891c36bbfb
Major Features: - 🖥️ Standalone desktop app (VideoMatcher.app) - double-click to run - 🎨 Black & gold branded UI (Montserrat font, #FFC407 accent) - 📁 Local file browser for master/adaptation folders - ⚡ Fast mode processing (10-20x faster, disables AKAZE/AI Vision) - 🤖 Smart AI Vision fallback (auto-retry when no matches found) - 📊 Real-time progress bars (fingerprinting & matching) - 💾 Local processing (no cloud, no authentication) - 📤 CSV export with master filenames Web Application (Enterprise): - 🌐 Flask web app with Azure AD authentication - 📦 Box.com integration for cloud storage - 🐳 Docker support for deployment - 🔐 JWT validation with httpOnly cookies - 🎯 REST API endpoints Enhancements: - Fixed master filename lookup (was showing "Unknown") - Automatic fingerprint recovery (detects missing files) - Improved CSV format (master file next to adaptation) - Port conflict handling (auto-finds available port) - Environment variable fixes for standalone mode Documentation: - Updated README with standalone app section - Added 10+ guide documents (UI improvements, fingerprint recovery, etc.) - Build instructions with PyInstaller - Comprehensive troubleshooting guide Technical: - PyInstaller build configuration (video_matcher.spec) - Launcher with environment setup (launcher.py) - Mock authentication for standalone mode - Video matcher service layer - Metadata parser and AKAZE video matching 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
519 lines
17 KiB
Python
Executable file
519 lines
17 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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Batch match adaptations from a folder and generate HTML report.
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"""
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import sys
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import json
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from pathlib import Path
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from datetime import datetime
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from rich.console import Console
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from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
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# Add src to path
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sys.path.insert(0, str(Path(__file__).parent / "src"))
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from video_matcher.matcher import VideoMatcher
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console = Console()
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# Common video file extensions
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VIDEO_EXTENSIONS = {'.mp4', '.mov', '.avi', '.mkv', '.webm', '.flv', '.wmv', '.m4v'}
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def generate_html_report(results, output_path, folder_path):
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"""Generate an HTML report from matching results."""
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html_content = f"""<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Video Matching Report - {datetime.now().strftime('%Y-%m-%d %H:%M')}</title>
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<style>
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* {{
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}}
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body {{
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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padding: 20px;
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color: #333;
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}}
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.container {{
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max-width: 1400px;
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margin: 0 auto;
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}}
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.header {{
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background: white;
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padding: 30px;
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border-radius: 15px;
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box-shadow: 0 10px 30px rgba(0,0,0,0.2);
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margin-bottom: 30px;
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}}
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.header h1 {{
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color: #667eea;
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margin-bottom: 10px;
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font-size: 2.5em;
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}}
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.header .meta {{
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color: #666;
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font-size: 0.95em;
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}}
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.summary {{
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background: white;
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padding: 25px;
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border-radius: 15px;
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box-shadow: 0 10px 30px rgba(0,0,0,0.2);
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margin-bottom: 30px;
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 20px;
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}}
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.stat {{
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text-align: center;
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padding: 15px;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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border-radius: 10px;
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color: white;
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}}
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.stat-value {{
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font-size: 2.5em;
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font-weight: bold;
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display: block;
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}}
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.stat-label {{
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font-size: 0.9em;
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opacity: 0.9;
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margin-top: 5px;
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}}
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.adaptation {{
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background: white;
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padding: 25px;
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border-radius: 15px;
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box-shadow: 0 10px 30px rgba(0,0,0,0.2);
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margin-bottom: 25px;
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transition: transform 0.2s;
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}}
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.adaptation:hover {{
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transform: translateY(-2px);
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box-shadow: 0 15px 40px rgba(0,0,0,0.25);
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}}
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.adaptation-header {{
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display: flex;
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justify-content: space-between;
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align-items: center;
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margin-bottom: 20px;
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padding-bottom: 15px;
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border-bottom: 2px solid #f0f0f0;
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}}
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.adaptation-name {{
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font-size: 1.4em;
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font-weight: 600;
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color: #333;
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word-break: break-all;
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}}
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.match-count {{
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background: #667eea;
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color: white;
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padding: 8px 16px;
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border-radius: 20px;
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font-weight: 600;
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font-size: 0.9em;
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}}
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.no-matches {{
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background: #ff6b6b;
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}}
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.matches-list {{
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display: grid;
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gap: 15px;
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}}
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.match-item {{
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background: #f8f9fa;
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padding: 20px;
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border-radius: 10px;
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border-left: 5px solid #667eea;
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transition: all 0.2s;
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}}
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.match-item:hover {{
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background: #e9ecef;
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border-left-width: 8px;
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}}
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.match-header {{
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display: flex;
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justify-content: space-between;
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align-items: center;
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margin-bottom: 15px;
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}}
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.master-name {{
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font-weight: 600;
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color: #333;
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font-size: 1.1em;
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}}
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.confidence-badge {{
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padding: 6px 14px;
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border-radius: 15px;
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font-weight: 600;
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font-size: 0.85em;
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text-transform: uppercase;
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letter-spacing: 0.5px;
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}}
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.confidence-very-high {{ background: #51cf66; color: white; }}
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.confidence-high {{ background: #69db7c; color: white; }}
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.confidence-medium {{ background: #ffd43b; color: #333; }}
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.confidence-low {{ background: #ff922b; color: white; }}
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.confidence-very-low {{ background: #ff6b6b; color: white; }}
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.match-details {{
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
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gap: 12px;
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margin-top: 12px;
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}}
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.detail-item {{
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background: white;
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padding: 10px;
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border-radius: 8px;
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text-align: center;
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}}
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.detail-label {{
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font-size: 0.8em;
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color: #666;
