales={upscales}")
|
|
|
+ print(f" contrast_methods={len(contrast_grid)} orders={contrast_orders}")
|
|
|
+ if args.target:
|
|
|
+ print(f" target={args.target!r}")
|
|
|
+
|
|
|
+ summary: List[Dict[str, Any]] = []
|
|
|
+ for img_path in inputs:
|
|
|
+ print(f"\n=== {img_path.name} ===")
|
|
|
+ report = run_sweep(
|
|
|
+ img_path,
|
|
|
+ out_root,
|
|
|
+ prefer_raw=not args.no_prefer_raw,
|
|
|
+ target=args.target,
|
|
|
+ model_dir=model_dir,
|
|
|
+ methods=methods,
|
|
|
+ thresholds=thresholds,
|
|
|
+ contrast_grid=contrast_grid,
|
|
|
+ contrast_orders=contrast_orders,
|
|
|
+ upscales=upscales,
|
|
|
+ det_threshs=det_threshs,
|
|
|
+ save_images=not args.no_save_images,
|
|
|
+ run_baseline=not args.no_baseline,
|
|
|
+ baseline_upscale=args.baseline_upscale,
|
|
|
+ )
|
|
|
+ summary.append(
|
|
|
+ {
|
|
|
+ "input": report["input"],
|
|
|
+ "hits": len(report["hits"]),
|
|
|
+ "report": str(Path(report["output_dir"]) / "sweep_report.json"),
|
|
|
+ }
|
|
|
+ )
|
|
|
+
|
|
|
+ index_path = out_root / "sweep_index.json"
|
|
|
+ index_path.write_text(
|
|
|
+ json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8"
|
|
|
+ )
|
|
|
+ print(f"\n全部完成，索引: {index_path}")
|
|
|
+ for s in summary:
|
|
|
+ print(f" {s['input']}: {s['hits']} hits -> {s['report']}")
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ if len(sys.argv) == 1:
|
|
|
+ print("ℹ️ 未提供命令行参数，使用默认配置运行...")
|
|
|
+ default_config = {
|
|
|
+ # "input": "/Users/zhch158/workspace/data/流水分析/彭_广东兴宁农村商业银行/bank_statement_yusys_local/debug/table_recognition_wired/tablecell_ocr/彭_广东兴宁农村商业银行_page_002_0/cell219_empty_empty_raw.png",
|
|
|
+ # "output": "./output/彭_广东兴宁农村商业银行/cell219_sweep",
|
|
|
+ # "target": "ATM存折取款",
|
|
|
+
|
|
|
+ # "input": "/Users/zhch158/workspace/data/流水分析/彭_广东兴宁农村商业银行/bank_statement_yusys_local/debug/table_recognition_wired/tablecell_ocr/彭_广东兴宁农村商业银行_page_002_0/cell007_whole_longer_易型交类_raw.png",
|
|
|
+ # "output": "./output/彭_广东兴宁农村商业银行/cell007_sweep",
|
|
|
+ # "target": "交易类型",
|
|
|
+ # "quick": True,
|
|
|
+
|
|
|
+ # "input": "/Users/zhch158/workspace/data/流水分析/钟_广东陆丰农村商业银行/bank_statement_yusys_local/debug/table_recognition_wired/tablecell_ocr/钟_广东陆丰农村商业银行_page_001_0/cell217_empty_empty_raw.png",
|
|
|
+ # "output": "./output/钟_广东陆丰农村商业银行/cell217_sweep",
|
|
|
+ # "target": "专项资金",
|
|
|
+ # "quick": True,
|
|
|
+
|
|
|
+ # "input": "/Users/zhch158/workspace/data/流水分析/彭_广东兴宁农村商业银行/bank_statement_yusys_local/debug/table_recognition_wired/tablecell_ocr/彭_广东兴宁农村商业银行_page_002_0",
|
|
|
+ # "output": "./output/彭_广东兴宁农村商业银行",
|
|
|
+ # "best-config": "threshold_t150_cl_1.0_8_ob_u128_det0.5",
|
|
|
+ # "best-only": True,
|
|
|
+
|
|
|
+ "input": "/Users/zhch158/workspace/data/流水分析/钟_广东陆丰农村商业银行/bank_statement_yusys_local/debug/table_recognition_wired/tablecell_ocr/钟_广东陆丰农村商业银行_page_001_0",
|
|
|
+ "output": "./output/钟_广东陆丰农村商业银行",
|
|
|
+ # "best-config": "threshold_t150_cl_1.0_8_ob_u128_det0.5",
|
|
|
+ "best-config": "threshold_t150_cl_1.0_4_ob_u128_det0.5",
|
|
|
+ "best-only": True,
|
|
|
+ }
|
|
|
+ sys.argv = [sys.argv[0], default_config["input"]]
|
|
|
+ for key, value in default_config.items():
|
|
|
+ if key == "input":
|
|
|
+ continue
|
|
|
+ flag = f"--{key.replace('_', '-')}"
|
|
|
+ if isinstance(value, bool) and value:
|
|
|
+ sys.argv.append(flag)
|
|
|
+ elif not isinstance(value, bool):
|
|
|
+ sys.argv.extend([flag, str(value)])
|
|
|
+
|
|
|
+ sys.exit(main())
|
