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+"""PPStructureV3公共函数"""
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+import json
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+import traceback
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+import warnings
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+import base64
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+from pathlib import Path
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+from PIL import Image
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+from typing import List, Dict, Any, Union
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+import numpy as np
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+
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+from utils import (
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+ get_image_files_from_dir,
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+ get_image_files_from_list,
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+ get_image_files_from_csv,
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+ load_images_from_pdf,
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+ normalize_markdown_table
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+)
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+
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+def convert_pdf_to_images(pdf_file: str, output_dir: str | None = None, dpi: int = 200) -> List[str]:
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+ """
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+ 将PDF转换为图像文件
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+
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+ Args:
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+ pdf_file: PDF文件路径
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+ output_dir: 输出目录
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+ dpi: 图像分辨率
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+
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+ Returns:
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+ 生成的图像文件路径列表
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+ """
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+ pdf_path = Path(pdf_file)
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+ if not pdf_path.exists() or pdf_path.suffix.lower() != '.pdf':
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+ print(f"❌ Invalid PDF file: {pdf_path}")
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+ return []
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+
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+ # 如果没有指定输出目录，使用PDF同名目录
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+ if output_dir is None:
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+ output_path = pdf_path.parent / f"{pdf_path.stem}"
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+ else:
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+ output_path = Path(output_dir) / f"{pdf_path.stem}"
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+ output_path = output_path.resolve()
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+ output_path.mkdir(parents=True, exist_ok=True)
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+
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+ try:
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+ # 使用doc_utils中的函数加载PDF图像
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+ images = load_images_from_pdf(str(pdf_path), dpi=dpi)
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+
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+ image_paths = []
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+ for i, image in enumerate(images):
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+ # 生成图像文件名
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+ image_filename = f"{pdf_path.stem}_page_{i+1:03d}.png"
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+ image_path = output_path / image_filename
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+
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+ # 保存图像
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+ image.save(str(image_path))
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+ image_paths.append(str(image_path))
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+
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+ print(f"✅ Converted {len(images)} pages from {pdf_path.name} to images")
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+ return image_paths
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+
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+ except Exception as e:
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+ print(f"❌ Error converting PDF {pdf_path}: {e}")
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+ traceback.print_exc()
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+ return []
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+
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+def get_input_files(args) -> List[str]:
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+ """
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+ 获取输入文件列表，统一处理PDF和图像文件
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+
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+ Args:
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+ args: 命令行参数
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+
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+ Returns:
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+ 处理后的图像文件路径列表
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+ """
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+ input_files = []
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+
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+ # 获取原始输入文件
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+ if args.input_csv:
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+ raw_files = get_image_files_from_csv(args.input_csv, "fail")
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+ elif args.input_file_list:
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+ raw_files = get_image_files_from_list(args.input_file_list)
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+ elif args.input_file:
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+ raw_files = [Path(args.input_file).resolve()]
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+ else:
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+ input_dir = Path(args.input_dir).resolve()
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+ if not input_dir.exists():
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+ print(f"❌ Input directory does not exist: {input_dir}")
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+ return []
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+
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+ # 获取所有支持的文件（图像和PDF）
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+ image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif']
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+ pdf_extensions = ['.pdf']
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+
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+ raw_files = []
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+ for ext in image_extensions + pdf_extensions:
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+ raw_files.extend(list(input_dir.glob(f"*{ext}")))
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+ raw_files.extend(list(input_dir.glob(f"*{ext.upper()}")))
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+
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+ raw_files = [str(f) for f in raw_files]
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+
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+ # 分别处理PDF和图像文件
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+ pdf_count = 0
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+ image_count = 0
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+
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+ for file_path in raw_files:
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+ file_path = Path(file_path)
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+
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+ if file_path.suffix.lower() == '.pdf':
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+ # 转换PDF为图像
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+ print(f"📄 Processing PDF: {file_path.name}")
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+ pdf_images = convert_pdf_to_images(
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+ str(file_path),
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+ args.output_dir,
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+ dpi=args.pdf_dpi
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+ )
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+ input_files.extend(pdf_images)
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+ pdf_count += 1
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+ else:
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+ # 直接添加图像文件
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+ if file_path.exists():
