init: 添加初始python版本

index.py

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+from __future__ import annotations
+
+import argparse
+import os
+import re
+import sys
+from datetime import datetime
+from pathlib import Path
+
+import fitz  # PyMuPDF
+
+
+BASE_DIR = Path(__file__).resolve().parent
+DEFAULT_SOURCE_DIR = BASE_DIR / "source"
+DEFAULT_OUTPUT_DIR = BASE_DIR / "output"
+
+
+def safe_dir_name(name: str) -> str:
+	"""Sanitize directory name for Windows filesystem."""
+	cleaned = re.sub(r"[\\/:*?\"<>|]", "_", name.strip())
+	cleaned = re.sub(r"\s+", " ", cleaned)
+	return cleaned[:120] if cleaned else "未识别收款人"
+
+
+def extract_payee_name(text: str) -> str | None:
+	"""Extract 收款人全称 from text using common label patterns."""
+	if not text:
+		return None
+
+	normalized = text.replace("\u3000", " ")
+	compact = re.sub(r"\s+", "", normalized)
+
+	compact_match = re.search(
+		r"收款人全称[:：]?(.+?)(别名|账号|开户行|大写金额|小写金额|用途|钞汇标志|摘要|重要提示|付款人全称|$)",
+		compact,
+		flags=re.IGNORECASE,
+	)
+	if compact_match:
+		value = compact_match.group(1).strip(" :：")
+		if value:
+			return value
+
+	patterns = [
+		r"收款人全称\s*[:：]\s*([^\n\r]+)",
+		r"收款人\s*全称\s*[:：]\s*([^\n\r]+)",
+		r"收\s*款\s*人\s*全\s*称\s*[:：]?\s*([^\n\r]+)",
+		r"收款人全称\s+([^\n\r]+)",
+	]
+	for pattern in patterns:
+		match = re.search(pattern, normalized, flags=re.IGNORECASE)
+		if match:
+			value = match.group(1).strip()
+			value = re.split(r"\s{2,}|金额|开户行|账号|日期", value)[0].strip(" :：")
+			if value:
+				return value
+
+	lines = [line.strip() for line in normalized.splitlines() if line.strip()]
+	for idx, line in enumerate(lines):
+		if "收款人全称" in line:
+			after = line.split("收款人全称", 1)[1].strip(" :：")
+			if after:
+				return after
+			if idx + 1 < len(lines):
+				candidate = lines[idx + 1].strip(" :：")
+				if candidate:
+					return candidate
+
+	return None
+
+
+def extract_text_via_ocr(image_path: Path) -> str:
+	"""OCR fallback for scanned PDFs without embedded text."""
+	try:
+		import pytesseract
+		from PIL import Image
+	except ImportError:
+		return ""
+
+	tesseract_cmd = os.environ.get("TESSERACT_CMD", "")
+	if tesseract_cmd:
+		pytesseract.pytesseract.tesseract_cmd = tesseract_cmd
+
+	try:
+		with Image.open(image_path) as img:
+			return pytesseract.image_to_string(img, lang="chi_sim+eng")
+	except Exception:
+		return ""
+
+
+def resolve_io_dirs() -> tuple[Path, Path]:
+	"""Resolve source/output directories from CLI, with defaults as fallback."""
+	parser = argparse.ArgumentParser(description="PDF 回执拆分与按收款人分类")
+	parser.add_argument("input_dir", nargs="?", help="输入目录（可选，默认 ./source）")
+	parser.add_argument("output_dir", nargs="?", help="输出目录（可选，默认 ./output）")
+	parser.add_argument("-i", "--input", dest="input_opt", help="输入目录")
+	parser.add_argument("-o", "--output", dest="output_opt", help="输出目录")
+	args = parser.parse_args()
+
+	input_raw = args.input_opt or args.input_dir
+	output_raw = args.output_opt or args.output_dir
+
+	source_dir = Path(input_raw).expanduser().resolve() if input_raw else DEFAULT_SOURCE_DIR
+	output_dir = Path(output_raw).expanduser().resolve() if output_raw else DEFAULT_OUTPUT_DIR
+	return source_dir, output_dir
+
+
+def ensure_dirs(source_dir: Path, output_dir: Path) -> None:
+	source_dir.mkdir(parents=True, exist_ok=True)
+	output_dir.mkdir(parents=True, exist_ok=True)
+
+
+def get_receipt_clips(page: fitz.Page) -> list[fitz.Rect]:
+	"""Infer receipt regions on one page by locating repeated receipt headers."""