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text-transform: uppercase;
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letter-spacing: 0.5px;
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margin-bottom: 5px;
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}}
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.detail-value {{
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font-size: 1.2em;
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font-weight: 600;
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color: #667eea;
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}}
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.no-match-message {{
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text-align: center;
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padding: 30px;
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color: #999;
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font-style: italic;
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}}
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.error-message {{
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background: #ffe0e0;
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border-left: 5px solid #ff6b6b;
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padding: 15px;
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border-radius: 8px;
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color: #c92a2a;
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}}
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.footer {{
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text-align: center;
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padding: 20px;
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color: white;
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margin-top: 30px;
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font-size: 0.9em;
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}}
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.progress-bar {{
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width: 100%;
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height: 8px;
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background: #e9ecef;
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border-radius: 4px;
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overflow: hidden;
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margin-top: 8px;
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}}
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.progress-fill {{
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height: 100%;
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background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
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transition: width 0.3s ease;
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}}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>🎬 Video Matching Report</h1>
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<div class="meta">
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<strong>Generated:</strong> {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}<br>
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<strong>Source Folder:</strong> {folder_path}
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</div>
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</div>
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<div class="summary">
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<div class="stat">
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<span class="stat-value">{len(results)}</span>
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<span class="stat-label">Adaptations Processed</span>
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</div>
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<div class="stat">
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<span class="stat-value">{sum(1 for r in results if r['matches'])}</span>
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<span class="stat-label">Matched</span>
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</div>
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<div class="stat">
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<span class="stat-value">{sum(1 for r in results if not r['matches'])}</span>
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<span class="stat-label">No Matches</span>
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</div>
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<div class="stat">
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<span class="stat-value">{sum(len(r['matches']) for r in results)}</span>
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<span class="stat-label">Total Master Matches</span>
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</div>
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<div class="stat">
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<span class="stat-value">{sum(1 for r in results for m in r.get('matches', []) if m.get('matching_method') == 'akaze')}</span>
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<span class="stat-label">AKAZE Matches</span>
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</div>
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<div class="stat">
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<span class="stat-value">{sum(1 for r in results for m in r.get('matches', []) if m.get('matching_method') == 'ai_vision')}</span>
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<span class="stat-label">AI Vision Matches</span>
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</div>
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</div>
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"""
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# Add each adaptation result
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for result in results:
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adaptation_name = result['adaptation_name']
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matches = result['matches']
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error = result.get('error')
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match_class = 'no-matches' if not matches else ''
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match_count = len(matches) if matches else 0
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html_content += f"""
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<div class="adaptation">
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<div class="adaptation-header">
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<div class="adaptation-name">{adaptation_name}</div>
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<div class="match-count {match_class}">
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{match_count} Match{'es' if match_count != 1 else ''}
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</div>
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</div>
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"""
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if error:
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html_content += f"""
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<div class="error-message">
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<strong>Error:</strong> {error}
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</div>
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"""
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elif not matches:
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html_content += """
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<div class="no-match-message">
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No matching masters found above threshold
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</div>
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"""
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else:
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html_content += """
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<div class="matches-list">
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"""
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for idx, match in enumerate(matches, 1):
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confidence = match['confidence'].lower().replace(' ', '-')
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html_content += f"""
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<div class="match-item">
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<div class="match-header">
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<div class="master-name">
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#{idx} {match['master_id']}
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</div>
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<div class="confidence-badge confidence-{confidence}">
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{match['confidence']}
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</div>
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</div>
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<div class="match-details">
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<div class="detail-item">
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<div class="detail-label">Duration</div>
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<div class="detail-value">{match['master_duration']:.0f}s</div>
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</div>
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<div class="detail-item">
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<div class="detail-label">Video Match</div>
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<div class="detail-value">{match['video_percentage']:.1f}%</div>
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</div>
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<div class="detail-item">
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<div class="detail-label">Frames</div>
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<div class="detail-value">{match['matching_frames']}/{match['total_frames']}</div>
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</div>
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<div class="detail-item">
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<div class="detail-label">Combined Score</div>
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<div class="detail-value">{match['combined_score']:.1%}</div>
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</div>
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<div class="detail-item">
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<div class="detail-label">Method</div>
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<div class="detail-value" style="font-size: 0.9em;">
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{match.get('matching_method', 'hash').upper().replace('_', ' ')}
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</div>
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</div>
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</div>
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<div class="progress-bar">
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<div class="progress-fill" style="width: {match['video_percentage']:.0f}%"></div>
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</div>
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</div>
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"""
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html_content += """
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</div>
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"""
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html_content += """
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</div>
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"""
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html_content += """
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<div class="footer">
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Generated by Video Master-Adaptation Detection Tool<br>
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🚀 Enhanced with AKAZE feature matching + metadata filtering + AI Vision fallback
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</div>
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</div>
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</body>
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</html>
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"""
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# Write HTML file
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write(html_content)
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def batch_match_folder(folder_path, threshold=0.80, frame_threshold=0.80, min_avg_similarity=0.90, output_file=None):
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"""
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Match all videos in a folder against masters and generate report.
