95ae1f1bdb
- Add TableDetectionSelector component for wired/wireless/region detection - Add CV-based table line detector module (disabled due to poor performance) - Add scan artifact removal preprocessing step (removes faint horizontal lines) - Add PreprocessingConfig schema with remove_scan_artifacts option - Update frontend PreprocessingSettings with scan artifact toggle - Integrate table detection config into ProcessingPage - Archive extract-table-cell-boxes proposal 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
1051 lines
47 KiB
Python
1051 lines
47 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, TYPE_CHECKING
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import json
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import gc
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# Import ScalingInfo for type checking (avoid circular imports at runtime)
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if TYPE_CHECKING:
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from app.services.layout_preprocessing_service import ScalingInfo
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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 PIL import Image
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import numpy as np
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import cv2
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from app.models.unified_document import ElementType
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from app.core.config import settings
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from app.services.memory_manager import prediction_context
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from app.services.cv_table_detector import CVTableDetector
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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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'paragraph_title': ElementType.TITLE, # PP-StructureV3 block_label
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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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'seal': ElementType.STAMP, # PP-StructureV3 may use 'seal' label
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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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preprocessed_image: Optional[Image.Image] = None,
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scaling_info: Optional['ScalingInfo'] = None,
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save_visualization: bool = False,
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use_cv_table_detection: bool = False
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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 original image file (used for cropping extracted images)
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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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preprocessed_image: Optional preprocessed PIL Image for layout detection.
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If provided, this is used for PP-Structure prediction,
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but original image_path is still used for cropping images.
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scaling_info: Optional ScalingInfo from preprocessing. If image was scaled
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for layout detection, all bbox coordinates will be scaled back
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to original image coordinates for proper cropping.
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save_visualization: If True, save detection visualization images
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(layout_det_res, layout_order_res, overall_ocr_res, etc.)
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use_cv_table_detection: If True, use CV-based line detection for wired tables
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instead of ML-based cell detection (RT-DETR-L)
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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 (in original coords)
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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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- visualization_dir: Path to visualization images (if save_visualization=True)
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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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if preprocessed_image:
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logger.info("Using preprocessed image for layout detection")
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# Perform structure analysis with semaphore control
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# This prevents OOM errors from multiple simultaneous predictions
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with prediction_context(timeout=settings.service_acquire_timeout_seconds) as acquired:
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if not acquired:
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logger.error("Failed to acquire prediction slot (timeout), returning empty result")
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return {
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'has_parsing_res_list': False,
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'elements': [],
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'total_elements': 0,
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'images': [],
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'tables': [],
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'element_types': {},
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'error': 'Prediction slot timeout'
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}
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# Use preprocessed image if provided, otherwise use original path
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if preprocessed_image is not None:
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# Convert PIL to numpy array (BGR format for PP-Structure)
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predict_input = np.array(preprocessed_image)
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if len(predict_input.shape) == 3 and predict_input.shape[2] == 3:
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# Convert RGB to BGR
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predict_input = predict_input[:, :, ::-1]
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results = self.structure_engine.predict(predict_input)
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else:
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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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visualization_dir = None
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# Process each page result
