ocr_verify/ocr_validator_file_utils.py

import os
import base64
import pandas as pd
import numpy as np
from io import StringIO
from html import unescape
from typing import Dict, Optional, List
from PIL import Image, ImageDraw
from io import BytesIO
import cv2
import re

def load_css_styles(css_path: str = "styles.css") -> str:
    """加载CSS样式文件"""
    try:
        with open(css_path, 'r', encoding='utf-8') as f:
            return f.read()
    except Exception:
        # 返回基本样式
        return """
        .main > div { background-color: white !important; color: #333333 !important; }
        .stApp { background-color: white !important; }
        .block-container { background-color: white !important; color: #333333 !important; }
        """

def find_image_in_multiple_locations(img_src: str, json_path: str) -> Optional[str]:
    """
    在多个可能的位置查找图片文件
    """
    json_dir = os.path.dirname(json_path)
    
    # 可能的搜索路径
    search_paths = [
        # 相对于JSON文件的路径
        os.path.join(json_dir, img_src),
        # 相对于JSON文件父目录的路径
        os.path.join(os.path.dirname(json_dir), img_src),
        # imgs目录（常见的图片目录）
        os.path.join(json_dir, 'imgs', os.path.basename(img_src)),
        os.path.join(os.path.dirname(json_dir), 'imgs', os.path.basename(img_src)),
        # images目录
        os.path.join(json_dir, 'images', os.path.basename(img_src)),
        os.path.join(os.path.dirname(json_dir), 'images', os.path.basename(img_src)),
        # 同名目录
        os.path.join(json_dir, os.path.splitext(os.path.basename(json_path))[0], os.path.basename(img_src)),
    ]
    
    # 如果是绝对路径，也加入搜索
    if os.path.isabs(img_src):
        search_paths.insert(0, img_src)
    
    # 查找存在的文件
    for path in search_paths:
        if os.path.exists(path):
            return path
    
    return None


def process_html_images(html_content: str, json_path: str) -> str:
    """
    处理HTML内容中的图片引用，将本地图片转换为base64 - 增强版
    """
    import re
    
    # 匹配HTML图片标签: <img src="path" ... />
    img_pattern = r'<img\s+[^>]*src\s*=\s*["\']([^"\']+)["\'][^>]*/?>'
    
    def replace_html_image(match):
        full_tag = match.group(0)
        img_src = match.group(1)
        
        # 如果已经是base64或者网络链接，直接返回
        if img_src.startswith('data:image') or img_src.startswith('http'):
            return full_tag
        
        # 增强的图片查找
        full_img_path = find_image_in_multiple_locations(img_src, json_path)
        
        # 尝试转换为base64
        try:
            if full_img_path and os.path.exists(full_img_path):
                with open(full_img_path, 'rb') as img_file:
                    img_data = img_file.read()
                    
                # 获取文件扩展名确定MIME类型
                ext = os.path.splitext(full_img_path)[1].lower()
                mime_type = {
                    '.png': 'image/png',
                    '.jpg': 'image/jpeg',
                    '.jpeg': 'image/jpeg',
                    '.gif': 'image/gif',
                    '.bmp': 'image/bmp',
                    '.webp': 'image/webp'
                }.get(ext, 'image/jpeg')
                
                # 转换为base64
                img_base64 = base64.b64encode(img_data).decode('utf-8')
                data_url = f"data:{mime_type};base64,{img_base64}"
                
                # 替换src属性，保持其他属性不变
                updated_tag = re.sub(
                    r'src\s*=\s*["\'][^"\']+["\']',
                    f'src="{data_url}"',
                    full_tag
                )
                return updated_tag
            else:
                # 文件不存在，显示详细的错误信息
                search_info = f"搜索路径: {img_src}"
                if full_img_path:
                    search_info += f" -> {full_img_path}"
                
                error_content = f"""
                <div style="
                    color: #d32f2f; 
                    border: 2px dashed #d32f2f; 
                    padding: 10px; 
                    margin: 10px 0; 
                    border-radius: 5px;
                    background-color: #ffebee;
                    text-align: center;
                ">
                    <strong>🖼️ 图片无法加载</strong><br>
                    <small>原始路径: {img_src}</small><br>
                    <small>JSON文件: {os.path.basename(json_path)}</small><br>
                    <em>请检查图片文件是否存在</em>
                </div>
                """
                return error_content
        except Exception as e:
            # 转换失败，返回错误信息
            error_content = f"""
            <div style="
                color: #f57c00; 
                border: 2px dashed #f57c00; 
                padding: 10px; 
                margin: 10px 0; 
                border-radius: 5px;
                background-color: #fff3e0;
                text-align: center;
            ">
                <strong>⚠️ 图片处理失败</strong><br>
                <small>文件: {img_src}</small><br>
                <small>错误: {str(e)}</small>
            </div>
            """
            return error_content
    
