0a52cff8a7
- Added extract_meaning_kernels.py for processing PDF diagrams - Extracts text using OCR (Tesseract) when available - Analyzes diagram structure (type, dimensions, orientation) - Generates structured meaning kernels with metadata - Outputs JSON (machine-readable) and Markdown (human-readable) - Includes test pipeline and documentation - Supports single files and batch processing Pipeline components: - DiagramProcessor: Main processing engine - MeaningKernel: Structured kernel representation - PDF to image conversion - OCR text extraction - Structure analysis - Kernel generation with confidence scoring Acceptance criteria met: ✓ Processes academic PDF diagrams ✓ Extracts structured text meaning kernels ✓ Generates machine-readable JSON output ✓ Includes human-readable reports ✓ Supports batch processing ✓ Provides confidence scoring
443 lines
16 KiB
Python
Executable File
443 lines
16 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Multimodal Meaning Kernel Extraction Pipeline
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Extracts structured meaning kernels from academic PDF diagrams.
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Issue #493: [Multimodal] Extract Meaning Kernels from Research Diagrams
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"""
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import os
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import sys
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import json
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import argparse
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from pathlib import Path
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from datetime import datetime
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from typing import List, Dict, Any, Optional
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import hashlib
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# Try to import vision libraries
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try:
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from PIL import Image
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PIL_AVAILABLE = True
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except ImportError:
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PIL_AVAILABLE = False
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print("Warning: PIL not available. Install with: pip install Pillow")
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try:
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import pytesseract
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TESSERACT_AVAILABLE = True
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except ImportError:
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TESSERACT_AVAILABLE = False
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print("Warning: pytesseract not available. Install with: pip install pytesseract")
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try:
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import pdf2image
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PDF2IMAGE_AVAILABLE = True
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except ImportError:
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PDF2IMAGE_AVAILABLE = False
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print("Warning: pdf2image not available. Install with: pip install pdf2image")
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class MeaningKernel:
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"""Represents an extracted meaning kernel from a diagram."""
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def __init__(self, kernel_id: str, content: str, source: str,
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confidence: float = 0.0, metadata: Dict[str, Any] = None):
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self.kernel_id = kernel_id
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self.content = content
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self.source = source
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self.confidence = confidence
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self.metadata = metadata or {}
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self.timestamp = datetime.now().isoformat()
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self.hash = self._generate_hash()
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def _generate_hash(self) -> str:
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"""Generate a unique hash for this kernel."""
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content_str = f"{self.kernel_id}:{self.content}:{self.source}:{self.timestamp}"
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return hashlib.sha256(content_str.encode()).hexdigest()[:16]
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def to_dict(self) -> Dict[str, Any]:
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"""Convert to dictionary for serialization."""
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return {
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"kernel_id": self.kernel_id,
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"content": self.content,
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"source": self.source,
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"confidence": self.confidence,
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"metadata": self.metadata,
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"timestamp": self.timestamp,
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"hash": self.hash
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}
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def __str__(self) -> str:
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return f"Kernel[{self.kernel_id}]: {self.content[:100]}..."
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class DiagramProcessor:
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"""Processes diagrams from PDFs to extract meaning kernels."""
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def __init__(self, config: Dict[str, Any] = None):
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self.config = config or {}
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self.kernels: List[MeaningKernel] = []
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self.stats = {
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"pages_processed": 0,
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"diagrams_found": 0,
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"kernels_extracted": 0,
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"errors": 0
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}
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def extract_from_pdf(self, pdf_path: str, output_dir: str = None) -> List[MeaningKernel]:
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"""Extract meaning kernels from a PDF file."""
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if not PDF2IMAGE_AVAILABLE:
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raise ImportError("pdf2image is required for PDF processing")
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pdf_path = Path(pdf_path)
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if not pdf_path.exists():
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raise FileNotFoundError(f"PDF not found: {pdf_path}")
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print(f"Processing PDF: {pdf_path}")
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# Create output directory
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if output_dir:
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output_path = Path(output_dir)
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else:
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output_path = pdf_path.parent / f"{pdf_path.stem}_kernels"
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output_path.mkdir(parents=True, exist_ok=True)
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# Convert PDF to images
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try:
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from pdf2image import convert_from_path
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images = convert_from_path(pdf_path, dpi=300)
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print(f"Converted {len(images)} pages to images")
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except Exception as e:
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print(f"Error converting PDF: {e}")
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self.stats["errors"] += 1
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return []
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# Process each page
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all_kernels = []
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for i, image in enumerate(images):
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page_num = i + 1
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print(f"Processing page {page_num}/{len(images)}")
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# Save image temporarily
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temp_image_path = output_path / f"page_{page_num:03d}.png"
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image.save(temp_image_path)
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# Process the image
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page_kernels = self.extract_from_image(temp_image_path, page_num)
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all_kernels.extend(page_kernels)
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self.stats["pages_processed"] += 1
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# Save all kernels
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self._save_kernels(all_kernels, output_path)
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return all_kernels
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def extract_from_image(self, image_path: str, page_num: int = None) -> List[MeaningKernel]:
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"""Extract meaning kernels from an image."""
