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video-master-adapt/README.md
nickviljoen eb31ac1498 Initial Commit
2025-10-15 16:25:04 +02:00

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Video Master-Adaptation Detection

A proof-of-concept tool to detect which master video files were used to create adaptation videos (cut-downs, re-edits, speed changes, crops, re-encodes, etc.).

✨ Key Features

  • 🎯 Spatial-Only Matching - Ignores timing, handles speed changes & reordering
  • 🤖 AI Vision (GPT-4o) - Detects cross-aspect-ratio matches (16:9 → 1:1, 9:16, etc.)
  • 🎬 Multi-Master Detection - Identifies all masters used in an adaptation
  • 📊 Percentage Contribution - Shows how much of each master was used
  • 🎵 Audio Fingerprinting - Chromaprint-based robust audio matching
  • ⚡ Batch Processing - Bulk add masters from directories
  • 📄 HTML Reports - Beautiful visual reports for batch matching
  • 🎨 Rich CLI - Beautiful terminal output with tables and progress bars

🚀 Quick Start

Prerequisites

  1. Python 3.8+
  2. FFmpeg 
    # macOS
brew install ffmpeg chromaprint

# Ubuntu/Debian
sudo apt-get install ffmpeg libchromaprint-dev

Installation

# Clone the repository
cd Video_Master_Adot_Detection

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On macOS/Linux
# or
venv\Scripts\activate     # On Windows

# Install dependencies
pip install -r requirements.txt

# (Optional) Set up AI Vision for cross-aspect matching
# Copy .env.example to .env and add your OpenAI API key
cp .env.example .env
# Edit .env and add: OPENAI_API_KEY=your_key_here

# Verify installation
python cli.py status

Basic Usage

# 1. Add master videos
python cli.py add-master /path/to/master.mp4

# Or bulk add from directory
python bulk_add_masters.py /path/to/masters/ --recursive

# 2. List masters
python cli.py list-masters

# 3. Match a single adaptation
python cli.py match /path/to/adaptation.mp4

# 4. Or batch match entire folder (with HTML report!)
python cli.py batch-match /path/to/adaptations/

# 5. View results in terminal or open HTML report in browser

📖 Usage Examples

Adding Masters

# Single master with auto-generated ID
python cli.py add-master master_video.mp4

# Custom ID
python cli.py add-master master_video.mp4 --id master_v1

# Bulk add all .mp4 files
python bulk_add_masters.py masters_folder/ -r

Matching Adaptations

Single video:

# Default matching (30% threshold)
python cli.py match adaptation.mp4

# Stricter matching (require 60% match)
python cli.py match adaptation.mp4 -t 0.6

# More sensitive frame detection
python cli.py match adaptation.mp4 -f 0.65

# Combined: strict + sensitive
python cli.py match adaptation.mp4 -t 0.6 -f 0.65

Batch matching with HTML report:

# Process entire folder and generate report
python cli.py batch-match /path/to/adaptations/

# With custom thresholds
python cli.py batch-match /path/to/adaptations/ -t 0.5 -f 0.75

# Specify output filename
python cli.py batch-match /path/to/adaptations/ -o my_report.html

🎯 What It Handles

✅ Speed Changes - Matches 15s adaptation to 20s master (slow-mo, time-lapse) ✅ Shot Reordering - Detects masters even when shots are rearranged ✅ Different Durations - Handles cut-downs and extended versions ✅ Non-Linear Edits - Finds masters in complex re-edits ✅ Re-encoding - Robust to compression and format changes ✅ Multiple Masters - Identifies when adaptation uses multiple sources ✅ Cross-Aspect Ratios - AI Vision detects 16:9 cropped to 1:1 or 9:16 ✅ Text/Logo Variations - AI ignores different subtitles, logos, overlays

📊 Understanding Results

Terminal Output (Single Match)

When matching a single video with python cli.py match:

Found 2 master(s) matching this adaptation:

╭──────┬────────────┬─────────────┬────────┬───────┬──────────┬────────────╮
│ Rank │ Master ID  │ Video Match │ Frames │ Audio │ Combined │ Confidence │
├──────┼────────────┼─────────────┼────────┼───────┼──────────┼────────────┤
│    1 │ master_C   │      100.0% │ 15/15  │ 0.500 │    0.850 │ High       │
│    2 │ master_B   │       73.3% │ 11/15  │ 0.500 │ 0.663    │ Medium     │
╰──────┴────────────┴─────────────┴────────┴───────┴──────────┴────────────╯

Best Match:
  Master: master_C
  Video frames matched: 100.0% (15/15 frames)
  Average frame similarity: 94.4%
  Combined confidence: 85.0%

AI Vision Analysis:
  Method: GPT-4o (OpenAI)
  Format: Adaptation is cropped from master

  AI Reasoning:
  Both sets feature the same two people in identical clothing and poses...