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for page_idx, page_result in enumerate(results):
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# Save visualization images if requested
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if save_visualization and output_dir and hasattr(page_result, 'save_to_img'):
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try:
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vis_dir = output_dir / 'visualization'
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vis_dir.mkdir(parents=True, exist_ok=True)
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page_result.save_to_img(str(vis_dir))
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visualization_dir = vis_dir
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logger.info(f"Saved visualization images to {vis_dir}")
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except Exception as e:
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logger.warning(f"Failed to save visualization images: {e}")
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# Try to access parsing_res_list and table_res_list (the complete structure)
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parsing_res_list = None
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table_res_list = None
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result_dict = None
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# Method 1: Direct access to json attribute (check both top-level and res)
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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):
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result_dict = result_json
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# Check top-level
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if '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 at top level with {len(parsing_res_list)} elements")
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# Check inside 'res' (new structure in paddlex)
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elif 'res' in result_json and isinstance(result_json['res'], dict):
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result_dict = result_json['res']
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if 'parsing_res_list' in result_json['res']:
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parsing_res_list = result_json['res']['parsing_res_list']
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logger.info(f"Found parsing_res_list inside 'res' with {len(parsing_res_list)} elements")
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# Method 2: Try direct dict access (LayoutParsingResultV2 inherits from dict)
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elif isinstance(page_result, dict):
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result_dict = page_result
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if 'parsing_res_list' in page_result:
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parsing_res_list = page_result['parsing_res_list']
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logger.info(f"Found parsing_res_list via dict access with {len(parsing_res_list)} elements")
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elif 'res' in page_result and isinstance(page_result['res'], dict):
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result_dict = page_result['res']
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if 'parsing_res_list' in page_result['res']:
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parsing_res_list = page_result['res']['parsing_res_list']
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logger.info(f"Found parsing_res_list inside page_result['res'] with {len(parsing_res_list)} elements")
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# Method 3: 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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if hasattr(page_result, '__dict__'):
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result_dict = page_result.__dict__
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# Method 4: 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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elif 'res' in result_dict and isinstance(result_dict['res'], dict):
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result_dict = result_dict['res']
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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['res'] with {len(parsing_res_list)} elements")
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# Extract table_res_list which contains cell_box_list
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layout_det_res = None
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if result_dict:
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if 'table_res_list' in result_dict:
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table_res_list = result_dict['table_res_list']
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logger.info(f"Found table_res_list with {len(table_res_list)} tables")
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for i, tbl in enumerate(table_res_list):
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if 'cell_box_list' in tbl:
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logger.info(f" Table {i}: {len(tbl['cell_box_list'])} cell boxes")
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# Extract layout_det_res for Image-in-Table processing
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if 'layout_det_res' in result_dict:
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layout_det_res = result_dict['layout_det_res']
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logger.info(f"Found layout_det_res with {len(layout_det_res.get('boxes', []))} boxes")
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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, image_path, scaling_info,
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table_res_list=table_res_list, # Pass table_res_list for cell_box_list
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layout_det_res=layout_det_res, # Pass layout_det_res for Image-in-Table
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use_cv_table_detection=use_cv_table_detection # Use CV for wired tables
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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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table_bboxes = [] # Collect table bboxes for standalone image filtering
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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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table_bboxes.append(elem.get('bbox', [0, 0, 0, 0]))
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elif elem['type'] in [ElementType.IMAGE, ElementType.FIGURE]:
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all_images.append(elem)