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+ input_files.append(str(file_path))
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+ image_count += 1
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+
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+ print(f"📊 Input summary:")
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+ print(f" PDF files processed: {pdf_count}")
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+ print(f" Image files found: {image_count}")
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+ print(f" Total image files to process: {len(input_files)}")
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+
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+ return input_files
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+
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+def convert_pruned_result_to_json(pruned_result: Dict[str, Any],
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+ input_image_path: str,
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+ output_dir: str,
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+ filename: str,
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+ normalize_numbers: bool = True) -> tuple[str, Dict[str, Any]]:
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+ """
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+ 将API返回结果转换为标准JSON格式，并支持数字标准化
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+ """
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+ if not pruned_result:
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+ return "", {}
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+
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+ # 构造标准格式的JSON
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+ converted_json = {
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+ "input_path": input_image_path,
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+ "page_index": None,
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+ "model_settings": pruned_result.get('model_settings', {}),
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+ "parsing_res_list": pruned_result.get('parsing_res_list', []),
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+ "doc_preprocessor_res": {
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+ "input_path": None,
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+ "page_index": None,
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+ "model_settings": pruned_result.get('doc_preprocessor_res', {}).get('model_settings', {}),
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+ "angle": pruned_result.get('doc_preprocessor_res', {}).get('angle', 0)
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+ },
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+ "layout_det_res": {
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+ "input_path": None,
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+ "page_index": None,
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+ "boxes": pruned_result.get('layout_det_res', {}).get('boxes', [])
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+ },
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+ "overall_ocr_res": {
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+ "input_path": None,
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+ "page_index": None,
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+ "model_settings": pruned_result.get('overall_ocr_res', {}).get('model_settings', {}),
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+ "dt_polys": pruned_result.get('overall_ocr_res', {}).get('dt_polys', []),
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+ "text_det_params": pruned_result.get('overall_ocr_res', {}).get('text_det_params', {}),
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+ "text_type": pruned_result.get('overall_ocr_res', {}).get('text_type', 'general'),
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+ "textline_orientation_angles": pruned_result.get('overall_ocr_res', {}).get('textline_orientation_angles', []),
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+ "text_rec_score_thresh": pruned_result.get('overall_ocr_res', {}).get('text_rec_score_thresh', 0.0),
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+ "return_word_box": pruned_result.get('overall_ocr_res', {}).get('return_word_box', False),
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+ "rec_texts": pruned_result.get('overall_ocr_res', {}).get('rec_texts', []),
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+ "rec_scores": pruned_result.get('overall_ocr_res', {}).get('rec_scores', []),
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+ "rec_polys": pruned_result.get('overall_ocr_res', {}).get('rec_polys', []),
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+ "rec_boxes": pruned_result.get('overall_ocr_res', {}).get('rec_boxes', [])
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+ },
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+ "table_res_list": pruned_result.get('table_res_list', [])
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+ }
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+
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+ # 数字标准化处理
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+ original_json = converted_json.copy()
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+ changes_count = 0
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+
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+ if normalize_numbers:
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+ # 1. 标准化 parsing_res_list 中的文本内容
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+ for item in converted_json.get('parsing_res_list', []):
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+ if 'block_content' in item:
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+ original_content = item['block_content']
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+ normalized_content = original_content
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+ # 根据block_label类型选择标准化方法
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+ if item.get('block_label') == 'table':
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+ normalized_content = normalize_markdown_table(original_content)
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+ # else:
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+ # normalized_content = normalize_financial_numbers(original_content)
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+
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+ if original_content != normalized_content:
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+ item['block_content'] = normalized_content
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+ changes_count += len([1 for o, n in zip(original_content, normalized_content) if o != n])
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+
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+ # 2. 标准化 table_res_list 中的HTML表格
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+ for table_item in converted_json.get('table_res_list', []):
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+ if 'pred_html' in table_item:
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+ original_html = table_item['pred_html']
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+ normalized_html = normalize_markdown_table(original_html)
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+
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+ if original_html != normalized_html:
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+ table_item['pred_html'] = normalized_html
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+ changes_count += len([1 for o, n in zip(original_html, normalized_html) if o != n])
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+
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+ # 检查是否需要修复表格一致性（这里只做统计，实际修复可能需要更复杂的逻辑）
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+ # 统计表格数量
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+ parsing_res_tables_count = 0
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+ table_res_list_count = 0
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+ if 'parsing_res_list' in converted_json:
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+ parsing_res_tables_count = len([item for item in converted_json['parsing_res_list']
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+ if 'block_label' in item and item['block_label'] == 'table'])
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+ if 'table_res_list' in converted_json:
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+ table_res_list_count = len(converted_json["table_res_list"])
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+ table_consistency_fixed = False
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+ if parsing_res_tables_count != table_res_list_count:
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+ warnings.warn(f"⚠️ Warning: {filename} Table count mismatch - parsing_res_list has {parsing_res_tables_count} tables, "
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+ f"but table_res_list has {table_res_list_count} tables.")