+	header_text = "中国建设银行网上银行电子回执"
+	header_rects = page.search_for(header_text)
+
+	if not header_rects:
+		return [page.rect]
+
+	sorted_rects = sorted(header_rects, key=lambda r: (round(r.y0, 1), r.x0))
+	unique_y: list[float] = []
+	for rect in sorted_rects:
+		y = rect.y0
+		if not unique_y or abs(y - unique_y[-1]) > 8:
+			unique_y.append(y)
+
+	bounds = page.rect
+	clips: list[fitz.Rect] = []
+	for idx, y in enumerate(unique_y):
+		y0 = max(bounds.y0, y - 6)
+		y1 = unique_y[idx + 1] - 6 if idx + 1 < len(unique_y) else bounds.y1
+		if y1 - y0 > 40:
+			clips.append(fitz.Rect(bounds.x0, y0, bounds.x1, y1))
+
+	return clips or [page.rect]
+
+
+def process_pdf(pdf_path: Path, output_dir: Path) -> tuple[int, int, dict[str, int]]:
+	"""Split PDF pages to images and classify by payee name.
+
+	Returns:
+		(success_count, failed_count, per_dir_counts)
+	"""
+	success_count = 0
+	failed_count = 0
+	per_dir_counts: dict[str, int] = {}
+
+	with fitz.open(pdf_path) as doc:
+		for page_index, page in enumerate(doc, start=1):
+			receipt_clips = get_receipt_clips(page)
+			for receipt_index, clip in enumerate(receipt_clips, start=1):
+				image_name = f"{pdf_path.stem}_p{page_index:03d}_r{receipt_index:03d}.png"
+				temp_image = output_dir / image_name
+
+				# Render one inferred receipt region as one output image.
+				pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0), alpha=False, clip=clip)
+				pix.save(temp_image)
+
+				receipt_text = page.get_text("text", clip=clip) or ""
+				payee_name = extract_payee_name(receipt_text)
+
+				if not payee_name:
+					ocr_text = extract_text_via_ocr(temp_image)
+					payee_name = extract_payee_name(ocr_text)
+
+				if not payee_name:
+					payee_name = "未识别收款人"
+					failed_count += 1
+				else:
+					success_count += 1
+
+				target_dir = output_dir / safe_dir_name(payee_name)
+				target_dir.mkdir(parents=True, exist_ok=True)
+				per_dir_counts[target_dir.name] = per_dir_counts.get(target_dir.name, 0) + 1
+
+				final_image = target_dir / image_name
+				if final_image.exists():
+					final_image = target_dir / f"{pdf_path.stem}_p{page_index:03d}_r{receipt_index:03d}_{os.getpid()}.png"
+
+				temp_image.replace(final_image)
+
+	return success_count, failed_count, per_dir_counts
+
+
+def write_execution_report(report_lines: list[str]) -> Path:
+	"""Write execution details into a timestamped report file under BASE_DIR."""
+	timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+	report_path = BASE_DIR / f"执行结果记录_{timestamp}.txt"
+	report_path.write_text("\n".join(report_lines) + "\n", encoding="utf-8")
+	return report_path
+
+
+def main() -> int:
+	source_dir, output_dir = resolve_io_dirs()
+	ensure_dirs(source_dir, output_dir)
+
+	pdf_files = sorted(source_dir.glob("*.pdf"))
+	if not pdf_files:
+		print(f"未在目录中发现 PDF: {source_dir}")
+		print("请将 PDF 文件放到 source 目录后重试。")
+		return 0
+
+	total_ok = 0
+	total_unknown = 0
+	report_lines = [
+		f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
+		f"输入目录: {source_dir}",
+		f"输出目录: {output_dir}",
+		"",
+	]
+
+	for pdf_file in pdf_files:
+		print(f"处理文件: {pdf_file.name}")
+		ok, unknown, dir_counts = process_pdf(pdf_file, output_dir)
+		print(f"    识别到收款人全称的图片数: {ok}")
+		print(f"    未识别收款人全称的图片数: {unknown}")
+		total_ok += ok
+		total_unknown += unknown
+
+		report_lines.append(f"PDF: {pdf_file.name}")
+		report_lines.append(f"  拆分目录数: {len(dir_counts)}")
+		report_lines.append(f"  识别到收款人全称的图片数: {ok}")
+		report_lines.append(f"  未识别收款人全称的图片数: {unknown}")
+		report_lines.append("  目录明细:")
+		for dir_name in sorted(dir_counts):
+			report_lines.append(f"    - {dir_name}: {dir_counts[dir_name]} 张")
+		report_lines.append("")
+
+	print("\n处理完成")
+	print(f"识别到收款人全称的图片数: {total_ok}")
+	print(f"未识别收款人全称的图片数: {total_unknown}")
+	print(f"输出目录: {output_dir}")
+
+	report_lines.append("汇总:")
+	report_lines.append(f"  识别到收款人全称的图片总数: {total_ok}")
+	report_lines.append(f"  未识别收款人全称的图片总数: {total_unknown}")
+
+	report_path = write_execution_report(report_lines)
+	print(f"执行结果记录文件: {report_path}")
+	return 0
+
+
+if __name__ == "__main__":
+	sys.exit(main())