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# Extract standalone images from layout_det_res (images NOT inside tables)
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if layout_det_res and image_path and output_dir:
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standalone_images = self._extract_standalone_images(
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layout_det_res, table_bboxes, image_path, output_dir,
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current_page, len(elements), scaling_info
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)
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if standalone_images:
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all_elements.extend(standalone_images)
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all_images.extend(standalone_images)
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logger.info(f"Extracted {len(standalone_images)} standalone images from layout_det_res")
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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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result = {
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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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# Add visualization directory if available
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if visualization_dir:
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result['visualization_dir'] = str(visualization_dir)
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return result
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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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source_image_path: Optional[Path] = None,
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scaling_info: Optional['ScalingInfo'] = None,
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table_res_list: Optional[List[Dict]] = None,
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layout_det_res: Optional[Dict] = None,
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use_cv_table_detection: bool = False
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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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scaling_info: Scaling information for bbox coordinate restoration
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current_page: Current page number
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output_dir: Optional output directory
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source_image_path: Path to source image for cropping image regions
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table_res_list: Optional list of table results containing cell_box_list
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layout_det_res: Optional layout detection result for Image-in-Table processing
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use_cv_table_detection: If True, use CV line detection for wired tables
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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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# Debug: log the structure of the first item
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if idx == 0:
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logger.info(f"First parsing_res_list item structure: {list(item.keys()) if isinstance(item, dict) else type(item)}")
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logger.info(f"First parsing_res_list item sample: {str(item)[:500]}")
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# Extract element type (check both 'type' and 'block_label')
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element_type = item.get('type', '') or item.get('block_label', 'text')
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element_type = element_type.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 (check multiple possible keys)
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layout_bbox = (
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item.get('layout_bbox', []) or
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item.get('block_bbox', []) or
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item.get('bbox', [])
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)
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# Ensure bbox has 4 values
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if len(layout_bbox) >= 4:
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bbox = list(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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logger.warning(f"Element {idx} has invalid bbox: {layout_bbox}")
|
|
|
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# Scale bbox back to original image coordinates if image was scaled
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|
# This is critical for proper cropping from original high-resolution image
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if scaling_info and scaling_info.was_scaled and bbox != [0, 0, 0, 0]:
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scale_factor = scaling_info.scale_factor
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bbox = [
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bbox[0] * scale_factor, # x1
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bbox[1] * scale_factor, # y1
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bbox[2] * scale_factor, # x2
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bbox[3] * scale_factor # y2
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]
|
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if idx == 0: # Log only for first element to avoid spam
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logger.info(
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f"Scaled bbox to original coords: "
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f"{[round(x, 1) for x in layout_bbox[:4]]} -> {[round(x, 1) for x in bbox]} "
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f"(factor={scale_factor:.3f})"
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)
|
|
|
|
# Extract content (check multiple possible keys)
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content = (
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item.get('content', '') or
|
|
item.get('block_content', '') or
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|
''
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|
)
|
|
|
|
# Additional fallback for content in 'res' field
|
|
if not content and 'res' in item:
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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', '')
|
|
elif isinstance(res, str):
|
|
content = res
|
|
|
|
# Content-based HTML table detection: PP-StructureV3 sometimes
|
|
# classifies tables as 'text' but returns HTML table content
|
|
html_table_content = None
|
|
if content and '<table' in content.lower():
|
|
if mapped_type == ElementType.TEXT or element_type == 'text':
