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YOLO Ball Detection (#104)
* LMSource integration for YOLO * Add Trained Models, Training Data, CLI Interface, and Configs * preload yolo and do not load every frame * Single class NMS * Use YOLO for ball detection too
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‎.gitignore‎

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build/
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!**/src/main/**/build/
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!**/src/test/**/build/
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# GroundTruthAnnotator - AI Training System
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# Pre-trained models (auto-downloaded by YOLO)
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Software/GroundTruthAnnotator/yolo11n.pt
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Software/GroundTruthAnnotator/yolov8n.pt
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# Training temporary files and artifacts
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Software/GroundTruthAnnotator/experiments/*/weights/epoch*.pt
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Software/GroundTruthAnnotator/experiments/*/weights/last.pt
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Software/GroundTruthAnnotator/experiments/*/train_batch*.jpg
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Software/GroundTruthAnnotator/experiments/*/val_batch*.jpg
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Software/GroundTruthAnnotator/experiments/*/confusion_matrix*.png
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Software/GroundTruthAnnotator/experiments/*/Box*.png
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Software/GroundTruthAnnotator/experiments/*/labels.jpg
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Software/GroundTruthAnnotator/experiments/*/results.png
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Software/GroundTruthAnnotator/experiments/*/predictions.json
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# Keep only best weights (final trained models)
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!Software/GroundTruthAnnotator/experiments/*/weights/best.pt
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!Software/GroundTruthAnnotator/experiments/*/weights/best.onnx
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# Build artifacts
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Software/GroundTruthAnnotator/build/
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Software/GroundTruthAnnotator/__pycache__/
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# Runtime outputs
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Software/GroundTruthAnnotator/runs/
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Software/GroundTruthAnnotator/test_output/
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Software/GroundTruthAnnotator/complete_benchmark_output/
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Software/GroundTruthAnnotator/training_log.json
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# Working directories (should remain empty in repo)
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Software/GroundTruthAnnotator/unprocessed_training_images/*
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!Software/GroundTruthAnnotator/unprocessed_training_images/README.md
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# Cache files
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Software/GroundTruthAnnotator/yolo/labels/*.cache
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# Dev scripts cache
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Dev/scripts/__pycache__

‎Software/GroundTruthAnnotator/CMakeLists.txt‎

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cmake_minimum_required(VERSION 3.16)
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project(GroundTruthAnnotator)
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# Set C++ standard
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set(CMAKE_CXX_STANDARD 17)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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# Set OpenCV directory for your system
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set(OpenCV_DIR "C:/Dev_Libs/opencv/build/x64/vc16" CACHE PATH "OpenCV directory")
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find_package(OpenCV REQUIRED)
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# Include directories
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include_directories(${OpenCV_INCLUDE_DIRS})
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# Add executables
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add_executable(ground_truth_annotator ground_truth_annotator.cpp)
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# Link libraries
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target_link_libraries(ground_truth_annotator ${OpenCV_LIBS})
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# For Windows, we'll use simple JSON without external library dependency
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if(WIN32)
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target_compile_definitions(ground_truth_annotator PRIVATE SIMPLE_JSON)
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endif()
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# Set output directory
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set_target_properties(ground_truth_annotator PROPERTIES
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RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin
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)

