b997f9355a
Problem: - Backend failed to start with ModuleNotFoundError for torch module - torch was imported as hard dependency but not in requirements.txt - Project uses PaddlePaddle which has its own CUDA implementation Changes: - Make torch import optional with try/except in ocr_service.py - Make torch import optional in pp_structure_enhanced.py - Add cleanup_gpu_memory() method using PaddlePaddle's memory management - Add check_gpu_memory() method to monitor available GPU memory - Use paddle.device.cuda.empty_cache() for GPU cleanup - Use torch.cuda only if TORCH_AVAILABLE flag is True - Add cleanup calls after OCR processing to prevent OOM errors - Add memory checks before GPU-intensive operations Benefits: - Backend can start without torch installed - GPU memory is properly managed using PaddlePaddle - Optional torch support provides additional memory monitoring - Prevents GPU OOM errors during document processing 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
431 lines
16 KiB
Python
431 lines
16 KiB
Python
"""
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Enhanced PP-StructureV3 processing with full element extraction
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This module provides enhanced PP-StructureV3 processing that extracts all
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23 element types with their bbox coordinates and reading order.
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"""
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import logging
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Any
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import json
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import gc
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# Optional torch import for additional GPU memory management
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try:
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import torch
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TORCH_AVAILABLE = True
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except ImportError:
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TORCH_AVAILABLE = False
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import paddle
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from paddleocr import PPStructureV3
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from app.models.unified_document import ElementType
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logger = logging.getLogger(__name__)
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class PPStructureEnhanced:
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"""
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Enhanced PP-StructureV3 processor that extracts all available element types
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and structure information from parsing_res_list.
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"""
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# Mapping from PP-StructureV3 types to our ElementType
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ELEMENT_TYPE_MAPPING = {
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'title': ElementType.TITLE,
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'text': ElementType.TEXT,
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'paragraph': ElementType.PARAGRAPH,
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'figure': ElementType.FIGURE,
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'figure_caption': ElementType.CAPTION,
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'table': ElementType.TABLE,
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'table_caption': ElementType.TABLE_CAPTION,
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'header': ElementType.HEADER,
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'footer': ElementType.FOOTER,
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'reference': ElementType.REFERENCE,
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'equation': ElementType.EQUATION,
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'formula': ElementType.FORMULA,
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'list-item': ElementType.LIST_ITEM,
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'list': ElementType.LIST,
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'code': ElementType.CODE,
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'footnote': ElementType.FOOTNOTE,
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'page-number': ElementType.PAGE_NUMBER,
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'watermark': ElementType.WATERMARK,
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'signature': ElementType.SIGNATURE,
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'stamp': ElementType.STAMP,
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'logo': ElementType.LOGO,
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'barcode': ElementType.BARCODE,
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'qr-code': ElementType.QR_CODE,
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# Default fallback
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'image': ElementType.IMAGE,
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'chart': ElementType.CHART,
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'diagram': ElementType.DIAGRAM,
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}
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def __init__(self, structure_engine: PPStructureV3):
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"""
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Initialize with existing PP-StructureV3 engine.
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Args:
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structure_engine: Initialized PPStructureV3 instance
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"""
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self.structure_engine = structure_engine
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def analyze_with_full_structure(
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self,
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image_path: Path,
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output_dir: Optional[Path] = None,
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current_page: int = 0
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) -> Dict[str, Any]:
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"""
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Analyze document with full PP-StructureV3 capabilities.
