Video Master-Adaptation Detection

A powerful video matching system to detect which master video files were used to create adaptation videos (cut-downs, re-edits, speed changes, crops, re-encodes, etc.).

Available in three modes: Standalone Desktop App, Web Application, and CLI Tool!

🖥️ Standalone Desktop Application (RECOMMENDED!)

The easiest way to use Video Matcher - a double-click desktop app with no server setup required!

Features:

• 🎨 Modern Black & Gold UI - Professional, branded interface
• 📁 Local File Browser - Select master and adaptation folders directly
• 🚀 Zero Configuration - No authentication, no server setup
• ⚡ Fast Mode - 10-20x faster than full analysis
• 🤖 Smart AI Fallback - Automatically retries with AI Vision when needed
• 📊 Real-time Progress - Visual progress bars during processing
• 💾 Local Processing - All data stays on your machine
• 🍎 macOS Native - Packaged as .app bundle

Quick Start (Standalone App)

Option 1: Use Pre-built App (Easiest)

# Download the standalone app
# Extract VideoMatcher.zip
# Double-click VideoMatcher.app

# That's it! The app will:
# - Open your browser automatically
# - Let you select master and adaptation folders
# - Process videos and show results

Option 2: Build from Source

# Clone the repository
cd Video_Master_Adot_Detection

# Install dependencies
pip install -r requirements.txt
pip install pyinstaller

# Build the standalone app
python build.py

# The app will be created in: dist/VideoMatcher.app

Using the Standalone App

1. Double-click VideoMatcher.app (or run ./VideoMatcher in terminal)
2. Select Master Folder - Choose folder containing your master videos
   • App scans for videos
   • Fingerprints them (with progress bar)
   • Shows count of processed masters
3. Select Adaptation Folder(s) - Choose folder(s) with adaptations
4. Start Matching - Click to begin processing
   • Real-time progress updates
   • Shows current video being processed
   • Visual progress bar
5. View Results - See which masters matched which adaptations
   • Master filename clearly displayed
   • Confidence scores and match percentages
   • AI Vision badge for cross-aspect matches
   • Export results as CSV

Stopping the App

From .app bundle:

# Option 1: Activity Monitor
# Search for "VideoMatcher" and quit

# Option 2: Terminal
pkill -f VideoMatcher

From terminal:

• Press Ctrl+C in the terminal window

Standalone App Architecture

VideoMatcher.app/
├── VideoMatcher         # Executable (launcher.py bundled)
├── _internal/           # Python runtime and dependencies
│   ├── data/            # Fingerprints and master registry
│   ├── tmp/             # Temporary processing files
│   └── [libraries]      # Flask, OpenCV, FFmpeg bindings
├── templates/           # UI templates
│   └── standalone.html  # Main interface
└── static/              # CSS/JS assets

How it works:

1. Launcher sets environment variables (standalone mode)
2. Finds available port (default: 5001 if 5000 is taken)
3. Starts Flask server on localhost
4. Opens browser automatically
5. No authentication required
6. All processing happens locally

System Requirements

• macOS: 10.13+ (High Sierra or later)
• Windows: Windows 10/11 (untested)
• Linux: Ubuntu 20.04+ (untested)
• FFmpeg: Must be installed on system

  # macOS
  brew install ffmpeg
  
  # Windows
  # Download from https://ffmpeg.org/download.html
  
  # Linux
  sudo apt-get install ffmpeg
  

• Disk Space: ~200MB for app + storage for fingerprints
• RAM: 4GB minimum, 8GB recommended

Troubleshooting (Standalone App)

Issue	Solution
Port 5000 already in use	App automatically finds next available port (5001, 5002, etc.)
403 Access Denied error	Make sure you're running the latest build with authentication disabled
"Application is not open" error	Old instance running - use pkill -f VideoMatcher to stop it
FFmpeg errors	Install FFmpeg: brew install ffmpeg
Slow fingerprinting	Normal - Fast mode processes ~5-10 seconds per video
Can't see master filenames	Update to latest version - this bug was fixed

---

🌐 Web Application (Enterprise)

The tool now includes a Flask web application with:

• 🔐 Azure AD Authentication - Secure Microsoft SSO
• 📦 Box.com Integration - Browse and process videos directly from Box storage
• 🎯 Modern UI - Beautiful, responsive interface with real-time progress
• 🐳 Docker Support - Easy deployment to AWS/Azure/GCP
• 🔄 Production Ready - Development and production configurations

Quick Start (Web App)

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your Azure AD credentials

# Run development server
python app.py

# Visit http://localhost:7183

See the Web Application Guide below for detailed setup.

