SETUP_COMPLETE.md

🏀 Basketball Analysis System - Setup Complete! ✅

System Status: READY FOR TESTING

Your basketball analysis system is now fully configured and ready to test with the pre-trained models from the basketball_analysis repository.

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✅ What's Been Set Up

1. Pre-trained Models (All Downloaded & Verified)

• ✓ player_detector.pt (164.65 MB) - YOLO v11 player detection
• ✓ ball_detector_model.pt (164.67 MB) - YOLO v5 ball detection
• ✓ court_keypoint_detector.pt (398.37 MB) - YOLO v8 court keypoints

2. Dependencies (All Installed)

• ✓ OpenCV 4.13.0
• ✓ NumPy 2.3.5
• ✓ Pandas 3.0.0
• ✓ PyTorch 2.10.0+cpu (CPU version for compatibility)
• ✓ Ultralytics 8.4.9
• ✓ Supervision 0.27.0
• ✓ Transformers 5.0.0
• ✓ Pillow 12.0.0

3. Test Videos Available

• ✓ video_1.mp4 (4.24 MB)
• ✓ video_2.mp4 (6.69 MB)
• ✓ video_3.mp4 (9.06 MB)
• ✓ video_4.mp4 (6.60 MB)

4. Testing Scripts Created

• ✓ test_system.py - Comprehensive system testing
• ✓ run.sh - Convenient bash wrapper
• ✓ TESTING_GUIDE.md - Detailed testing guide
• ✓ QUICK_REFERENCE.md - Quick command reference

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🚀 Quick Start - Test the System Now!

Option 1: Using the Convenience Script (Easiest)

# Navigate to back-end directory
cd /home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end

# Run analysis on first test video
./run.sh input_videos/video_1.mp4

Option 2: Using Python Directly

# Navigate to back-end directory
cd /home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end

# Activate virtual environment
source venv/bin/activate

# Run analysis
python main.py input_videos/video_1.mp4

Option 3: Using the Test Script

cd /home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end

source venv/bin/activate

# Run full test
python test_system.py

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📊 What the Analysis Does

The system performs comprehensive basketball video analysis:

1. Player Detection & Tracking - Identifies and tracks all players
2. Ball Detection & Tracking - Tracks basketball with smooth interpolation
3. Court Keypoint Detection - Identifies court lines and zones
4. Team Assignment - Classifies players by jersey color
5. Ball Possession - Determines who has the ball
6. Pass Detection - Identifies passes between players
7. Interception Detection - Detects intercepted passes
8. Tactical View - Creates top-down mini-map
9. Speed & Distance - Calculates player movement metrics

Output Features

The analyzed video will include:

• Player bounding boxes (color-coded by team)
• Ball tracking visualization
• Court keypoint overlays
• Team ball control statistics
• Pass and interception markers
• Tactical view (mini-map)
• Player speed and distance metrics
• Frame numbers

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⏱️ Expected Processing Time

First Run (No Cached Data)

• CPU Only: ~5-15 minutes per minute of video
• The system will create "stubs" (cached intermediate results)

Subsequent Runs (With Cached Data)

• Much Faster: Reuses cached player/ball detections
• Only recomputes final visualization

Performance Tips

• Start with short clips (10-30 seconds) for faster testing
• Keep the stubs/ directory for faster re-runs
• Delete stubs/ to force fresh analysis

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📁 Output Location

Analyzed videos will be saved to:

/home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end/output_videos/

Default output filename: output_video.avi (or custom name if specified)

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🎥 Adding Your Own Test Videos

1. Place basketball video files in:

   /home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end/input_videos/
   

2. Supported formats: .mp4, .avi

3. Recommended video characteristics:

   • Resolution: 720p or 1080p
   • Frame rate: 30fps or higher
   • Clear view of basketball court
   • Good lighting
   • Stable camera angle

4. Run analysis:

   ./run.sh input_videos/your_video.mp4
   

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🔍 System Verification

All checks passed ✅:

• All dependencies installed
• All models present and loading correctly
• Test videos available
• Directory structure set up
• Virtual environment configured

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📖 Documentation

• TESTING_GUIDE.md - Comprehensive testing guide with troubleshooting
• QUICK_REFERENCE.md - Quick command reference
• test_system.py - Automated system testing
• run.sh - Convenience script for running analysis

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🐛 Troubleshooting

If you encounter issues:

1. Ensure virtual environment is activated:

   source venv/bin/activate
   

2. Check system status:

   python test_system.py --check-only
   

3. View detailed logs: Check console output for error messages

4. Clear cache and retry:

   rm -rf stubs/
   python main.py input_videos/video_1.mp4
   

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📝 Notes

• CPU vs GPU: Currently using PyTorch CPU version for compatibility

  • Processing will be slower than GPU but fully functional
  • If you have CUDA-capable GPU and want to use it, you can reinstall PyTorch with CUDA support

• Stub Caching: The system caches intermediate results in stubs/

  • First run: Slower (creates cache)
  • Subsequent runs: Much faster (uses cache)
  • Delete stubs/ to force fresh analysis

• Video Quality: Better quality input videos = better detection results

  • Clear court view
  • Good lighting
  • Stable camera
  • Visible players and ball

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🎯 Next Steps

1. Test the System (NOW!)

cd /home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end
./run.sh input_videos/video_1.mp4

2. Review Output

• Check output_videos/ for the analyzed video
• Verify all analysis features are working
• Test with different input videos

3. Integration Planning

Once testing is successful:

• Integrate with FastAPI backend
• Add video upload endpoints
• Implement async processing
• Store results in Supabase
• Connect to frontend

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🎉 Ready to Go!

Your system is fully prepared and ready for testing. Run your first analysis now:

cd /home/okidi6/Documents/Personalised-AI-Basketball-Skill-Analysis-System./back-end
./run.sh input_videos/video_1.mp4

Good luck with your testing! 🏀

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📞 Support Resources

• TESTING_GUIDE.md - Detailed testing instructions
• QUICK_REFERENCE.md - Command reference
• Original Repository: https://github.com/abdullahtarek/basketball_analysis
• System Check: python test_system.py --check-only

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System Status: ✅ READY FOR TESTING

Last Verified: 2026-02-01 13:16 UTC

All Systems: ✅ GO!