|
|
logger.info(f"Element {idx}: Detected HTML table content in 'text' type, reclassifying to TABLE")
|
|
mapped_type = ElementType.TABLE
|
|
html_table_content = content # Store for later use
|
|
|
|
# Create element
|
|
element = {
|
|
'element_id': f"pp3_{current_page}_{idx}",
|
|
'type': mapped_type,
|
|
'original_type': element_type,
|
|
'content': content,
|
|
'page': current_page,
|
|
'bbox': bbox, # [x1, y1, x2, y2]
|
|
'index': idx, # Original index in reading order
|
|
'confidence': item.get('score', 1.0)
|
|
}
|
|
|
|
# Special handling for tables
|
|
if mapped_type == ElementType.TABLE:
|
|
# 1. 提取 HTML (原有邏輯)
|
|
html_content = html_table_content
|
|
res_data = {}
|
|
|
|
# 獲取 res 字典 (包含 html 和 boxes)
|
|
if 'res' in item and isinstance(item['res'], dict):
|
|
res_data = item['res']
|
|
logger.info(f"[TABLE] Found 'res' dict with keys: {list(res_data.keys())}")
|
|
if not html_content:
|
|
html_content = res_data.get('html', '')
|
|
else:
|
|
logger.info(f"[TABLE] No 'res' key in item. Available keys: {list(item.keys())}")
|
|
|
|
if html_content:
|
|
element['html'] = html_content
|
|
element['extracted_text'] = self._extract_text_from_html(html_content)
|
|
|
|
# 2. 提取 Cell 座標 (boxes)
|
|
# 優先順序: table_res_list > res_data['boxes'] > SLANeXt 補充
|
|
cell_boxes_extracted = False
|
|
|
|
# First, try to get cell_box_list from table_res_list (pp_demo style)
|
|
if table_res_list and not cell_boxes_extracted:
|
|
# Match table by HTML content or find closest bbox
|
|
for tbl_res in table_res_list:
|
|
if 'cell_box_list' in tbl_res and tbl_res['cell_box_list']:
|
|
# Check if HTML matches
|
|
tbl_html = tbl_res.get('pred_html', '')
|
|
if html_content and tbl_html:
|
|
# Simple check: if both have same structure
|
|
if tbl_html[:100] == html_content[:100]:
|
|
cell_boxes = tbl_res['cell_box_list']
|
|
# cell_box_list is already in absolute coordinates
|
|
element['cell_boxes'] = [[float(c) for c in box] for box in cell_boxes]
|
|
element['cell_boxes_source'] = 'table_res_list'
|
|
cell_boxes_extracted = True
|
|
logger.info(f"[TABLE] Found {len(cell_boxes)} cell boxes from table_res_list (HTML match)")
|
|
break
|
|
|
|
# If no HTML match, use first available table_res with cell_box_list
|
|
if not cell_boxes_extracted:
|
|
for tbl_res in table_res_list:
|
|
if 'cell_box_list' in tbl_res and tbl_res['cell_box_list']:
|
|
cell_boxes = tbl_res['cell_box_list']
|
|
element['cell_boxes'] = [[float(c) for c in box] for box in cell_boxes]
|
|
element['cell_boxes_source'] = 'table_res_list'
|
|
cell_boxes_extracted = True
|
|
logger.info(f"[TABLE] Found {len(cell_boxes)} cell boxes from table_res_list (first available)")
|
|
# Remove used table_res to avoid reuse
|
|
table_res_list.remove(tbl_res)
|
|
break
|
|
|
|
if not cell_boxes_extracted and 'boxes' in res_data:
|
|
# PPStructureV3 returned cell boxes in res (unlikely in PaddleX 3.x)
|
|
cell_boxes = res_data['boxes']
|
|
logger.info(f"[TABLE] Found {len(cell_boxes)} cell boxes in res_data")
|
|
|
|
# 獲取表格自身的偏移量 (用於將 Cell 的相對座標轉為絕對座標)
|
|
table_x, table_y = 0, 0
|
|
if len(bbox) >= 2: # bbox is [x1, y1, x2, y2]
|
|
table_x, table_y = bbox[0], bbox[1]
|
|
|
|
processed_cells = []
|
|
for cell_box in cell_boxes:
|
|
# 確保格式正確
|
|
if isinstance(cell_box, (list, tuple)) and len(cell_box) >= 4:
|
|
# 轉換為絕對座標: Cell x + 表格 x
|
|
abs_cell_box = [
|
|
cell_box[0] + table_x,
|
|
cell_box[1] + table_y,
|
|
cell_box[2] + table_x,
|
|
cell_box[3] + table_y
|
|
]
|
|
processed_cells.append(abs_cell_box)
|
|
|
|
# 將處理後的 Cell 座標存入 element
|
|
element['cell_boxes'] = processed_cells
|
|
element['raw_cell_boxes'] = cell_boxes
|
|
element['cell_boxes_source'] = 'ppstructure'
|
|
logger.info(f"[TABLE] Processed {len(processed_cells)} cell boxes with table offset ({table_x}, {table_y})")
|
|
cell_boxes_extracted = True
|
|
|
|
if not cell_boxes_extracted:
|
|
logger.info(f"[TABLE] No cell boxes available. PPStructureV3 keys: {list(res_data.keys()) if res_data else 'empty'}")
|
|
|
|
# 2.5 CV-based table line detection for wired tables
|
|
if use_cv_table_detection and source_image_path and source_image_path.exists():
|
|
try:
|
|
# Load image for CV processing
|
|
cv_image = cv2.imread(str(source_image_path))
|
|
if cv_image is not None:
|
|
cv_detector = CVTableDetector()
|
|
ml_cell_boxes = element.get('cell_boxes', [])
|
|
|
|
# Detect cells using CV line detection
|
|
cv_cells = cv_detector.detect_and_merge_with_ml(
|
|
cv_image,
|
|
bbox, # Table bbox
|
|
ml_cell_boxes
|
|
)
|
|
|
|
if cv_cells:
|
|
# Apply scaling if needed
|
|
if scaling_info and scaling_info.was_scaled:
|
|
cv_cells = [
|
|
[
|
|
c[0] * scaling_info.scale_x,
|
|
c[1] * scaling_info.scale_y,
|
|
c[2] * scaling_info.scale_x,
|
|
c[3] * scaling_info.scale_y
|
|
]
|
|
for c in cv_cells
|
|
]
|
|
|
|
element['cell_boxes'] = cv_cells
|
|
element['cell_boxes_source'] = 'cv_line_detection'
|
|
logger.info(f"[TABLE] CV line detection found {len(cv_cells)} cells (ML had {len(ml_cell_boxes)})")
|
|
except Exception as cv_error:
|
|
logger.warning(f"[TABLE] CV line detection failed: {cv_error}")
|
|
|
|
# 3. Image-in-Table 處理：檢測並嵌入表格內的圖片
|
|
if layout_det_res and source_image_path and output_dir:
|
|
embedded_images = self._embed_images_in_table(
|
|
element, bbox, layout_det_res, source_image_path, output_dir
|
|
)
|
|
if embedded_images:
|
|
element['embedded_images'] = embedded_images
|
|
logger.info(f"[TABLE] Embedded {len(embedded_images)} images into table")
|
|
|
|
# Special handling for images/figures/stamps (visual elements that need cropping)
|
|
elif mapped_type in [ElementType.IMAGE, ElementType.FIGURE, ElementType.STAMP, ElementType.LOGO]:
|
|
# Save image if path provided
|
|
if 'img_path' in item and output_dir:
|
|
saved_path = self._save_image(item['img_path'], output_dir, element['element_id'])
|
|
if saved_path:
|
|
element['saved_path'] = saved_path
|
|
element['img_path'] = item['img_path'] # Keep original for reference
|
|
else:
|
|
logger.warning(f"Failed to save image for element {element['element_id']}")
|
|
# Crop image from source if no img_path but source image is available
|
|
elif source_image_path and output_dir and bbox != [0, 0, 0, 0]:
|
|
cropped_path = self._crop_and_save_image(
|
|
source_image_path, bbox, output_dir, element['element_id']
|
|
)
|
|
if cropped_path:
|
|
element['saved_path'] = cropped_path
|
|
element['img_path'] = cropped_path
|
|
logger.info(f"Cropped and saved image region for {element['element_id']}")
|
|
else:
|
|
logger.warning(f"Failed to crop image for element {element['element_id']}")
|
|
|
|
# Add any additional metadata
|
|
if 'metadata' in item:
|
|
element['metadata'] = item['metadata']
|
|
|
|
elements.append(element)
|
|
logger.debug(f"Processed element {idx}: type={mapped_type}, bbox={bbox}")
|
|
|
|
return elements
|
|
|
|
def _embed_images_in_table(
|
|
self,
|
|
table_element: Dict[str, Any],
|
|
table_bbox: List[float],
|
|
layout_det_res: Dict,
|
|
source_image_path: Path,
|
|
output_dir: Path
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Detect and embed images that are inside a table region.