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Args:
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image_path: Path to image file
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output_dir: Optional output directory for saving extracted content
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current_page: Current page number (0-based)
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Returns:
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Dictionary with complete structure information including:
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- elements: List of all detected elements with types and bbox
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- reading_order: Reading order indices
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- images: Extracted images with metadata
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- tables: Extracted tables with structure
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"""
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try:
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logger.info(f"Enhanced PP-StructureV3 analysis on {image_path.name}")
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# Perform structure analysis
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results = self.structure_engine.predict(str(image_path))
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all_elements = []
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all_images = []
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all_tables = []
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# Process each page result
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for page_idx, page_result in enumerate(results):
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# Try to access parsing_res_list (the complete structure)
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parsing_res_list = None
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# Method 1: Direct access to json attribute
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if hasattr(page_result, 'json'):
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result_json = page_result.json
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if isinstance(result_json, dict) and 'parsing_res_list' in result_json:
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parsing_res_list = result_json['parsing_res_list']
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logger.info(f"Found parsing_res_list with {len(parsing_res_list)} elements")
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# Method 2: Try to access as attribute
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elif hasattr(page_result, 'parsing_res_list'):
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parsing_res_list = page_result.parsing_res_list
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logger.info(f"Found parsing_res_list attribute with {len(parsing_res_list)} elements")
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# Method 3: Check if result has to_dict method
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elif hasattr(page_result, 'to_dict'):
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result_dict = page_result.to_dict()
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if 'parsing_res_list' in result_dict:
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parsing_res_list = result_dict['parsing_res_list']
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logger.info(f"Found parsing_res_list in to_dict with {len(parsing_res_list)} elements")
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# Process parsing_res_list if found
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if parsing_res_list:
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elements = self._process_parsing_res_list(
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parsing_res_list, current_page, output_dir
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)
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all_elements.extend(elements)
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# Extract tables and images from elements
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for elem in elements:
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if elem['type'] == ElementType.TABLE:
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all_tables.append(elem)
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elif elem['type'] in [ElementType.IMAGE, ElementType.FIGURE]:
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all_images.append(elem)
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else:
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# Fallback to markdown if parsing_res_list not available
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logger.warning("parsing_res_list not found, falling back to markdown")
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elements = self._process_markdown_fallback(
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page_result, current_page, output_dir
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)
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all_elements.extend(elements)
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# Create reading order based on element positions
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reading_order = self._determine_reading_order(all_elements)
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return {
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'elements': all_elements,
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'total_elements': len(all_elements),
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'reading_order': reading_order,
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'tables': all_tables,
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'images': all_images,
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'element_types': self._count_element_types(all_elements),
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'has_parsing_res_list': parsing_res_list is not None
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}
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except Exception as e:
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logger.error(f"Enhanced PP-StructureV3 analysis error: {e}")
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import traceback
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traceback.print_exc()
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# Clean up GPU memory on error
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try:
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if TORCH_AVAILABLE and torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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if paddle.device.is_compiled_with_cuda():
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paddle.device.cuda.empty_cache()
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gc.collect()
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except:
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pass # Ignore cleanup errors
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return {
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'elements': [],
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'total_elements': 0,
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'reading_order': [],
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'tables': [],
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'images': [],
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'element_types': {},
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'has_parsing_res_list': False,
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'error': str(e)
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}
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def _process_parsing_res_list(
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self,
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parsing_res_list: List[Dict],
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current_page: int,
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output_dir: Optional[Path]
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) -> List[Dict[str, Any]]:
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"""
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Process parsing_res_list to extract all elements.
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Args:
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parsing_res_list: List of parsed elements from PP-StructureV3
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current_page: Current page number
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output_dir: Optional output directory
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Returns:
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List of processed elements with normalized structure
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"""
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elements = []
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for idx, item in enumerate(parsing_res_list):
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# Extract element type
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element_type = item.get('type', 'text').lower()
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mapped_type = self.ELEMENT_TYPE_MAPPING.get(
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element_type, ElementType.TEXT
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)
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# Extract bbox (layout_bbox has the precise coordinates)
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layout_bbox = item.get('layout_bbox', [])
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if not layout_bbox and 'bbox' in item:
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layout_bbox = item['bbox']
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# Ensure bbox has 4 values
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if len(layout_bbox) >= 4:
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bbox = layout_bbox[:4] # [x1, y1, x2, y2]
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else:
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bbox = [0, 0, 0, 0] # Default if bbox missing
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# Extract content
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content = item.get('content', '')
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if not content and 'res' in item:
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# Some elements have content in 'res' field
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res = item.get('res', {})
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if isinstance(res, dict):
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content = res.get('content', '') or res.get('text', '')
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elif isinstance(res, str):
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content = res
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# Create element
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element = {
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'element_id': f"pp3_{current_page}_{idx}",
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'type': mapped_type,
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'original_type': element_type,
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'content': content,
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'page': current_page,
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'bbox': bbox, # [x1, y1, x2, y2]
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'index': idx, # Original index in reading order
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'confidence': item.get('score', 1.0)
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}
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# Special handling for tables
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if mapped_type == ElementType.TABLE:
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# Extract table structure if available
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if 'res' in item and isinstance(item['res'], dict):
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html_content = item['res'].get('html', '')
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if html_content:
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element['html'] = html_content
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element['extracted_text'] = self._extract_text_from_html(html_content)
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# Special handling for images/figures
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elif mapped_type in [ElementType.IMAGE, ElementType.FIGURE]:
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# Save image if path provided
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if 'img_path' in item and output_dir:
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self._save_image(item['img_path'], output_dir, element['element_id'])
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element['img_path'] = item['img_path']
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# Add any additional metadata
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if 'metadata' in item:
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element['metadata'] = item['metadata']
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elements.append(element)
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logger.debug(f"Processed element {idx}: type={mapped_type}, bbox={bbox}")
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return elements
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def _process_markdown_fallback(
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self,
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page_result: Any,
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current_page: int,
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output_dir: Optional[Path]
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) -> List[Dict[str, Any]]:
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"""
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Fallback to markdown processing if parsing_res_list not available.
