punimtag/DEMO.md
2025-09-15 12:16:01 -04:00

4.4 KiB

🎬 PunimTag Complete Demo Guide

🎯 Quick Client Demo (10 minutes)

Perfect for: Client presentations, showcasing enhanced face recognition features


🚀 Setup (2 minutes)

1. Prerequisites

cd /home/beast/Code/punimtag
source venv/bin/activate  # Always activate first!
sudo apt install feh      # Image viewer (one-time setup)

2. Prepare Demo

# Clean start
rm -f demo.db

# Check demo photos (should have 6+ photos with faces)
find demo_photos/ -name "*.jpg" -o -name "*.png" | wc -l

🎭 Client Demo Script (8 minutes)

Opening (30 seconds)

"I'll show you PunimTag - an enhanced face recognition tool that runs entirely on your local machine. It features visual face identification and intelligent cross-photo matching."

Step 1: Scan & Process (2 minutes)

# Scan photos
python3 photo_tagger.py scan demo_photos --recursive --db demo.db -v

# Process for faces  
python3 photo_tagger.py process --db demo.db -v

# Show results
python3 photo_tagger.py stats --db demo.db

Say: "Perfect! It found X photos and detected Y faces automatically."

Step 2: Visual Face Identification (3 minutes)

python3 photo_tagger.py identify --show-faces --batch 3 --db demo.db

**Key points to mention:**s

  • "Notice how it shows individual face crops - no guessing!"
  • "Each face opens automatically in the image viewer"
  • "You see exactly which person you're identifying"

Step 3: Smart Auto-Matching (3 minutes)

python3 photo_tagger.py auto-match --show-faces --db demo.db

Key points to mention:

  • "Watch how it finds the same people across different photos"
  • "Side-by-side comparison with confidence scoring"
  • "Only suggests logical cross-photo matches"
  • "Color-coded confidence: Green=High, Yellow=Medium, Red=Low"

Step 4: Search & Results (1 minute)

# Search for identified person
python3 photo_tagger.py search "Alice" --db demo.db

# Final statistics
python3 photo_tagger.py stats --db demo.db

Say: "Now you can instantly find all photos containing any person."


🎯 Key Demo Points for Clients

Privacy-First: Everything runs locally, no cloud services
Visual Interface: See actual faces, not coordinates
Intelligent Matching: Cross-photo recognition with confidence scores
Professional Quality: Color-coded confidence, automatic cleanup
Easy to Use: Simple commands, clear visual feedback
Fast & Efficient: Batch processing, smart suggestions


🔧 Advanced Features (Optional)

Confidence Control

# Strict matching (high confidence only)
python3 photo_tagger.py auto-match --tolerance 0.3 --show-faces --db demo.db

# Automatic high-confidence identification
python3 photo_tagger.py auto-match --auto --show-faces --db demo.db

Twins Detection

# Include same-photo matching (for twins)
python3 photo_tagger.py auto-match --include-twins --show-faces --db demo.db

📊 Confidence Guide

Level Color Description Recommendation
80%+ 🟢 Very High - Almost Certain Accept confidently
70%+ 🟡 High - Likely Match Probably correct
60%+ 🟠 Medium - Possible Review carefully
50%+ 🔴 Low - Questionable Likely incorrect
<50% Very Low - Unlikely Filtered out

🚨 Demo Troubleshooting

If no faces display:

  • Check feh installation: sudo apt install feh
  • Manually open: feh /tmp/face_*_crop.jpg

If no auto-matches:

  • Ensure same people appear in multiple photos
  • Lower tolerance: --tolerance 0.7

If confidence seems low:

  • 60-70% is normal for different lighting/angles
  • 80%+ indicates excellent matches

🎪 Complete Demo Commands

# Full demo workflow
source venv/bin/activate
rm -f demo.db
python3 photo_tagger.py scan demo_photos --recursive --db demo.db -v
python3 photo_tagger.py process --db demo.db -v
python3 photo_tagger.py stats --db demo.db
python3 photo_tagger.py identify --show-faces --batch 3 --db demo.db
python3 photo_tagger.py auto-match --show-faces --db demo.db
python3 photo_tagger.py search "Alice" --db demo.db
python3 photo_tagger.py stats --db demo.db

Or use the interactive script:

./demo.sh

🎉 Demo Complete! Clients will see a professional-grade face recognition system with visual interfaces and intelligent matching capabilities.