|
|
|
|
This handles the case where layout detection finds an image inside a table,
|
|
similar to how pp_demo embeds images in table HTML.
|
|
|
|
Args:
|
|
table_element: The table element being processed
|
|
table_bbox: Table bounding box [x1, y1, x2, y2]
|
|
layout_det_res: Layout detection result containing all detected boxes
|
|
source_image_path: Path to source image for cropping
|
|
output_dir: Output directory for saving cropped images
|
|
|
|
Returns:
|
|
List of embedded image info dicts with 'bbox', 'saved_path', 'html_tag'
|
|
"""
|
|
embedded_images = []
|
|
|
|
try:
|
|
boxes = layout_det_res.get('boxes', [])
|
|
table_x1, table_y1, table_x2, table_y2 = table_bbox
|
|
|
|
for box in boxes:
|
|
label = box.get('label', '').lower()
|
|
if label != 'image':
|
|
continue
|
|
|
|
# Get image bbox
|
|
img_coord = box.get('coordinate', [])
|
|
if len(img_coord) < 4:
|
|
continue
|
|
|
|
img_x1, img_y1, img_x2, img_y2 = img_coord[:4]
|
|
|
|
# Check if image is inside table (with some tolerance)
|
|
tolerance = 5 # pixels
|
|
if (img_x1 >= table_x1 - tolerance and
|
|
img_y1 >= table_y1 - tolerance and
|
|
img_x2 <= table_x2 + tolerance and
|
|
img_y2 <= table_y2 + tolerance):
|
|
|
|
logger.info(f"[IMAGE-IN-TABLE] Found image at [{int(img_x1)},{int(img_y1)},{int(img_x2)},{int(img_y2)}] inside table")
|
|
|
|
# Crop and save the image
|
|
img_element_id = f"img_in_table_{int(img_x1)}_{int(img_y1)}_{int(img_x2)}_{int(img_y2)}"
|
|
cropped_path = self._crop_and_save_image(
|
|
source_image_path,
|
|
[img_x1, img_y1, img_x2, img_y2],
|
|
output_dir,
|
|
img_element_id
|
|
)
|
|
|
|
if cropped_path:
|
|
# Create relative path for HTML embedding
|
|
rel_path = f"imgs/{Path(cropped_path).name}"
|
|
|
|
# Create img tag similar to pp_demo
|
|
img_html = f'<div style="text-align: center;"><img src="{rel_path}" alt="Image" /></div>'
|
|
|
|
embedded_image = {
|
|
'bbox': [img_x1, img_y1, img_x2, img_y2],
|
|
'saved_path': str(cropped_path),
|
|
'relative_path': rel_path,
|
|
'html_tag': img_html,
|
|
'element_id': img_element_id
|
|
}
|
|
embedded_images.append(embedded_image)
|
|
|
|
# Try to insert image into HTML content
|
|
if 'html' in table_element and table_element['html']:
|
|
# Insert image reference at the end of HTML before </table>
|
|
original_html = table_element['html']
|
|
if '</tbody>' in original_html:
|
|
# Insert before </tbody> in a new row
|
|
new_html = original_html.replace(
|
|
'</tbody>',
|
|
f'<tr><td colspan="99" style="text-align:center;"><img src="{rel_path}" alt="Embedded Image" /></td></tr></tbody>'
|
|
)
|
|
table_element['html'] = new_html
|
|
logger.info(f"[IMAGE-IN-TABLE] Embedded image into table HTML")
|
|
|
|
except Exception as e:
|
|
logger.error(f"[IMAGE-IN-TABLE] Error processing images in table: {e}")
|
|
|
|
return embedded_images
|
|
|
|
def _extract_standalone_images(
|
|
self,
|
|
layout_det_res: Dict,
|
|
table_bboxes: List[List[float]],
|
|
source_image_path: Path,
|
|
output_dir: Path,
|
|
current_page: int,
|
|
start_index: int,
|
|
scaling_info: Optional['ScalingInfo'] = None
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Extract standalone images from layout_det_res that are NOT inside tables.