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Args:
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folder_path: Path to folder containing adaptation videos
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threshold: Minimum percentage match threshold
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frame_threshold: Frame similarity threshold
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min_avg_similarity: Minimum average similarity of matched frames
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output_file: Output HTML file path (default: auto-generated)
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"""
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folder_path = Path(folder_path)
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if not folder_path.exists():
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console.print(f"[red]✗[/red] Folder not found: {folder_path}")
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return
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if not folder_path.is_dir():
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console.print(f"[red]✗[/red] Not a directory: {folder_path}")
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return
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# Find all video files
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video_files = []
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for ext in VIDEO_EXTENSIONS:
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video_files.extend(folder_path.glob(f"*{ext}"))
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video_files.extend(folder_path.glob(f"*{ext.upper()}"))
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if not video_files:
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console.print(f"[yellow]No video files found in {folder_path}[/yellow]")
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return
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console.print(f"\n[bold]Found {len(video_files)} video file(s) to process[/bold]\n")
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# Initialize matcher
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matcher = VideoMatcher()
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# Check if we have masters
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masters = matcher.list_masters()
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if not masters:
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console.print("[red]✗[/red] No master videos found in library.")
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console.print("Use 'python cli.py add-master' to add masters first.")
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return
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console.print(f"[cyan]Comparing against {len(masters)} master(s)...[/cyan]\n")
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# Process each video
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results = []
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
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console=console
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) as progress:
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task = progress.add_task("[cyan]Processing adaptations...", total=len(video_files))
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for video_file in video_files:
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progress.update(task, description=f"[cyan]Processing {video_file.name}...")
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try:
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matches = matcher.match_adaptation(
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str(video_file),
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threshold=threshold,
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frame_threshold=frame_threshold,
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min_avg_similarity=min_avg_similarity
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)
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results.append({
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'adaptation_name': video_file.name,
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'adaptation_path': str(video_file),
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'matches': matches,
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'error': None
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})
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except Exception as e:
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console.print(f"[red]✗[/red] Error processing {video_file.name}: {e}")
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results.append({
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'adaptation_name': video_file.name,
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'adaptation_path': str(video_file),
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'matches': [],
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'error': str(e)
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})
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progress.advance(task)
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# Generate output filename if not specified
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if output_file is None:
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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output_file = f"matching_report_{timestamp}.html"
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output_path = Path(output_file)
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# Generate HTML report
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console.print(f"\n[cyan]Generating HTML report...[/cyan]")
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generate_html_report(results, output_path, str(folder_path))
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# Summary
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console.print(f"\n[bold green]✓ Report generated successfully![/bold green]")
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console.print(f"\n[bold]Summary:[/bold]")
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console.print(f" Total adaptations: {len(results)}")
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console.print(f" Matched: {sum(1 for r in results if r['matches'])}")
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console.print(f" No matches: {sum(1 for r in results if not r['matches'])}")
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console.print(f" Total master matches: {sum(len(r['matches']) for r in results)}")
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console.print(f"\n[bold cyan]📄 Report saved to:[/bold cyan] {output_path.absolute()}")
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console.print(f"\n[dim]Open in browser: file://{output_path.absolute()}[/dim]")
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if __name__ == '__main__':
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import click
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@click.command()
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@click.argument('folder_path', type=click.Path(exists=True))
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@click.option('--threshold', '-t', default=0.80, type=float, help='Minimum percentage match (0-1)')
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@click.option('--frame-threshold', '-f', default=0.80, type=float, help='Frame similarity threshold (0-1)')
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@click.option('--min-avg-similarity', '-m', default=0.90, type=float, help='Minimum average similarity of matched frames (0-1)')
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@click.option('--output', '-o', default=None, help='Output HTML file path')
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def main(folder_path, threshold, frame_threshold, min_avg_similarity, output):
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"""Batch match all videos in a folder and generate HTML report."""
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batch_match_folder(folder_path, threshold, frame_threshold, min_avg_similarity, output)
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main()
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