✨ Key Features ✅ ENHANCED v2.1

• 🎯 Spatial-Only Matching - Ignores timing, handles speed changes & reordering
• 🔍 AKAZE Feature Matching - Robust geometric verification (NEW in v2.1)
• ⚡ Metadata Filtering - Instant 80-95% search space reduction (NEW in v2.1)
• 🤖 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 - Fast & full modes with beautiful HTML reports (ENHANCED in v2.1)
• 🎨 Rich CLI - Beautiful terminal output with tables and progress bars
• 🌍 Text/Logo Handling - Ignores different languages, logos, overlays (VERIFIED in v2.1)

🚀 Quick Start

Choose your preferred way to use the tool:

• Web Application - Modern UI with Azure AD + Box.com
• CLI Tool - Command-line interface for local use

---

🌐 Web Application Guide

Overview

The web application provides a modern interface for video matching with enterprise authentication and cloud storage integration.

Architecture:

• Frontend: Bootstrap 5 + MSAL Browser for Azure AD authentication
• Backend: Flask 3.0 with JWT validation and httpOnly cookies
• Storage: Box.com for video files, JSON for matching results
• Deployment: Docker-ready for AWS/Azure/GCP

Prerequisites (Web App)

1. Python 3.11+
2. FFmpeg & Chromaprint (same as CLI)
3. Azure AD App Registration (already configured)
4. Box.com API Credentials (JWT config file)

Installation (Web App)

# Navigate to project directory
cd Video_Master_Adot_Detection

# Create and activate virtual environment (if not already done)
python3 -m venv venv
source venv/bin/activate  # On macOS/Linux

# Install all dependencies (includes Flask, auth, Box SDK)
pip install -r requirements.txt

# Configure environment
cp .env.example .env

Configuration

1. Edit .env file:

# Flask Configuration
FLASK_APP=app.py
FLASK_ENV=development
SECRET_KEY=your-secret-key-here  # Generate with: python3 -c "import secrets; print(secrets.token_hex(32))"
PORT=7183

# Azure AD Authentication (Pre-configured)
AZURE_TENANT_ID=e519c2e6-bc6d-4fdf-8d9c-923c2f002385
AZURE_CLIENT_ID=9079054c-9620-4757-a256-23413042f1ef

# Box.com (To be configured when credentials received)
BOX_CONFIG_PATH=config/box_config.json
BOX_ROOT_FOLDER_ID=your_folder_id

# Video Processing
VIDEO_TEMP_DIR=tmp/video_downloads
MAX_VIDEOS_PER_JOB=20

# OpenAI (for AI Vision matching)
OPENAI_API_KEY=your_openai_key_here

2. Box.com Setup (when credentials are received):

# Place your Box JWT config file
mkdir -p config
# Copy box_config.json to config/box_config.json

Running the Web App

Development Mode:

# Start the development server
python app.py

# Server will run on http://localhost:7183
# Opens automatically with hot-reload enabled

Production Mode (with Gunicorn):

# Update .env
FLASK_ENV=production
DEBUG=False

# Run with Gunicorn
gunicorn -c gunicorn_config.py wsgi:app

# Or use Docker (recommended)
docker-compose up -d

Using the Web Application

1. Access the App

   • Open browser to http://localhost:7183
   • You'll see the authentication screen

2. Sign In

   • Click "Sign in with Microsoft"
   • Authenticate with your Azure AD credentials
   • You'll be redirected to the main dashboard

3. Browse Box Folders (once Box credentials are configured)

   • Navigate through your Box folders
   • Select videos to process
   • Choose matching parameters

4. Process Videos

   • Select videos from Box
   • Click "Start Matching"
   • Watch real-time progress
   • View results with confidence scores

5. View Results

   • Detailed matching reports
   • Export as HTML or JSON
   • Review all matched masters

API Endpoints

The web app exposes these REST API endpoints:

Authentication:

• POST /auth/login - Process Azure AD token
• POST /auth/logout - Clear session
• GET /auth/status - Check authentication status

Box Integration:

• GET /box/folders - List root folders
• GET /box/folders/<folder_id> - List subfolders
• GET /box/videos/<folder_id> - List videos in folder

Video Matching:

• POST /match - Start matching job
• GET /jobs/<job_id>/status - Get job status
• GET /jobs/<job_id>/results - Get job results