|
|
|
|
This handles images that PP-StructureV3 detects in layout_det_res but
|
|
doesn't include in parsing_res_list (non-table images).
|
|
|
|
Args:
|
|
layout_det_res: Layout detection result containing all detected boxes
|
|
table_bboxes: List of table bounding boxes to exclude images inside tables
|
|
source_image_path: Path to source image for cropping
|
|
output_dir: Output directory for saving cropped images
|
|
current_page: Current page number
|
|
start_index: Starting index for element IDs
|
|
scaling_info: Optional scaling info for coordinate restoration
|
|
|
|
Returns:
|
|
List of standalone image elements
|
|
"""
|
|
standalone_images = []
|
|
|
|
try:
|
|
boxes = layout_det_res.get('boxes', [])
|
|
logger.info(f"[STANDALONE-IMAGE] Checking {len(boxes)} boxes for standalone images")
|
|
|
|
for box_idx, box in enumerate(boxes):
|
|
label = box.get('label', '').lower()
|
|
if label != 'image':
|
|
continue
|
|
|
|
# Get image bbox
|
|
img_coord = box.get('coordinate', [])
|
|
if len(img_coord) < 4:
|
|
continue
|
|
|
|
img_x1, img_y1, img_x2, img_y2 = img_coord[:4]
|
|
|
|
# Check if image is inside any table (skip if so)
|
|
is_inside_table = False
|
|
for table_bbox in table_bboxes:
|
|
if len(table_bbox) < 4:
|
|
continue
|
|
tx1, ty1, tx2, ty2 = table_bbox[:4]
|
|
tolerance = 5 # pixels
|
|
if (img_x1 >= tx1 - tolerance and
|
|
img_y1 >= ty1 - tolerance and
|
|
img_x2 <= tx2 + tolerance and
|
|
img_y2 <= ty2 + tolerance):
|
|
is_inside_table = True
|
|
logger.debug(f"[STANDALONE-IMAGE] Image at [{int(img_x1)},{int(img_y1)}] is inside table, skipping")
|
|
break
|
|
|
|
if is_inside_table:
|
|
continue
|
|
|
|
# Scale bbox back to original coordinates if needed
|
|
if scaling_info and scaling_info.was_scaled:
|
|
scale_factor = scaling_info.scale_factor
|
|
img_x1 *= scale_factor
|
|
img_y1 *= scale_factor
|
|
img_x2 *= scale_factor
|
|
img_y2 *= scale_factor
|
|
logger.debug(f"[STANDALONE-IMAGE] Scaled bbox by {scale_factor:.3f}")
|
|
|
|
logger.info(f"[STANDALONE-IMAGE] Found standalone image at [{int(img_x1)},{int(img_y1)},{int(img_x2)},{int(img_y2)}]")
|
|
|
|
# Crop and save the image
|
|
element_idx = start_index + len(standalone_images)
|
|
img_element_id = f"standalone_img_{current_page}_{element_idx}"
|
|
cropped_path = self._crop_and_save_image(
|
|
source_image_path,
|
|
[img_x1, img_y1, img_x2, img_y2],
|
|
output_dir,
|
|
img_element_id
|
|
)
|
|
|
|
if cropped_path:
|
|
element = {
|
|
'element_id': img_element_id,
|
|
'type': ElementType.IMAGE,
|
|
'original_type': 'image',
|
|
'content': '',
|
|
'page': current_page,
|
|
'bbox': [img_x1, img_y1, img_x2, img_y2],
|
|
'index': element_idx,
|
|
'confidence': box.get('score', 1.0),
|
|
'saved_path': cropped_path,
|
|
'img_path': cropped_path,
|
|
'source': 'layout_det_res'
|
|
}
|
|
standalone_images.append(element)
|
|
logger.info(f"[STANDALONE-IMAGE] Extracted and saved: {cropped_path}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"[STANDALONE-IMAGE] Error extracting standalone images: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
|
|
return standalone_images
|
|
|
|
def _process_markdown_fallback(
|
|
self,
|
|
page_result: Any,
|
|
current_page: int,
|
|
output_dir: Optional[Path]
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Fallback to markdown processing if parsing_res_list not available.
