PaddleX/zhch/ppstructurev3_utils.py

"""PPStructureV3公共函数"""
import json
import traceback
import warnings
import base64
from pathlib import Path
from PIL import Image
from typing import List, Dict, Any, Union
import numpy as np

from utils import (
    load_images_from_pdf,
    normalize_markdown_table
)

def convert_pdf_to_images(pdf_file: str, output_dir: str | None = None, dpi: int = 200) -> List[str]:
    """
    将PDF转换为图像文件
    
    Args:
        pdf_file: PDF文件路径
        output_dir: 输出目录
        dpi: 图像分辨率
        
    Returns:
        生成的图像文件路径列表
    """
    pdf_path = Path(pdf_file)
    if not pdf_path.exists() or pdf_path.suffix.lower() != '.pdf':
        print(f"❌ Invalid PDF file: {pdf_path}")
        return []

    # 如果没有指定输出目录，使用PDF同名目录
    if output_dir is None:
        output_path = pdf_path.parent / f"{pdf_path.stem}"
    else:
        output_path = Path(output_dir) / f"{pdf_path.stem}"
    output_path = output_path.resolve()
    output_path.mkdir(parents=True, exist_ok=True)

    try:
        # 使用doc_utils中的函数加载PDF图像
        images = load_images_from_pdf(str(pdf_path), dpi=dpi)
        
        image_paths = []
        for i, image in enumerate(images):
            # 生成图像文件名
            image_filename = f"{pdf_path.stem}_page_{i+1:03d}.png"
            image_path = output_path / image_filename

            # 保存图像
            image.save(str(image_path))
            image_paths.append(str(image_path))
            
        print(f"✅ Converted {len(images)} pages from {pdf_path.name} to images")
        return image_paths
        
    except Exception as e:
        print(f"❌ Error converting PDF {pdf_path}: {e}")
        traceback.print_exc()
        return []

def convert_pruned_result_to_json(pruned_result: Dict[str, Any], 
                              input_image_path: str, 
                              output_dir: str, 
                              filename: str,
                              normalize_numbers: bool = True) -> tuple[str, Dict[str, Any]]:
    """
    将API返回结果转换为标准JSON格式，并支持数字标准化
    """
    if not pruned_result:
        return "", {}
    
    # 构造标准格式的JSON
    converted_json = {
        "input_path": input_image_path,
        "page_index": None,
        "model_settings": pruned_result.get('model_settings', {}),
        "parsing_res_list": pruned_result.get('parsing_res_list', []),
        "doc_preprocessor_res": {
            "input_path": None,
            "page_index": None,
            "model_settings": pruned_result.get('doc_preprocessor_res', {}).get('model_settings', {}),
            "angle": pruned_result.get('doc_preprocessor_res', {}).get('angle', 0)
        },
        "layout_det_res": {
            "input_path": None,
            "page_index": None,
            "boxes": pruned_result.get('layout_det_res', {}).get('boxes', [])
        },
        "overall_ocr_res": {
            "input_path": None,
            "page_index": None,
            "model_settings": pruned_result.get('overall_ocr_res', {}).get('model_settings', {}),
            "dt_polys": pruned_result.get('overall_ocr_res', {}).get('dt_polys', []),
            "text_det_params": pruned_result.get('overall_ocr_res', {}).get('text_det_params', {}),
            "text_type": pruned_result.get('overall_ocr_res', {}).get('text_type', 'general'),
            "textline_orientation_angles": pruned_result.get('overall_ocr_res', {}).get('textline_orientation_angles', []),
            "text_rec_score_thresh": pruned_result.get('overall_ocr_res', {}).get('text_rec_score_thresh', 0.0),
            "return_word_box": pruned_result.get('overall_ocr_res', {}).get('return_word_box', False),
            "rec_texts": pruned_result.get('overall_ocr_res', {}).get('rec_texts', []),
            "rec_scores": pruned_result.get('overall_ocr_res', {}).get('rec_scores', []),
            "rec_polys": pruned_result.get('overall_ocr_res', {}).get('rec_polys', []),
            "rec_boxes": pruned_result.get('overall_ocr_res', {}).get('rec_boxes', [])
        },
        "table_res_list": pruned_result.get('table_res_list', [])
    }
    
    # 数字标准化处理
    original_json = converted_json.copy()
    changes_count = 0
    
    if normalize_numbers:
        # 1. 标准化 parsing_res_list 中的文本内容
        for item in converted_json.get('parsing_res_list', []):
            if 'block_content' in item:
                original_content = item['block_content']
                normalized_content = original_content
                # 根据block_label类型选择标准化方法
                if item.get('block_label') == 'table':
                    normalized_content = normalize_markdown_table(original_content)
                # else:
                #     normalized_content = normalize_financial_numbers(original_content)
                
                if original_content != normalized_content:
                    item['block_content'] = normalized_content
                    changes_count += len([1 for o, n in zip(original_content, normalized_content) if o != n])
        
        # 2. 标准化 table_res_list 中的HTML表格
        for table_item in converted_json.get('table_res_list', []):
            if 'pred_html' in table_item:
                original_html = table_item['pred_html']
                normalized_html = normalize_markdown_table(original_html)
                
                if original_html != normalized_html:
                    table_item['pred_html'] = normalized_html
                    changes_count += len([1 for o, n in zip(original_html, normalized_html) if o != n])