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+ table_consistency_fixed = True
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+ # 这里可以添加实际的修复逻辑，例如根据需要添加或删除表格项
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+ # 但由于缺乏具体规则，暂时只做统计和警告
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+
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+ # 3. 标准化 overall_ocr_res 中的识别文本
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+ # ocr_res = converted_json.get('overall_ocr_res', {})
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+ # if 'rec_texts' in ocr_res:
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+ # original_texts = ocr_res['rec_texts'][:]
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+ # normalized_texts = []
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+
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+ # for text in original_texts:
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+ # normalized_text = normalize_financial_numbers(text)
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+ # normalized_texts.append(normalized_text)
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+ # if text != normalized_text:
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+ # changes_count += len([1 for o, n in zip(text, normalized_text) if o != n])
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+
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+ # ocr_res['rec_texts'] = normalized_texts
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+
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+ # 添加标准化处理信息
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+ converted_json['processing_info'] = {
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+ "normalize_numbers": normalize_numbers,
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+ "changes_applied": changes_count > 0,
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+ "character_changes_count": changes_count,
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+ "parsing_res_tables_count": parsing_res_tables_count,
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+ "table_res_list_count": table_res_list_count,
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+ "table_consistency_fixed": table_consistency_fixed
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+ }
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+
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+ # if changes_count > 0:
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+ # print(f"🔧 已标准化 {changes_count} 个字符（全角→半角）")
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+ else:
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+ converted_json['processing_info'] = {
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+ "normalize_numbers": False,
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+ "changes_applied": False,
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+ "character_changes_count": 0
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+ }
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+
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+ # 保存JSON文件
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+ output_path = Path(output_dir).resolve()
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+ output_path.mkdir(parents=True, exist_ok=True)
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+
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+ json_file_path = output_path / f"{filename}.json"
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+ with open(json_file_path, 'w', encoding='utf-8') as f:
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+ json.dump(converted_json, f, ensure_ascii=False, indent=2)
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+
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+ # 如果启用了标准化且有变化，保存原始版本用于对比
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+ if normalize_numbers and changes_count > 0:
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+ original_output_path = output_path / f"{filename}_original.json"
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+ with open(original_output_path, 'w', encoding='utf-8') as f:
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+ json.dump(original_json, f, ensure_ascii=False, indent=2)
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+
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+ return str(output_path), converted_json
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+
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+def save_image(image: Union[Image.Image, str, np.ndarray], output_path: str) -> str:
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+ """
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+ 保存单个图像到指定路径
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+
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+ Args:
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+ image: 要保存的图像，可以是PIL Image对象、base64字符串或numpy数组
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+ output_path: 输出文件路径
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+
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+ Returns:
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+ 保存的图像文件路径
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+ """
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+ try:
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+ if isinstance(image, Image.Image):
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+ image.save(output_path)
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+ elif isinstance(image, str):
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+ # 处理base64字符串
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+ img_data = base64.b64decode(image)
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+ with open(output_path, 'wb') as f:
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+ f.write(img_data)
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+ elif isinstance(image, np.ndarray):
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+ # 处理numpy数组
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+ pil_image = Image.fromarray(image)
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+ pil_image.save(output_path)
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+ else:
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+ raise ValueError(f"Unsupported image type: {type(image)}")
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+
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+ # print(f"📷 Saved image: {output_path}")
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+ return str(output_path)
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+
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+ except Exception as e:
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+ print(f"❌ Error saving image {output_path}: {e}")
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+ return ""
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+
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+def save_output_images(output_images: Dict[str, Any], output_dir: str, output_filename: str) -> Dict[str, str]:
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+ """
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+ 保存API返回的输出图像
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+
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+ Args:
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+ output_images: 图像数组字典或PIL Image对象字典
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+ output_dir: 输出目录
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+ output_filename: 输出文件名前缀
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+
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+ Returns:
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+ 保存的图像文件路径字典
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+ """
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+ if not output_images:
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+ return {}
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+
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+ output_path = Path(output_dir).resolve()
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+ output_path.mkdir(parents=True, exist_ok=True)
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+
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+ saved_images = {}
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+
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+ for img_name, img_data in output_images.items():
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+ try:
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+ # 生成文件名
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+ img_filename = f"{output_filename}_{img_name}.jpg"
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+ img_path = output_path / img_filename
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+ save_image(img_data, str(img_path))
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+ saved_images[img_name] = str(img_path)
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+
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+ except Exception as e:
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+ print(f"❌ Error saving image {img_name}: {e}")
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+ print(f" Image data type: {type(img_data)}")
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+ if hasattr(img_data, 'shape'):
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+ print(f" Image shape: {img_data.shape}")
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+ traceback.print_exc()
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+
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+ return saved_images
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+
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+def save_markdown_content(markdown_data: Dict[str, Any], output_dir: str,
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+ filename: str, normalize_numbers: bool = True, key_text: str = 'text', key_images: str = 'images') -> str:
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+ """
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+ 保存Markdown内容，支持数字标准化
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+ """
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+ if not markdown_data:
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+ return ""
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+ output_path = Path(output_dir).resolve()
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+ output_path.mkdir(parents=True, exist_ok=True)
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+
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+ # 保存Markdown文本
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+ markdown_text = markdown_data.get(key_text, '')
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+
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+ # 数字标准化处理
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+ changes_count = 0
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+ if normalize_numbers and markdown_text:
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+ original_markdown_text = markdown_text
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+ markdown_text = normalize_markdown_table(markdown_text)
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+
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+ changes_count = len([1 for o, n in zip(original_markdown_text, markdown_text) if o != n])
|
|
|
+ # if changes_count > 0:
|
|
|
+ # print(f"🔧 Markdown中已标准化 {changes_count} 个字符（全角→半角）")
|
|
|
+
|
|
|
+ md_file_path = output_path / f"{filename}.md"
|
|
|
+ with open(md_file_path, 'w', encoding='utf-8') as f:
|
|
|
+ f.write(markdown_text)
|
|
|
+
|
|
|
+ # 如果启用了标准化且有变化，保存原始版本用于对比
|
|
|
+ if normalize_numbers and changes_count > 0:
|
|
|
+ original_output_path = output_path / f"{filename}_original.md"
|
|
|
+ with open(original_output_path, 'w', encoding='utf-8') as f:
|
|
|
+ f.write(original_markdown_text)
|
|
|
+
|
|
|
+ # 保存Markdown中的图像
|
|
|
+ markdown_images = markdown_data.get(key_images, {})
|
|
|
+ for img_path, img_data in markdown_images.items():
|
|
|
+ try:
|
|
|
+ full_img_path = output_path / img_path
|
|
|
+ full_img_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
+ save_image(img_data, str(full_img_path))
|
|
|
+
|
|
|
+ except Exception as e:
|
|
|
+ print(f"❌ Error saving Markdown image {img_path}: {e}")
|
|
|
+
|
|
|
+ return str(md_file_path)
|