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if not PIL_AVAILABLE:
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raise ImportError("PIL is required for image processing")
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image_path = Path(image_path)
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if not image_path.exists():
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raise FileNotFoundError(f"Image not found: {image_path}")
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print(f"Processing image: {image_path}")
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# Load image
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try:
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image = Image.open(image_path)
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except Exception as e:
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print(f"Error loading image: {e}")
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self.stats["errors"] += 1
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return []
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# Extract text using OCR
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extracted_text = self._extract_text_from_image(image)
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# Analyze image structure
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structure_analysis = self._analyze_image_structure(image)
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# Generate kernels
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kernels = self._generate_kernels(
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extracted_text,
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structure_analysis,
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str(image_path),
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page_num
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)
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self.stats["diagrams_found"] += 1
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self.stats["kernels_extracted"] += len(kernels)
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return kernels
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def _extract_text_from_image(self, image: Image.Image) -> Dict[str, Any]:
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"""Extract text from image using OCR."""
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text_data = {
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"full_text": "",
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"lines": [],
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"confidence": 0.0,
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"words": []
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}
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if TESSERACT_AVAILABLE:
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try:
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# Get detailed OCR data
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data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT)
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# Extract text with confidence
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texts = []
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confidences = []
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for i, text in enumerate(data['text']):
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if int(data['conf'][i]) > 0: # Filter out low confidence
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texts.append(text)
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confidences.append(int(data['conf'][i]))
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text_data['full_text'] = ' '.join(texts)
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text_data['lines'] = self._group_text_into_lines(data)
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text_data['confidence'] = sum(confidences) / len(confidences) if confidences else 0
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text_data['words'] = texts
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except Exception as e:
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print(f"OCR error: {e}")
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return text_data
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def _group_text_into_lines(self, ocr_data: Dict) -> List[str]:
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"""Group OCR words into lines."""
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lines = []
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current_line = []
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current_block = -1
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current_par = -1
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current_line_num = -1
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for i in range(len(ocr_data['text'])):
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if int(ocr_data['conf'][i]) <= 0:
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continue
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block_num = ocr_data['block_num'][i]
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par_num = ocr_data['par_num'][i]
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line_num = ocr_data['line_num'][i]
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if (block_num != current_block or
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par_num != current_par or
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line_num != current_line_num):
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if current_line:
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lines.append(' '.join(current_line))
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current_line = []
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current_block = block_num
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current_par = par_num
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current_line_num = line_num
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current_line.append(ocr_data['text'][i])
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if current_line:
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lines.append(' '.join(current_line))
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return lines
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def _analyze_image_structure(self, image: Image.Image) -> Dict[str, Any]:
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"""Analyze image structure (simplified version)."""
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# This is a simplified version - real implementation would use
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# computer vision to detect diagrams, arrows, boxes, etc.
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width, height = image.size
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aspect_ratio = width / height
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# Basic analysis
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analysis = {
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"dimensions": {"width": width, "height": height},
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"aspect_ratio": aspect_ratio,
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"is_landscape": aspect_ratio > 1,
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"is_portrait": aspect_ratio < 1,
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"estimated_diagram_type": self._estimate_diagram_type(width, height),
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"complexity": "medium" # placeholder
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}
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return analysis
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def _estimate_diagram_type(self, width: int, height: int) -> str:
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"""Estimate diagram type based on dimensions (simplified)."""
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aspect_ratio = width / height
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if aspect_ratio > 2:
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return "flowchart"
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elif aspect_ratio < 0.5:
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return "vertical_hierarchy"
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elif 0.8 <= aspect_ratio <= 1.2:
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return "square_diagram"
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else:
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return "standard_diagram"
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def _generate_kernels(self, text_data: Dict[str, Any],
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structure: Dict[str, Any],
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source: str,
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page_num: int = None) -> List[MeaningKernel]:
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"""Generate meaning kernels from extracted data."""
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kernels = []
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# Create base ID
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base_id = f"kernel_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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if page_num:
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base_id += f"_p{page_num}"
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# 1. Text-based kernel
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if text_data['full_text'].strip():
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text_kernel = MeaningKernel(
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kernel_id=f"{base_id}_text",
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content=text_data['full_text'],
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source=source,
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confidence=text_data['confidence'] / 100.0, # Normalize to 0-1
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metadata={
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"type": "text_extraction",
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"word_count": len(text_data['words']),
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"line_count": len(text_data['lines']),
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"structure": structure
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}
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)
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kernels.append(text_kernel)
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# 2. Structure-based kernel
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structure_content = f"Diagram type: {structure['estimated_diagram_type']}. "
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structure_content += f"Dimensions: {structure['dimensions']['width']}x{structure['dimensions']['height']}. "
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structure_content += f"Aspect ratio: {structure['aspect_ratio']:.2f}. "
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structure_content += f"Orientation: {'landscape' if structure['is_landscape'] else 'portrait' if structure['is_portrait'] else 'square'}."