Utility:

• GET /health - Health check

Docker Deployment

Build and run with Docker:

# Build image
docker build -t video-matcher:latest .

# Run with docker-compose
docker-compose up -d

# View logs
docker-compose logs -f

# Stop
docker-compose down

Deploy to cloud:

# AWS Elastic Container Service
# Azure App Service
# GCP Cloud Run
# See deployment guides in DEPLOYMENT.md (coming soon)

Security Features

• 🔐 Azure AD JWT Validation - Cryptographic token verification
• 🍪 httpOnly Cookies - XSS protection
• 🔒 HTTPS in Production - TLS encryption required
• 🛡️ SameSite Cookies - CSRF protection
• 🔑 Secret Management - Environment-based configuration

Troubleshooting (Web App)

Issue	Solution
Can't sign in	Check Azure AD tenant/client ID in .env
Box not working	Verify config/box_config.json exists and is valid
Port already in use	Change PORT=7183 in .env to another port
Module not found	Run pip install -r requirements.txt
Permission denied on Box	Check Box JWT app has correct permissions

---

📋 CLI Tool Quick Start

The CLI tool provides command-line access to all video matching features.

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. Batch match entire folder (with HTML report!)
# Fast mode (recommended - 2x faster)
python batch_match_fast.py /path/to/adaptations/ report.html

# Or full mode (with AKAZE verification)
python cli.py batch-match /path/to/adaptations/ -o report.html

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

📖 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 + AKAZE detect 16:9 cropped to 1:1 or 9:16 ✅ Text/Logo Variations - All tiers ignore different subtitles, logos, overlays ✅ Language Differences - German, English, French, etc. (VERIFIED with real data) ✅ Logo Placement - Different positions, sizes, branding (VERIFIED)

📊 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

Core Documentation

• README.md - This file, quick start and overview
• DOCUMENTATION.md - Detailed technical documentation

Enhancement Documentation (v2.1) ✨ NEW

• QUICK_START_ENHANCEMENTS.md - Quick guide to new features
• ENHANCEMENTS.md - Complete technical details of enhancements
• BATCH_PROCESSING_GUIDE.md - Comprehensive batch processing guide

What's in the enhanced docs:

• AKAZE feature matching (Tier 2 verification)
• Metadata filtering (Stage 0 optimization)
• Fast vs Full batch processing modes
• Real-world test results with 46 masters & 39 adaptations
• Text/logo/language handling confirmed working
• Performance benchmarks and best practices

🎬 How It Works (Enhanced in v2.1)

Hybrid 4-Stage Architecture ✅ TESTED & VERIFIED

Stage 0: Metadata Filtering (NEW - Instant)

• Parses filenames for format (1x1, 9x16, 16x9), variant (A-F), duration
• Reduces search space by 80-95% before matching
• Zero cost, instant filtering
• Best for: Organized filename conventions

Tier 1: Perceptual Hash Pre-Filtering (Fast)

• Extracts frames at 2 frames/second (catches quick edits)
• Generates perceptual hashes (8×8 DCT)
• Creates audio fingerprint (Chromaprint)
• Compares ALL masters, finds top candidates
• Stores as JSON for reuse
• Best for: Same aspect ratio videos
• Speed: ~5-10 seconds for 46 masters

Tier 2: AKAZE Verification (NEW - Selective)

• Runs ONLY on top 5 candidates (not all masters)
• Detects AKAZE keypoints and descriptors
• Geometric verification with RANSAC homography
• Confirms or improves perceptual hash results
• Best for: Scale/rotation/perspective changes
• Speed: ~10-15 seconds for 5 candidates
• Verified: Handles text overlays, logos, different languages

Tier 3: 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!)

