Add computer vision foundation for construction document analysis with NFPA 72 device placement

[Copilot]

Implements visual processing pipeline using OpenCV for construction drawing analysis, NFPA 72 compliant fire alarm device placement calculations, and professional construction drawing intelligence framework.

Core Components

• Visual Processor (autofire_visual_processor.py): PDF→image conversion at 3x resolution, Hough transform wall detection, contour-based room segmentation, scale extraction
• Device Placement Engine (autofire_device_placement.py): NFPA 72 spacing calculations (30ft max, 900sqft/detector), coordinate generation with engineering reasoning, visual diagram output
• Construction Intelligence (autofire_construction_drawing_intelligence.py): Drawing classification (A-/S-/M-/E-/P-/C- sheets), professional reading workflows, MEP coordination checking, 35 stub methods for future enhancement

Dependencies

Added: opencv-python, PyMuPDF, numpy, Pillow

Usage

from autofire_visual_processor import AutoFireVisualProcessor
from autofire_device_placement import AutoFireDevicePlacementEngine
from autofire_construction_drawing_intelligence import ConstructionDrawingIntelligence

processor = AutoFireVisualProcessor()
placement = AutoFireDevicePlacementEngine()
intelligence = ConstructionDrawingIntelligence()

# Analyze floor plan
results = processor.analyze_floor_plan_image("plan.pdf", page_num=0)

# Calculate NFPA 72 device placements
designs = placement.design_fire_alarm_system(results)
# Returns precise (x,y) coordinates with coverage radius and code references

# Enhance with construction intelligence
enhanced = intelligence.enhance_autofire_visual_analysis(results, image)
# Adds drawing classification, symbol recognition, coordination checks

Testing

46 tests covering visual processing, device placement algorithms, and construction intelligence integration. Example demonstration in examples/visual_processing_demo.py.

Documentation

• API reference and usage patterns: docs/VISUAL_PROCESSING.md
• Implementation details: VISUAL_PROCESSING_SUMMARY.md

Original prompt

🔥 AutoFire Visual Processing Foundation - Complete Computer Vision & Professional Construction Intelligence

🔥 AUTOFIRE VISUAL PROCESSING FOUNDATION - Complete Computer Vision for Construction

This PR introduces a complete visual processing foundation for AutoFire with professional construction drawing intelligence capabilities. This represents a fundamental transformation from text-only analysis to true visual understanding of construction documents.

🚀 Core Visual Processing Pipeline

AutoFire Visual Processor (autofire_visual_processor.py)

• OpenCV-based computer vision for construction document analysis
• High-resolution processing at 9072x6480 pixels from PDF sources
• Advanced edge detection with noise filtering and adaptive thresholding
• Wall detection using Hough transforms and morphological operations
• Room boundary detection through contour analysis and spatial reasoning
• Visual debugging output with annotated detection results

Intelligent Device Placement (autofire_device_placement.py)

• NFPA 72 compliant placement engine with engineering calculations
• Precise coordinate generation for smoke detectors, horns, pull stations
• 30-foot spacing compliance for smoke detection coverage
• Visual placement diagrams with device positioning overlay
• Engineering reasoning for each device placement decision

Professional Construction Intelligence (autofire_construction_drawing_intelligence.py)

• Industry-standard drawing reading workflows based on professional resources
• Drawing type classification using sheet prefixes (A-, S-, M-, E-, P-, C-)
• Scale detection and calibration systems for accurate measurements
• Architectural symbol recognition with standardized meaning interpretation
• Multi-discipline coordination checking for MEP and structural conflicts
• Quality validation and industry compliance verification

📊 Performance & Capabilities

Capability	Before AutoFire	After AutoFire	Improvement
Walls Detected	0	3,926+	∞%
Room Analysis	Text parsing only	Visual spatial analysis	Revolutionary
Device Placement	Manual estimates	NFPA 72 calculated coordinates	Precision engineering
Processing Method	Text extraction	Computer vision + AI	Next-generation
Construction Documents	Limited to text	Full visual understanding	Complete transformation
Industry Compliance	Manual checking	Automated NFPA validation	Professional standard

🔧 Technical Architecture

Computer Vision Stack:

# Core visual processing
from autofire_visual_processor import AutoFireVisualProcessor
from autofire_device_placement import AutoFireDevicePlacementEngine
from autofire_construction_drawing_intelligence import ConstructionDrawingIntelligence

# Enhanced integration
processor = AutoFireVisualProcessor()
placement_engine = AutoFireDevicePlacementEngine()
intelligence = ConstructionDrawingIntelligence()

# Professional analysis workflow
visual_results = processor.process_pdf_page(pdf_path, page_number)
enhanced_results = intelligence.enhance_autofire_visual_analysis(visual_results, image)
device_placements = placement_engine.place_devices_with_visual_analysis(enhanced_results)

Professional Integration:

• Construction Drawing Intelligence enhances AutoFire results with industry expertise
• Professional reading workflows: edges → legends → bearings → structure → MEP → coordination
• Scale calibration from title blocks, dimensions, and standard elements
• Symbol libraries for doors, windows, electrical, MEP, fire safety systems

🏗️ Industry Foundation Integration

Professional Resources Integrated:

• CAD Drafter: Step-by-step construction drawing reading methodology
• MT Copeland: Complete blueprint reading standards and workflows
• Premier CS: Construction drawing documentation standards
• TCLI: Professional blueprint reading for civil construction

Professional Capabilities:

• Drawing type classification using industry standards (Architectural, Structural, MEP, Civil)
• Line weight analysis (heavy=cut sections, medium=visible, light=hidden elements)
• Material hatch recognition (concrete, brick, insulation, metal patterns)
• Professional symbol interpretation with standardized meanings
• Cross-discipline coordination checking for construction conflicts

🎯 Real-World Validation

Tested with Actual Construction Documents:

• **15.6MB construction drawing se...

Created from VS Code via the GitHub Pull Request extension.

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