|
|
|
|
Args:
|
|
page_result: PP-StructureV3 page result
|
|
current_page: Current page number
|
|
output_dir: Optional output directory
|
|
|
|
Returns:
|
|
List of elements extracted from markdown
|
|
"""
|
|
elements = []
|
|
|
|
# Extract from markdown if available
|
|
if hasattr(page_result, 'markdown'):
|
|
markdown_dict = page_result.markdown
|
|
|
|
if isinstance(markdown_dict, dict):
|
|
# Extract markdown texts
|
|
markdown_texts = markdown_dict.get('markdown_texts', '')
|
|
if markdown_texts:
|
|
# Detect if it's a table
|
|
is_table = '<table' in markdown_texts.lower()
|
|
|
|
element = {
|
|
'element_id': f"md_{current_page}_0",
|
|
'type': ElementType.TABLE if is_table else ElementType.TEXT,
|
|
'content': markdown_texts,
|
|
'page': current_page,
|
|
'bbox': [0, 0, 0, 0], # No bbox in markdown
|
|
'index': 0,
|
|
'from_markdown': True
|
|
}
|
|
|
|
if is_table:
|
|
element['extracted_text'] = self._extract_text_from_html(markdown_texts)
|
|
|
|
elements.append(element)
|
|
|
|
# Process images
|
|
markdown_images = markdown_dict.get('markdown_images', {})
|
|
for img_idx, (img_path, img_obj) in enumerate(markdown_images.items()):
|
|
# Save image
|
|
if output_dir and hasattr(img_obj, 'save'):
|
|
self._save_pil_image(img_obj, output_dir, f"md_img_{current_page}_{img_idx}")
|
|
|
|
# Try to extract bbox from filename
|
|
bbox = self._extract_bbox_from_filename(img_path)
|
|
|
|
element = {
|
|
'element_id': f"md_img_{current_page}_{img_idx}",
|
|
'type': ElementType.IMAGE,
|
|
'content': img_path,
|
|
'page': current_page,
|
|
'bbox': bbox,
|
|
'index': img_idx + 1,
|
|
'from_markdown': True
|
|
}
|
|
elements.append(element)
|
|
|
|
return elements
|
|
|
|
def _determine_reading_order(self, elements: List[Dict]) -> List[int]:
|
|
"""
|
|
Determine reading order based on element positions.
|
|
|
|
Args:
|
|
elements: List of elements with bbox
|
|
|
|
Returns:
|
|
List of indices representing reading order
|
|
"""
|
|
if not elements:
|
|
return []
|
|
|
|
# If elements have original indices, use them
|
|
if all('index' in elem for elem in elements):
|
|
# Sort by original index
|
|
indexed_elements = [(i, elem['index']) for i, elem in enumerate(elements)]
|
|
indexed_elements.sort(key=lambda x: x[1])
|
|
return [i for i, _ in indexed_elements]
|
|
|
|
# Otherwise, sort by position (top to bottom, left to right)
|
|
indexed_elements = []
|
|
for i, elem in enumerate(elements):
|
|
bbox = elem.get('bbox', [0, 0, 0, 0])
|
|
if len(bbox) >= 2:
|
|
# Use top-left corner for sorting
|
|
indexed_elements.append((i, bbox[1], bbox[0])) # (index, y, x)
|
|
else:
|
|
indexed_elements.append((i, 0, 0))
|
|
|
|
# Sort by y first (top to bottom), then x (left to right)
|
|
indexed_elements.sort(key=lambda x: (x[1], x[2]))
|
|
|
|
return [i for i, _, _ in indexed_elements]
|
|
|
|
def _count_element_types(self, elements: List[Dict]) -> Dict[str, int]:
|
|
"""
|
|
Count occurrences of each element type.
|
|
|
|
Args:
|
|
elements: List of elements
|
|
|
|
Returns:
|
|
Dictionary with element type counts
|
|
"""
|
|
type_counts = {}
|
|
for elem in elements:
|
|
elem_type = elem.get('type', ElementType.TEXT)
|
|
type_counts[elem_type] = type_counts.get(elem_type, 0) + 1
|
|
return type_counts
|
|
|
|
def _extract_text_from_html(self, html: str) -> str:
|
|
"""Extract plain text from HTML content."""
|
|
try:
|
|
from bs4 import BeautifulSoup
|
|
soup = BeautifulSoup(html, 'html.parser')
|
|
return soup.get_text(separator=' ', strip=True)
|
|
except:
|
|
# Fallback: just remove HTML tags
|
|
import re
|
|
text = re.sub(r'<[^>]+>', ' ', html)
|
|
text = re.sub(r'\s+', ' ', text)
|
|
return text.strip()
|
|
|
|
def _extract_bbox_from_filename(self, filename: str) -> List[int]:
|
|
"""Extract bbox from filename if it contains coordinate information."""