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/
├── app.py                          # Flask web application (NEW)
├── config.py                       # Environment configuration (NEW)
├── wsgi.py                         # WSGI entry point (NEW)
├── gunicorn_config.py              # Production server config (NEW)
├── auth_middleware.py              # Azure AD authentication (NEW)
├── jwt_validator.py                # JWT token validation (NEW)
├── box_video_client.py             # Box.com integration (Phase 2)
├── video_matcher_service.py        # Service layer (Phase 3)
├── cli.py                          # CLI interface (maintained)
├── bulk_add_masters.py             # Batch processing script (CLI)
├── batch_match.py                  # Batch matching (CLI)
├── batch_match_fast.py             # Fast batch matching (CLI)
├── requirements.txt                # Python dependencies
├── Dockerfile                      # Docker containerization (NEW)
├── docker-compose.yml              # Docker compose config (NEW)
├── .dockerignore                   # Docker ignore patterns (NEW)
├── README.md                       # This file
├── DOCUMENTATION.md                # Detailed documentation
├── static/                         # Frontend assets (NEW)
│   ├── css/
│   │   └── styles.css
│   └── js/
│       ├── auth.js                 # MSAL authentication client
│       ├── box_browser.js          # Box folder browser (Phase 2)
│       ├── video_matcher.js        # Matching interface (Phase 3)
│       └── results_display.js      # Results visualization (Phase 3)
├── templates/                      # Flask templates (NEW)
│   ├── index.html                  # Main dashboard
│   ├── match.html                  # Video selection (Phase 2)
│   ├── results.html                # Results display (Phase 3)
│   ├── 404.html                    # Error page
│   └── 500.html                    # Error page
├── src/
│   └── video_matcher/
│       ├── fingerprinter.py        # Fingerprinting & matching logic
│       ├── matcher.py              # Master management & scoring
│       ├── ai_vision.py            # AI Vision (GPT-4o) integration
│       ├── video_akaze.py          # AKAZE feature matching
│       └── metadata_parser.py      # Metadata filtering
├── data/
│   ├── fingerprints/               # Stored fingerprints (*.json)
│   ├── masters.json                # Master video database
│   └── jobs/                       # Matching job state (NEW)
├── config/                         # Configuration files (NEW)
│   └── box_config.json             # Box JWT credentials (to be added)
├── logs/                           # Application logs (NEW)
│   ├── access.log
│   └── error.log
├── tmp/                            # Temporary storage (NEW)
│   └── video_downloads/            # Downloaded videos
├── .env.example                    # Example environment config
├── .env                            # Your configuration (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 (Real-World Tested)

Stage 0: Metadata Filtering

• Time: Instant (filename parsing)
• Reduction: 80-95% when filenames follow conventions
• Cost: $0.00

Tier 1: Perceptual Hash (2 fps sampling)

• Fingerprint generation: ~3-6 seconds per minute of video
• Matching: ~5-10 seconds for 46 masters
• Library size: Works well up to ~100 masters
• Cost: $0.00

Tier 2: AKAZE Verification (NEW)

• Time: ~10-15 seconds for top 5 candidates
• Optimization: 92% reduction (5 vs 46 masters)
• Accuracy: 95-100% match rates
• Cost: $0.00

Tier 3: 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

Real-World Example: Austrian Spring Fashion Campaign ✅ TESTED

• Masters: 46 videos (various formats, 6s-20s durations)
• Adaptations: 39 videos (Austrian market, German language)
• Variations: Different text overlays, logos, localization

Results:

• Stage 0: 0% reduction (duration not in filenames)
• Tier 1: Found 3 candidates per video (100% matches)
• Tier 2: Verified all with "very_high" confidence (60+ inliers)
• Tier 3: Skipped (perfect matches found)
• Processing time: 15-25 seconds per video (full mode)
• Processing time: 8-12 seconds per video (fast mode)
• Batch time: 5-8 minutes for 39 videos (fast mode)
• Batch time: 10-15 minutes for 39 videos (full mode)
• Total cost: $0.00 (no AI Vision needed)
• Accuracy: 100% correct master identification

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

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Built with: Python, Flask, FFmpeg, Chromaprint, OpenCV AKAZE, OpenAI GPT-4o, Rich, Azure AD, Box SDK Status: Production-Ready Web App + CLI (Phase 1 Complete: Authentication) Version: 3.0.0 - Web Application with Azure AD + Box.com Integration

What's New in v3.0:

• 🌐 Flask web application with modern UI
• 🔐 Azure AD authentication (Microsoft SSO)
• 📦 Box.com integration for cloud storage
• 🐳 Docker support for easy deployment
• 🔄 Production and development configurations
• 🎯 REST API for programmatic access
• ✅ Phase 1 Complete: Authentication working
• ⏳ Phase 2 Pending: Box integration (waiting for credentials)
• ⏳ Phase 3 Pending: Matching service layer

Implementation Phases:

• Phase 1 (✅ Complete): Flask app, Azure AD auth, templates, Docker config
• Phase 2 (⏳ Pending): Box.com client, folder browsing, video selection UI
• Phase 3 (⏳ Pending): Video matcher service, job management, results display
• CLI Tool: Fully functional and maintained for local use