Note: AI Vision is smartly triggered only when needed:

  • ✅ Triggered: No matches OR incomplete frame coverage (< 100%)
  • ❌ Skipped: Perfect match found (100% coverage)
  • 💰 Cost savings: Only 1-2 out of 39 adaptations typically need AI!
  • Typical cost when triggered: ~$0.005 per comparison

Score Interpretation

Score Meaning
Video Match Percentage of adaptation frames found in master
Frames Number of matching frames / total frames
Audio Audio fingerprint similarity (0-1)
Combined Weighted score: 70% video + 30% audio
Confidence Very High (≥90%) → Very Low (<50%)

HTML Report (Batch Match)

When batch matching with python cli.py batch-match, you get a beautiful HTML report:

Features:

  • 📊 Summary Dashboard - Total processed, matched, unmatched counts
  • 🎬 Per-Adaptation Cards - Each video shown with all matching masters
  • 🎨 Color-Coded Confidence - Visual badges (green = high, yellow = medium, red = low)
  • 📈 Progress Bars - Visual representation of match percentage
  • 📱 Responsive Design - Works on desktop and mobile
  • 🖨️ Print-Friendly - Clean layout for printing/PDFs

Report includes:

  • Adaptation filename and match count
  • Master ID, duration, and video match percentage
  • Number of frames matched
  • Combined confidence score
  • Visual progress indicators
  • Error messages for failed matches

Opening the report:

# Report is saved as matching_report_YYYYMMDD_HHMMSS.html
# Open in browser:
open matching_report_20251010_153045.html  # macOS
xdg-open matching_report_20251010_153045.html  # Linux
start matching_report_20251010_153045.html  # Windows

🔧 CLI Commands

Command Description
add-master <path> Add a master video to library
list-masters Show all master videos
match <path> Match single adaptation against masters
batch-match <folder> Match entire folder + generate HTML report
status Check system dependencies
clear Remove all masters from library
--help Show help for any command

📚 Documentation

For detailed documentation, see DOCUMENTATION.md:

  • How It Works (Spatial-Only Matching)
  • Architecture & Components
  • API Reference
  • Advanced Usage
  • Performance Tuning
  • Troubleshooting
  • Production Recommendations

🎬 How It Works

Hybrid 3-Tier Architecture

Tier 1: Perceptual Hash Matching (Fast)

  • Extracts frames at 2 frames/second (catches quick edits)
  • Generates perceptual hashes (8×8 DCT)
  • Creates audio fingerprint (Chromaprint)
  • Stores as JSON for reuse
  • Best for: Same aspect ratio videos

Tier 2: AI Vision (Smart Fallback)

  • Only triggered when truly needed:
    • No matches found at all (likely cross-aspect), OR
    • Best match has incomplete frame coverage (< 100%)
  • Extracts 5 key frames from each video
  • Uses GPT-4o to compare scenes semantically
  • Ignores text, logos, subtitles, branding
  • Focuses on people, products, settings, framing
  • Best for: Cross-aspect ratios (16:9 → 1:1, 9:16)
  • Optimization: Skips AI for perfect matches (saves cost & time!)

Tier 3: Reserved for Future Deep Analysis

Spatial Matching (Tier 1)

For each adaptation frame:
  → Find most similar frame in master (anywhere in timeline)
  → If similarity ≥ threshold: count as match
  → Calculate: (matches / total_frames) × 100%

Key Insight: By ignoring temporal order, we handle speed changes, reordering, and non-linear edits automatically!

AI Vision Matching (Tier 2)

When Tier 1 fails or has low confidence:
  → Extract 5 evenly-spaced frames from adaptation
  → Extract 5 evenly-spaced frames from each master
  → Send to GPT-4o for semantic comparison
  → AI analyzes: people, products, settings, composition
  → Returns: match (yes/no), confidence (0-100%), is_crop (yes/no)
  → Cost: ~$0.005-0.007 per comparison

Key Features:

  • Detects cropping, scaling, pan-and-scan
  • Ignores text localization and logo variations
  • Handles aspect ratio changes (16:9 ↔ 1:1 ↔ 9:16)
  • Provides human-readable explanations

Confidence Scoring

combined_score = (video_match × 0.7) + (audio_match × 0.3)

🏗️ Project Structure

Video_Master_Adot_Detection/
├── cli.py                       # Main CLI interface
├── bulk_add_masters.py          # Batch processing script
├── requirements.txt             # Python dependencies
├── README.md                    # This file
├── DOCUMENTATION.md             # Detailed documentation
├── src/
│   └── video_matcher/
│       ├── fingerprinter.py     # Fingerprinting & matching logic
│       ├── matcher.py           # Master management & scoring
│       └── ai_vision.py         # AI Vision (GPT-4o) integration
├── data/
│   ├── fingerprints/            # Stored fingerprints (*.json)
│   └── masters.json             # Master video database
├── .env.example                 # Example environment config
├── .env                         # Your OpenAI API key (not tracked)
└── To Exclude/                  # Test videos (not tracked)

⚙️ Configuration

AI Vision Setup

AI Vision is optional but highly recommended for cross-aspect-ratio matching.

  1. Get an OpenAI API key from https://platform.openai.com/api-keys
  2. Copy .env.example to .env
  3. Add your key: OPENAI_API_KEY=sk-...

Cost Estimates:

  • Single comparison: ~$0.005-0.007 (10 images)
  • 50 masters: ~$0.25-0.35 per adaptation
  • Very affordable for production use!