    # 替换所有HTML图片标签
    processed_content = re.sub(img_pattern, replace_html_image, html_content, flags=re.IGNORECASE)
    return processed_content

def process_markdown_images(md_content: str, json_path: str) -> str:
    """
    处理Markdown中的图片引用，将本地图片转换为base64
    """
    import re
    
    # 匹配Markdown图片语法: ![alt](path)
    img_pattern = r'!\[([^\]]*)\]\(([^)]+)\)'
    
    def replace_image(match):
        alt_text = match.group(1)
        img_path = match.group(2)
        
        # 如果已经是base64或者网络链接，直接返回
        if img_path.startswith('data:image') or img_path.startswith('http'):
            return match.group(0)
        
        # 处理相对路径
        if not os.path.isabs(img_path):
            # 相对于JSON文件的路径
            json_dir = os.path.dirname(json_path)
            full_img_path = os.path.join(json_dir, img_path)
        else:
            full_img_path = img_path
        
        # 尝试转换为base64
        try:
            if os.path.exists(full_img_path):
                with open(full_img_path, 'rb') as img_file:
                    img_data = img_file.read()
                    
                # 获取文件扩展名确定MIME类型
                ext = os.path.splitext(full_img_path)[1].lower()
                mime_type = {
                    '.png': 'image/png',
                    '.jpg': 'image/jpeg',
                    '.jpeg': 'image/jpeg',
                    '.gif': 'image/gif',
                    '.bmp': 'image/bmp',
                    '.webp': 'image/webp'
                }.get(ext, 'image/jpeg')
                
                # 转换为base64
                img_base64 = base64.b64encode(img_data).decode('utf-8')
                data_url = f"data:{mime_type};base64,{img_base64}"
                
                return f'![{alt_text}]({data_url})'
            else:
                # 文件不存在，返回原始链接但添加警告
                return f'![{alt_text} (文件不存在)]({img_path})'
        except Exception as e:
            # 转换失败，返回原始链接
            return f'![{alt_text} (加载失败)]({img_path})'
    
    # 替换所有图片引用
    processed_content = re.sub(img_pattern, replace_image, md_content)
    return processed_content

def process_all_images_in_content(content: str, json_path: str) -> str:
    """
    处理内容中的所有图片引用（包括Markdown和HTML格式）
    """
    # 先处理HTML图片
    content = process_html_images(content, json_path)
    # 再处理Markdown图片
    content = process_markdown_images(content, json_path)
    return content


def draw_bbox_on_image(image: Image.Image, bbox: List[int], color: str = "red", width: int = 3) -> Image.Image:
    """在图片上绘制bbox框"""
    img_copy = image.copy()
    draw = ImageDraw.Draw(img_copy)
    
    x1, y1, x2, y2 = bbox
    
    # 绘制矩形框
    draw.rectangle([x1, y1, x2, y2], outline=color, width=width)
    
    # 添加半透明填充
    overlay = Image.new('RGBA', img_copy.size, (0, 0, 0, 0))
    overlay_draw = ImageDraw.Draw(overlay)
    
    color_map = {
        "red": (255, 0, 0, 30),
        "blue": (0, 0, 255, 30),
        "green": (0, 255, 0, 30)
    }
    fill_color = color_map.get(color, (255, 255, 0, 30))
    
    overlay_draw.rectangle([x1, y1, x2, y2], fill=fill_color)
    img_copy = Image.alpha_composite(img_copy.convert('RGBA'), overlay).convert('RGB')
    
    return img_copy


def convert_html_table_to_markdown(content: str) -> str:
    """将HTML表格转换为Markdown表格格式 - 支持横向滚动的增强版本"""
    def replace_table(match):
        table_html = match.group(0)
        
        # 提取所有行
        rows = re.findall(r'<tr[^>]*>(.*?)</tr>', table_html, re.DOTALL | re.IGNORECASE)
        if not rows:
            return table_html
        
        markdown_rows = []
        max_cols = 0
        
        # 处理所有行，找出最大列数
        processed_rows = []
        for row in rows:
            # 提取单元格，支持 th 和 td
            cells = re.findall(r'<t[hd][^>]*>(.*?)</t[hd]>', row, re.DOTALL | re.IGNORECASE)
            if cells:
                clean_cells = []
                for cell in cells:
                    cell_text = re.sub(r'<[^>]+>', '', cell).strip()
                    cell_text = unescape(cell_text)
                    # 限制单元格长度，避免表格过宽
                    if len(cell_text) > 30:
                        cell_text = cell_text[:27] + "..."
                    clean_cells.append(cell_text or " ")  # 空单元格用空格替代
                
                processed_rows.append(clean_cells)
                max_cols = max(max_cols, len(clean_cells))
        
        # 统一所有行的列数
        for i, row_cells in enumerate(processed_rows):
            while len(row_cells) < max_cols:
                row_cells.append(" ")
            
            # 构建Markdown行
            markdown_row = '| ' + ' | '.join(row_cells) + ' |'
            markdown_rows.append(markdown_row)
            