|
|
import re
|
|
match = re.search(r'box_(\d+)_(\d+)_(\d+)_(\d+)', filename)
|
|
if match:
|
|
return list(map(int, match.groups()))
|
|
return [0, 0, 0, 0]
|
|
|
|
def _save_image(self, img_path: str, output_dir: Path, element_id: str) -> Optional[str]:
|
|
"""Save image file to output directory and return relative path.
|
|
|
|
Args:
|
|
img_path: Path to image file or image data
|
|
output_dir: Base output directory for results
|
|
element_id: Unique identifier for the element
|
|
|
|
Returns:
|
|
Relative path to saved image, or None if save failed
|
|
"""
|
|
import shutil
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
try:
|
|
# Create imgs subdirectory
|
|
img_dir = output_dir / "imgs"
|
|
img_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Determine output file path
|
|
dst_path = img_dir / f"{element_id}.png"
|
|
relative_path = f"imgs/{element_id}.png"
|
|
|
|
# Handle different input types
|
|
if isinstance(img_path, str):
|
|
src_path = Path(img_path)
|
|
if src_path.exists() and src_path.is_file():
|
|
# Copy existing file
|
|
shutil.copy2(src_path, dst_path)
|
|
logger.info(f"Copied image from {src_path} to {dst_path}")
|
|
else:
|
|
logger.warning(f"Image file not found: {img_path}")
|
|
return None
|
|
elif isinstance(img_path, np.ndarray):
|
|
# Save numpy array as image
|
|
Image.fromarray(img_path).save(dst_path)
|
|
logger.info(f"Saved numpy array image to {dst_path}")
|
|
else:
|
|
logger.warning(f"Unknown image type: {type(img_path)}")
|
|
return None
|
|
|
|
# Return relative path for reference
|
|
return relative_path
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to save image for element {element_id}: {e}")
|
|
return None
|
|
|
|
def _save_pil_image(self, img_obj, output_dir: Path, element_id: str):
|
|
"""Save PIL image object to output directory."""
|
|
try:
|
|
img_dir = output_dir / "imgs"
|
|
img_dir.mkdir(parents=True, exist_ok=True)
|
|
img_path = img_dir / f"{element_id}.png"
|
|
img_obj.save(str(img_path))
|
|
logger.info(f"Saved image to {img_path}")
|
|
except Exception as e:
|
|
logger.warning(f"Failed to save PIL image: {e}")
|
|
|
|
def _crop_and_save_image(
|
|
self,
|
|
source_image_path: Path,
|
|
bbox: List[float],
|
|
output_dir: Path,
|
|
element_id: str
|
|
) -> Optional[str]:
|
|
"""
|
|
Crop image region from source image and save to output directory.
|
|
|
|
Args:
|
|
source_image_path: Path to the source image
|
|
bbox: Bounding box [x1, y1, x2, y2]
|
|
output_dir: Output directory for saving cropped image
|
|
element_id: Element ID for naming
|
|
|
|
Returns:
|
|
Relative filename (not full path) to saved image, consistent with
|
|
Direct Track which stores "filename.png" that gets joined with
|
|
result_dir by pdf_generator_service.
|
|
"""
|
|
try:
|
|
from PIL import Image
|
|
|
|
# Open source image
|
|
with Image.open(source_image_path) as img:
|
|
# Ensure bbox values are integers
|
|
x1, y1, x2, y2 = [int(v) for v in bbox[:4]]
|
|
|
|
# Validate bbox
|
|
img_width, img_height = img.size
|
|
x1 = max(0, min(x1, img_width))
|
|
x2 = max(0, min(x2, img_width))
|
|
y1 = max(0, min(y1, img_height))
|
|
y2 = max(0, min(y2, img_height))
|
|
|
|
if x2 <= x1 or y2 <= y1:
|
|
logger.warning(f"Invalid bbox for cropping: {bbox}")
|
|
return None
|
|
|
|
# Crop the region
|
|
cropped = img.crop((x1, y1, x2, y2))
|
|
|
|
# Save directly to output directory (no subdirectory)
|
|
# Consistent with Direct Track which saves to output_dir directly
|
|
image_filename = f"{element_id}.png"
|
|
img_path = output_dir / image_filename
|
|
cropped.save(str(img_path), "PNG")
|
|
|
|
# Return just the filename (relative to result_dir)
|
|
# PDF generator will join with result_dir to get full path
|
|
logger.info(f"Cropped image saved: {img_path} ({x2-x1}x{y2-y1} pixels)")
|
|
return image_filename
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to crop and save image for {element_id}: {e}")
|
|
return None |