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Args:
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page_result: PP-StructureV3 page result
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current_page: Current page number
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output_dir: Optional output directory
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Returns:
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List of elements extracted from markdown
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"""
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elements = []
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# Extract from markdown if available
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if hasattr(page_result, 'markdown'):
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markdown_dict = page_result.markdown
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if isinstance(markdown_dict, dict):
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# Extract markdown texts
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markdown_texts = markdown_dict.get('markdown_texts', '')
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if markdown_texts:
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# Detect if it's a table
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is_table = '<table' in markdown_texts.lower()
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element = {
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'element_id': f"md_{current_page}_0",
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'type': ElementType.TABLE if is_table else ElementType.TEXT,
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'content': markdown_texts,
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'page': current_page,
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'bbox': [0, 0, 0, 0], # No bbox in markdown
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'index': 0,
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'from_markdown': True
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}
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if is_table:
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element['extracted_text'] = self._extract_text_from_html(markdown_texts)
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elements.append(element)
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# Process images
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markdown_images = markdown_dict.get('markdown_images', {})
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for img_idx, (img_path, img_obj) in enumerate(markdown_images.items()):
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# Save image
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if output_dir and hasattr(img_obj, 'save'):
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self._save_pil_image(img_obj, output_dir, f"md_img_{current_page}_{img_idx}")
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# Try to extract bbox from filename
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bbox = self._extract_bbox_from_filename(img_path)
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element = {
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'element_id': f"md_img_{current_page}_{img_idx}",
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'type': ElementType.IMAGE,
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'content': img_path,
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'page': current_page,
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'bbox': bbox,
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'index': img_idx + 1,
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'from_markdown': True
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}
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elements.append(element)
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return elements
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def _determine_reading_order(self, elements: List[Dict]) -> List[int]:
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"""
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Determine reading order based on element positions.
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Args:
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elements: List of elements with bbox
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Returns:
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List of indices representing reading order
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"""
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if not elements:
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return []
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# If elements have original indices, use them
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if all('index' in elem for elem in elements):
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# Sort by original index
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indexed_elements = [(i, elem['index']) for i, elem in enumerate(elements)]
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indexed_elements.sort(key=lambda x: x[1])
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return [i for i, _ in indexed_elements]
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# Otherwise, sort by position (top to bottom, left to right)
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indexed_elements = []
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for i, elem in enumerate(elements):
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bbox = elem.get('bbox', [0, 0, 0, 0])
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if len(bbox) >= 2:
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# Use top-left corner for sorting
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indexed_elements.append((i, bbox[1], bbox[0])) # (index, y, x)
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else:
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indexed_elements.append((i, 0, 0))
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# Sort by y first (top to bottom), then x (left to right)
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indexed_elements.sort(key=lambda x: (x[1], x[2]))
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return [i for i, _, _ in indexed_elements]
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def _count_element_types(self, elements: List[Dict]) -> Dict[str, int]:
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"""
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Count occurrences of each element type.
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Args:
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elements: List of elements
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Returns:
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Dictionary with element type counts
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"""
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type_counts = {}
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for elem in elements:
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elem_type = elem.get('type', ElementType.TEXT)
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type_counts[elem_type] = type_counts.get(elem_type, 0) + 1
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return type_counts
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def _extract_text_from_html(self, html: str) -> str:
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"""Extract plain text from HTML content."""
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try:
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from bs4 import BeautifulSoup
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soup = BeautifulSoup(html, 'html.parser')
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return soup.get_text(separator=' ', strip=True)
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except:
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# Fallback: just remove HTML tags
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import re
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text = re.sub(r'<[^>]+>', ' ', html)
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text = re.sub(r'\s+', ' ', text)
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return text.strip()
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def _extract_bbox_from_filename(self, filename: str) -> List[int]:
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"""Extract bbox from filename if it contains coordinate information."""
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import re
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match = re.search(r'box_(\d+)_(\d+)_(\d+)_(\d+)', filename)
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if match:
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return list(map(int, match.groups()))
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return [0, 0, 0, 0]
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def _save_image(self, img_path: str, output_dir: Path, element_id: str):
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"""Save image file to output directory."""
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try:
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# Implementation depends on how images are provided
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pass
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except Exception as e:
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logger.warning(f"Failed to save image {img_path}: {e}")
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def _save_pil_image(self, img_obj, output_dir: Path, element_id: str):
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"""Save PIL image object to output directory."""
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try:
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img_dir = output_dir / "imgs"
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img_dir.mkdir(parents=True, exist_ok=True)
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img_path = img_dir / f"{element_id}.png"
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img_obj.save(str(img_path))
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logger.info(f"Saved image to {img_path}")
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except Exception as e:
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logger.warning(f"Failed to save PIL image: {e}") |