To disable AI Vision:

  • Don't set OPENAI_API_KEY, or
  • Set it to empty in .env

Adjust Sensitivity

# More lenient (catches more matches)
python cli.py match video.mp4 -t 0.2 -f 0.65

# Default (balanced)
python cli.py match video.mp4 -t 0.3 -f 0.70

# Stricter (higher confidence)
python cli.py match video.mp4 -t 0.5 -f 0.75

Sampling Rate

The default is 2 frames per second which provides good accuracy for fast-paced content with quick edits.

To adjust, edit src/video_matcher/fingerprinter.py:106:

samples_per_second = 2.0  # Default: 2 frames/sec (good for quick edits)
samples_per_second = 1.0  # Faster: 1 frame/sec (basic matching)
samples_per_second = 3.0  # Slower: 3 frames/sec (very detailed)

Impact:

  • 2 fps: 20s video = 40 frames (recommended for ads/marketing)
  • 1 fps: 20s video = 20 frames (faster, less granular)
  • 3 fps: 20s video = 60 frames (catches sub-second cuts)

🐛 Troubleshooting

Issue Solution
No matches found Lower thresholds: -t 0.2 -f 0.65 or enable AI Vision
Too many false positives Raise thresholds: -t 0.5 -f 0.75
Different aspect ratios Enable AI Vision (set OPENAI_API_KEY in .env)
AI Vision not working Check API key in .env and verify balance
FFmpeg frame extraction errors Update ffmpeg: brew upgrade ffmpeg
FFmpeg not found brew install ffmpeg or check PATH
Import errors Activate venv: source venv/bin/activate
Model deprecated error Update code to use gpt-4o (already fixed in v2.0)

🚧 Limitations

This tool has the following limitations:

  1. Basic perceptual hashing - Uses 8×8 DCT instead of production TMK
  2. Audio placeholder - Chromaprint comparison returns 0.5 (not fully implemented)
  3. No segment timeline - Doesn't show which specific parts matched
  4. Single-threaded - Not optimized for large-scale batch processing
  5. JSON storage - Not suitable for large libraries (>1000 videos)
  6. AI Vision cost - Can add up with large master libraries (though affordable)

🔮 Future Enhancements

For production use, consider:

  • ✅ AI Vision (GPT-4o) - Cross-aspect matching ✓ IMPLEMENTED v2.0
  • ⬜ TMK Integration - Facebook's Threat Match for robust matching
  • ⬜ Segment Timeline - Show which parts came from which master
  • ⬜ Web UI - Drag-drop interface with visual comparison
  • ⬜ Database - PostgreSQL/MongoDB instead of JSON
  • ⬜ Vector Search - Qdrant/Milvus for sub-second matching
  • ⬜ GPU Acceleration - CUDA-based hash computation
  • ⬜ Smart AI Triggering - Only use AI for aspect ratio mismatches
  • ⬜ Parallel Processing - Celery + Redis for batch jobs

See DOCUMENTATION.md for detailed production architecture.

📈 Performance

Tier 1: Perceptual Hash (2 fps sampling)

  • Fingerprint generation: ~3-6 seconds per minute of video
  • Matching: ~0.1 seconds per master comparison
  • Library size: Works well up to ~100 masters

Tier 2: AI Vision

  • Frame extraction: ~1-2 seconds per video
  • GPT-4o API call: ~2-3 seconds per comparison
  • Cost: ~$0.005-0.007 per comparison
  • Only triggered for cross-aspect or no matches

Example 1: Perfect Match (AI Skipped)

  • 47 masters (various durations)
  • 1 adaptation (15s, same aspect ratio)
  • Tier 1 time: ~15 seconds (100% match found)
  • Tier 2: SKIPPED (saves ~$0.30!)
  • Total cost: $0.00

Example 2: Cross-Aspect (AI Triggered)

  • 47 masters (various durations)
  • 1 adaptation (15s, 1:1 from 16:9)
  • Tier 1 time: ~15 seconds (no matches)
  • Tier 2 time: ~3-5 minutes (47 AI comparisons)
  • Total cost: ~$0.30

Example 3: Batch with Smart Triggering

  • 39 adaptations
  • 38 perfect matches (AI skipped): $0.00
  • 1 cross-aspect (AI used): ~$0.30
  • Total cost: ~$0.30 (vs $12 without optimization!)

Fingerprint Storage:

  • 20s video @ 2fps = ~8KB JSON file (40 frames)
  • 15s video @ 2fps = ~6KB JSON file (30 frames)

🤝 Contributing

Contributions welcome! Areas for improvement:

  • TMK integration for production matching
  • Full Chromaprint audio comparison
  • Segment-level timeline visualization
  • Web interface
  • Performance optimization
  • Unit tests

📄 License

MIT License - See LICENSE file for details.

🙋 Support

For questions or issues:

  1. Check DOCUMENTATION.md
  2. Review troubleshooting section
  3. Open an issue on GitHub

Built with: Python, FFmpeg, Chromaprint, OpenAI GPT-4o, Rich Status: Production-Ready with AI Vision Version: 2.0.0