        # 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）
               # 统计表格数量
        parsing_res_tables_count = 0
        table_res_list_count = 0
        if 'parsing_res_list' in converted_json:
            parsing_res_tables_count = len([item for item in converted_json['parsing_res_list'] 
                                          if 'block_label' in item and item['block_label'] == 'table'])
        if 'table_res_list' in converted_json:
            table_res_list_count = len(converted_json["table_res_list"])
        table_consistency_fixed = False
        if parsing_res_tables_count != table_res_list_count:
            warnings.warn(f"⚠️ Warning: {filename} Table count mismatch - parsing_res_list has {parsing_res_tables_count} tables, "
                          f"but table_res_list has {table_res_list_count} tables.")
            table_consistency_fixed = True
            # 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
            # 但由于缺乏具体规则，暂时只做统计和警告

        # 3. 标准化 overall_ocr_res 中的识别文本
        # ocr_res = converted_json.get('overall_ocr_res', {})
        # if 'rec_texts' in ocr_res:
        #     original_texts = ocr_res['rec_texts'][:]
        #     normalized_texts = []
            
        #     for text in original_texts:
        #         normalized_text = normalize_financial_numbers(text)
        #         normalized_texts.append(normalized_text)
        #         if text != normalized_text:
        #             changes_count += len([1 for o, n in zip(text, normalized_text) if o != n])
            
        #     ocr_res['rec_texts'] = normalized_texts
        
        # 添加标准化处理信息
        converted_json['processing_info'] = {
            "normalize_numbers": normalize_numbers,
            "changes_applied": changes_count > 0,
            "character_changes_count": changes_count,
            "parsing_res_tables_count": parsing_res_tables_count,
            "table_res_list_count": table_res_list_count,
            "table_consistency_fixed": table_consistency_fixed
        }
        
        # if changes_count > 0:
        #     print(f"🔧 已标准化 {changes_count} 个字符（全角→半角）")
    else:
        converted_json['processing_info'] = {
            "normalize_numbers": False,
            "changes_applied": False,
            "character_changes_count": 0
        }
    
    # 保存JSON文件
    output_path = Path(output_dir).resolve()
    output_path.mkdir(parents=True, exist_ok=True)
    
    json_file_path = output_path / f"{filename}.json"
    with open(json_file_path, 'w', encoding='utf-8') as f:
        json.dump(converted_json, f, ensure_ascii=False, indent=2)
    
    # 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.json"
        with open(original_output_path, 'w', encoding='utf-8') as f:
            json.dump(original_json, f, ensure_ascii=False, indent=2)
    
    return str(output_path), converted_json

def save_image(image: Union[Image.Image, str, np.ndarray], output_path: str) -> str:
    """
    保存单个图像到指定路径

    Args:
        image: 要保存的图像，可以是PIL Image对象、base64字符串或numpy数组
        output_path: 输出文件路径

    Returns:
        保存的图像文件路径
    """
    try:
        if isinstance(image, Image.Image):
            image.save(output_path)
        elif isinstance(image, str):
            # 处理base64字符串
            img_data = base64.b64decode(image)
            with open(output_path, 'wb') as f:
                f.write(img_data)
        elif isinstance(image, np.ndarray):
            # 处理numpy数组
            pil_image = Image.fromarray(image)
            pil_image.save(output_path)
        else:
            raise ValueError(f"Unsupported image type: {type(image)}")

        # print(f"📷 Saved image: {output_path}")
        return str(output_path)

    except Exception as e:
        print(f"❌ Error saving image {output_path}: {e}")
        return ""

def save_output_images(output_images: Dict[str, Any], output_dir: str, output_filename: str) -> Dict[str, str]:
    """
    保存API返回的输出图像
    
    Args:
        output_images: 图像数组字典或PIL Image对象字典
        output_dir: 输出目录
        output_filename: 输出文件名前缀
        
    Returns:
        保存的图像文件路径字典
    """
    if not output_images:
        return {}
    
    output_path = Path(output_dir).resolve()
    output_path.mkdir(parents=True, exist_ok=True)
    
    saved_images = {}
    
    for img_name, img_data in output_images.items():
        try:
            # 生成文件名
            img_filename = f"{output_filename}_{img_name}.jpg"
            img_path = output_path / img_filename
            save_image(img_data, str(img_path))
            saved_images[img_name] = str(img_path)
            
        except Exception as e:
            print(f"❌ Error saving image {img_name}: {e}")
            print(f"   Image data type: {type(img_data)}")
            if hasattr(img_data, 'shape'):
                print(f"   Image shape: {img_data.shape}")
            traceback.print_exc()
    
    return saved_images

def save_markdown_content(markdown_data: Dict[str, Any], output_dir: str, 
                         filename: str, normalize_numbers: bool = True, key_text: str = 'text', key_images: str = 'images') -> str:
    """
    保存Markdown内容，支持数字标准化
    """
    if not markdown_data:
        return ""
    output_path = Path(output_dir).resolve()
    output_path.mkdir(parents=True, exist_ok=True)
    
    # 保存Markdown文本
    markdown_text = markdown_data.get(key_text, '')
    
    # 数字标准化处理
    changes_count = 0
    if normalize_numbers and markdown_text:
        original_markdown_text = markdown_text
        markdown_text = normalize_markdown_table(markdown_text)
        
        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)