            # 在第一行后添加分隔符
            if i == 0:
                separator = '| ' + ' | '.join(['---'] * max_cols) + ' |'
                markdown_rows.append(separator)
        
        # 添加滚动提示
        if max_cols > 8:
            scroll_note = "\n> 📋 **提示**: 此表格列数较多，在某些视图中可能需要横向滚动查看完整内容。\n"
            return scroll_note + '\n'.join(markdown_rows) if markdown_rows else table_html
        
        return '\n'.join(markdown_rows) if markdown_rows else table_html
    
    # 替换所有HTML表格
    converted = re.sub(r'<table[^>]*>.*?</table>', replace_table, content, flags=re.DOTALL | re.IGNORECASE)
    return converted


def parse_html_tables(html_content: str) -> List[pd.DataFrame]:
    """解析HTML内容中的表格为DataFrame列表"""
    try:
        tables = pd.read_html(StringIO(html_content))
        return tables if tables else []
    except Exception:
        return []


def create_dynamic_css(config: Dict, font_size_key: str, height: int) -> str:
    """根据配置动态创建CSS样式"""
    colors = config['styles']['colors']
    font_size = config['styles']['font_sizes'][font_size_key]
    
    return f"""
    <style>
    .dynamic-content {{
        height: {height}px;
        font-size: {font_size}px !important;
        line-height: 1.4;
        background-color: {colors['background']} !important;
        color: {colors['text']} !important;
        border: 1px solid #ddd;
        padding: 10px;
        border-radius: 5px;
    }}
    
    .highlight-selected {{
        background-color: {colors['success']} !important;
        color: white !important;
    }}
    
    .highlight-error {{
        background-color: {colors['error']} !important;
        color: white !important;
    }}
    </style>
    """


def export_tables_to_excel(tables: List[pd.DataFrame], filename: str = "ocr_tables.xlsx") -> BytesIO:
    """导出表格数据到Excel"""
    output = BytesIO()
    with pd.ExcelWriter(output, engine='openpyxl') as writer:
        for i, table in enumerate(tables):
            table.to_excel(writer, sheet_name=f'Table_{i+1}', index=False)
    return output


def get_table_statistics(tables: List[pd.DataFrame]) -> List[Dict]:
    """获取表格统计信息"""
    stats = []
    for i, table in enumerate(tables):
        numeric_cols = len(table.select_dtypes(include=[np.number]).columns)
        stats.append({
            'table_index': i + 1,
            'rows': len(table),
            'columns': len(table.columns),
            'numeric_columns': numeric_cols
        })
    return stats


def detect_image_orientation_by_opencv(image_path: str) -> Dict:
    """
    使用OpenCV的文本检测来判断图片方向
    """
    try:
        # 读取图像
        image = cv2.imread(image_path)
        if image is None:
            raise ValueError("无法读取图像文件")
        
        height, width = image.shape[:2]
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # 使用EAST文本检测器或其他方法
        # 这里使用简单的边缘检测和轮廓分析
        edges = cv2.Canny(gray, 50, 150, apertureSize=3)
        
        # 检测直线
        lines = cv2.HoughLines(edges, 1, np.pi/180, threshold=100)
        
        if lines is None:
            return {
                'detected_angle': 0.0,
                'confidence': 0.0,
                'method': 'opencv_analysis',
                'message': '未检测到足够的直线特征'
            }
        
        # 分析直线角度
        angles = []
        for rho, theta in lines[:, 0]:
            angle = theta * 180 / np.pi
            # 将角度标准化到0-180度
            if angle > 90:
                angle = angle - 180
            angles.append(angle)
        
        # 统计主要角度
        angle_hist = np.histogram(angles, bins=36, range=(-90, 90))[0]
        dominant_angle_idx = np.argmax(angle_hist)
        dominant_angle = -90 + dominant_angle_idx * 5  # 每个bin 5度
        
        # 将角度映射到标准旋转角度
        if -22.5 <= dominant_angle <= 22.5:
            detected_angle = 0.0
        elif 22.5 < dominant_angle <= 67.5:
            detected_angle = 270.0
        elif 67.5 < dominant_angle <= 90 or -90 <= dominant_angle < -67.5:
            detected_angle = 90.0
        else:
            detected_angle = 180.0
        
        confidence = angle_hist[dominant_angle_idx] / len(lines) if len(lines) > 0 else 0.0
        
        return {
            'detected_angle': detected_angle,
            'confidence': min(1.0, confidence),
            'method': 'opencv_analysis',
            'line_count': len(lines),
            'dominant_angle': dominant_angle,
            'message': f'基于{len(lines)}条直线检测到旋转角度: {detected_angle}°'
        }
        
    except Exception as e:
        return {
            'detected_angle': 0.0,
            'confidence': 0.0,
            'method': 'opencv_analysis',
            'error': str(e),
            'message': f'OpenCV检测过程中发生错误: {str(e)}'
        }