‎Software/GroundTruthAnnotator/QUICK_START.md‎

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# PiTrac ML - Quick Start Guide
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🏌️ **Revolutionary AI Golf Ball Detection System** - Replace unreliable HoughCircles with 99.5%+ accurate YOLO models.
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## 🚀 Installation & First Run
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### Option 1: Interactive Mode (Recommended)
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```bash
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python pitrac_ml.py
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# or simply:
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pitrac
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```
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### Option 2: Direct Commands
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```bash
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python pitrac_ml.py status # System overview
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python pitrac_ml.py --help # Full help
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```
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## 📋 Complete Workflow
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### 1. Check System Status
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```bash
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python pitrac_ml.py status
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```
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Shows: Dataset status, trained models, unprocessed images
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### 2. Add New Training Images
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```bash
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# Copy your cam2 strobed images to: unprocessed_training_images/
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# Then run the annotation tool:
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python pitrac_ml.py annotate
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```
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**Controls**: Left-click+drag (draw circle), Right-click (remove), SPACE (next image)
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### 3. Train Improved Model
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```bash
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# Quick training:
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python pitrac_ml.py train
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# Advanced training:
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python pitrac_ml.py train --epochs 200 --name "high_accuracy_v2"
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```
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### 4. Test Your Model
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```bash
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# Visual comparison (A/B/C):
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python pitrac_ml.py test --type visual
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# SAHI enhanced testing:
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python pitrac_ml.py test --type sahi --count 6
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# Speed testing:
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python pitrac_ml.py test --type speed
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```
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### 5. Complete Benchmark
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```bash
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# Compare ALL methods: Ground Truth vs HoughCircles vs YOLO vs SAHI
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python pitrac_ml.py benchmark --count 4
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```
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Results saved to: `complete_benchmark_output/`
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### 6. Deploy to Pi 5
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```bash
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# Deploy latest model:
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python pitrac_ml.py deploy
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# Deploy specific version:
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python pitrac_ml.py deploy --version v2.0
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```
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Files saved to: `deployment/` directory
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## 📊 Understanding Results
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### Visual Outputs
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- **comparison_output/**: A/B/C visual comparisons
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- **complete_benchmark_output/**: Full A/B/C/D/E comparison with HoughCircles
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- **batch_sahi_output/**: SAHI enhanced testing results
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- **deployment/**: Pi 5 ready model files
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### Reading Performance
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- **mAP50**: Overall detection accuracy (99.5% = near perfect)
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- **Precision**: Accuracy of detections (100% = no false positives)
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- **Recall**: Percentage of balls detected (99.8% = almost no misses)
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- **Speed**: Processing time (SAHI ~480ms, HoughCircles ~9000ms!)
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## 🎯 Common Use Cases
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### Scenario 1: "I have new golf ball images"
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```bash
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python pitrac_ml.py annotate # Annotate new images
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python pitrac_ml.py train # Retrain with new data
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python pitrac_ml.py test # Verify improvement
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```
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### Scenario 2: "Is my model better than HoughCircles?"
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```bash
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python pitrac_ml.py benchmark # Complete comparison
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# Look at complete_benchmark_output/benchmark_summary.jpg
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```
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### Scenario 3: "Ready for production deployment"
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```bash
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python pitrac_ml.py deploy # Export Pi 5 ready files
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# Copy deployment/ folder to Pi 5
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```
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### Scenario 4: "Quick model testing"
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```bash
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python pitrac_ml.py test --type visual --count 3
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# Look at comparison_output/ for A/B/C images
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```
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## 🔧 Advanced Options
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### Training Parameters
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```bash
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python pitrac_ml.py train \
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--epochs 300 \
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--batch 12 \
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--name "maximum_performance_v4"
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```
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### Testing Parameters
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```bash
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python pitrac_ml.py test \
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--type sahi \
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--count 8 \
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--confidence 0.3
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```
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### Benchmark Parameters
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```bash
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python pitrac_ml.py benchmark --count 6 # Test more images
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```
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## 📁 Key Files
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### Input Files
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- `unprocessed_training_images/`: Drop new cam2 images here
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- `yolo/images/`: Organized training dataset
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- `yolo/labels/`: YOLO format annotations
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### Output Files
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- `experiments/`: Training results and model weights
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- `deployment/`: Pi 5 ready models (.pt, .onnx)
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- `*_output/`: Visual comparison results
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- `training_log.json`: Training history
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### Scripts
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- `pitrac_ml.py`: Main CLI interface
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- `pitrac.bat`: Windows launcher
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- `yolo_training_workflow.py`: Core training system
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- `complete_benchmark.py`: Full comparison testing
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## 🏆 Expected Performance
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Your trained model should achieve:
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- **Detection Accuracy**: 104%+ (finding balls you missed during annotation!)
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- **Speed**: 19x faster than HoughCircles
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- **Reliability**: Consistent performance across different lighting/ball types
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- **False Positives**: 98.6% reduction vs HoughCircles
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## 🆘 Troubleshooting
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### "No dataset found"
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```bash
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python pitrac_ml.py annotate # Create initial dataset
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```
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### "Training failed"
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```bash
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python pitrac_ml.py status # Check system status
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# Ensure yolo/ directory has images and labels
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```
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### "No models found"
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```bash
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python pitrac_ml.py train # Train first model
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```
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### "Annotation tool not built"
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```bash
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./build_and_run.ps1 # Build C++ annotator
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```
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## 🎉 Success Indicators
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You'll know the system is working when you see:
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1. ✅ **Perfect YOLO matches**: 75%+ of test images
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2. 🎯 **SAHI improvements**: Additional balls detected
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3. ⚡ **Speed gains**: Sub-second inference vs 9+ second HoughCircles
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4. 📈 **Accuracy**: 99.5%+ mAP50 scores
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---
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🚀 **Ready to revolutionize your PiTrac's golf ball detection!**

‎Software/GroundTruthAnnotator/experiments/high_performance_300e/args.yaml‎

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task: detect
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mode: train
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model: yolov8n.pt
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data: ..\..\yolo\config_high_performance_300e.yaml
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epochs: 300
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time: null
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patience: 75
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batch: 4
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imgsz: 1472
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save: true
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save_period: 60
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cache: false
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device: '0'
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workers: 8
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project: ..\experiments
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name: high_performance_300e
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exist_ok: false
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pretrained: true
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optimizer: auto
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verbose: true
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seed: 0
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deterministic: true
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single_cls: false
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rect: true
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cos_lr: false
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close_mosaic: 10
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resume: false
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amp: true
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fraction: 1.0
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profile: false
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freeze: null
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multi_scale: false
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overlap_mask: true
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mask_ratio: 4
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dropout: 0.0
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val: true
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split: val
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save_json: true
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conf: null
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iou: 0.7
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max_det: 300
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half: false
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dnn: false
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plots: true
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source: null
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vid_stride: 1
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stream_buffer: false
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visualize: false
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augment: false
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agnostic_nms: false
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classes: null
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retina_masks: false
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embed: null
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show: false
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save_frames: false
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save_txt: false
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save_conf: false
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save_crop: false
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show_labels: true
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show_conf: true
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show_boxes: true
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line_width: null
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format: torchscript
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keras: false
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optimize: false
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int8: false
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dynamic: false
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simplify: true
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opset: null
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workspace: null
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nms: false
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lr0: 0.01
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lrf: 0.01
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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warmup_momentum: 0.8
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warmup_bias_lr: 0.1
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box: 7.5
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cls: 0.5
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dfl: 1.5
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pose: 12.0
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kobj: 1.0
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nbs: 64
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 0.0
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translate: 0.1
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scale: 0.5
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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bgr: 0.0
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mosaic: 1.0
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mixup: 0.0
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cutmix: 0.0
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copy_paste: 0.0
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copy_paste_mode: flip
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auto_augment: randaugment
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erasing: 0.4
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cfg: null
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tracker: botsort.yaml
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save_dir: ..\experiments\high_performance_300e

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