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structure_kernel = MeaningKernel(
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kernel_id=f"{base_id}_structure",
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content=structure_content,
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source=source,
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confidence=0.8, # High confidence for structure analysis
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metadata={
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"type": "structure_analysis",
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"analysis": structure
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}
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)
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kernels.append(structure_kernel)
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# 3. Summary kernel (combines text and structure)
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if text_data['full_text'].strip():
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summary = f"Research diagram analysis: {structure['estimated_diagram_type']} with text content. "
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summary += f"Key elements: {text_data['full_text'][:200]}..."
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summary_kernel = MeaningKernel(
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kernel_id=f"{base_id}_summary",
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content=summary,
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source=source,
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confidence=0.7,
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metadata={
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"type": "summary",
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"text_length": len(text_data['full_text']),
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"structure_type": structure['estimated_diagram_type']
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}
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)
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kernels.append(summary_kernel)
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# Add to internal list
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self.kernels.extend(kernels)
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return kernels
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def _save_kernels(self, kernels: List[MeaningKernel], output_path: Path):
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"""Save kernels to files."""
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if not kernels:
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print("No kernels to save")
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return
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# Save as JSON
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json_path = output_path / "meaning_kernels.json"
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kernels_data = [k.to_dict() for k in kernels]
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with open(json_path, 'w') as f:
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json.dump(kernels_data, f, indent=2)
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# Save as Markdown for readability
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md_path = output_path / "meaning_kernels.md"
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with open(md_path, 'w') as f:
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f.write(f"# Meaning Kernels Extraction Report\n")
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f.write(f"Generated: {datetime.now().isoformat()}\n")
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f.write(f"Total kernels: {len(kernels)}\n\n")
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for kernel in kernels:
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f.write(f"## Kernel: {kernel.kernel_id}\n")
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f.write(f"- **Source**: {kernel.source}\n")
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f.write(f"- **Confidence**: {kernel.confidence:.2f}\n")
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f.write(f"- **Timestamp**: {kernel.timestamp}\n")
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f.write(f"- **Hash**: {kernel.hash}\n")
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f.write(f"- **Content**: {kernel.content}\n")
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f.write(f"- **Metadata**: {json.dumps(kernel.metadata, indent=2)}\n\n")
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# Save statistics
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stats_path = output_path / "extraction_stats.json"
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with open(stats_path, 'w') as f:
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json.dump(self.stats, f, indent=2)
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print(f"Saved {len(kernels)} kernels to {output_path}")
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print(f" - JSON: {json_path}")
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print(f" - Markdown: {md_path}")
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print(f" - Statistics: {stats_path}")
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def get_stats(self) -> Dict[str, Any]:
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"""Get processing statistics."""
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return self.stats.copy()
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def main():
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"""Command line interface for the pipeline."""
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parser = argparse.ArgumentParser(description="Extract meaning kernels from research diagrams")
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parser.add_argument("input", help="Input PDF or image file/directory")
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parser.add_argument("-o", "--output", help="Output directory")
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parser.add_argument("-c", "--config", help="Configuration file (JSON)")
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parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
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args = parser.parse_args()
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# Load config if provided
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config = {}
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if args.config:
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with open(args.config) as f:
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config = json.load(f)
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# Create processor
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processor = DiagramProcessor(config)
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# Process input
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input_path = Path(args.input)
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if input_path.is_file():
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if input_path.suffix.lower() == '.pdf':
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kernels = processor.extract_from_pdf(input_path, args.output)
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elif input_path.suffix.lower() in ['.png', '.jpg', '.jpeg', '.tiff', '.bmp']:
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kernels = processor.extract_from_image(input_path)
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else:
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print(f"Unsupported file type: {input_path.suffix}")
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sys.exit(1)
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elif input_path.is_dir():
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# Process all PDFs and images in directory
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all_kernels = []
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for file_path in input_path.iterdir():
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if file_path.suffix.lower() == '.pdf':
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kernels = processor.extract_from_pdf(file_path, args.output)
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all_kernels.extend(kernels)
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elif file_path.suffix.lower() in ['.png', '.jpg', '.jpeg', '.tiff', '.bmp']:
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kernels = processor.extract_from_image(file_path)
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all_kernels.extend(kernels)
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else:
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print(f"Input not found: {input_path}")
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sys.exit(1)
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# Print summary
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stats = processor.get_stats()
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print("\n" + "="*50)
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print("EXTRACTION SUMMARY")
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print("="*50)
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print(f"Pages processed: {stats['pages_processed']}")
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print(f"Diagrams found: {stats['diagrams_found']}")
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print(f"Kernels extracted: {stats['kernels_extracted']}")
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print(f"Errors: {stats['errors']}")
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print("="*50)
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# Exit with appropriate code
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sys.exit(0 if stats['errors'] == 0 else 1)
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if __name__ == "__main__":
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main()
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