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+# Project Plan Summary - Microscopy Object Detection Application
+
+## Executive Summary
+
+This document provides a high-level overview of the planned microscopy object detection application. The application will enable users to train, validate, and deploy YOLOv8-based object detection models for microscopy images, with comprehensive GUI, database storage, and visualization capabilities.
+
+## Project Goals
+
+1. **Detect Objects**: Identify organelles and membrane branching structures in microscopy images
+2. **Train Models**: Fine-tune YOLOv8s on custom datasets with YOLO format annotations
+3. **Manage Results**: Store and query detection results in SQLite database
+4. **Visualize Data**: Interactive plots and image viewers for analysis
+5. **Export Results**: Flexible export options (CSV, JSON, Excel)
+6. **Future Annotation**: Provide manual annotation capabilities for dataset preparation
+
+## Key Technologies
+
+| Component | Technology | Purpose |
+|-----------|------------|---------|
+| ML Framework | Ultralytics YOLOv8 | Object detection model |
+| GUI | PySide6 | Desktop application interface |
+| Visualization | pyqtgraph | Real-time plots and charts |
+| Database | SQLite | Detection results storage |
+| Image Processing | OpenCV, Pillow | Image manipulation |
+
+## Application Architecture
+
+### Three-Layer Architecture
+
+```
+┌─────────────────────────────────────┐
+│         GUI Layer (PySide6)         │
+│  - Main Window with Tabs            │
+│  - Dialogs & Custom Widgets         │
+└──────────────┬──────────────────────┘
+               │
+┌──────────────▼──────────────────────┐
+│      Business Logic Layer           │
+│  - YOLO Wrapper                     │
+│  - Inference Engine                 │
+│  - Database Manager                 │
+│  - Config Manager                   │
+└──────────────┬──────────────────────┘
+               │
+┌──────────────▼──────────────────────┐
+│         Data Layer                  │
+│  - SQLite Database                  │
+│  - File System (images, models)     │
+│  - YOLOv8 Model Files               │
+└─────────────────────────────────────┘
+```
+
+## Core Features
+
+### 1. Detection Tab ✨
+- **Single Image Detection**: Select and detect objects in one image
+- **Batch Processing**: Process entire folders of images
+- **Real-time Preview**: View detections with bounding boxes
+- **Confidence Control**: Adjustable threshold slider
+- **Database Storage**: Automatic saving of results with metadata
+
+### 2. Training Tab 🎓
+- **Dataset Selection**: Browse for YOLO format datasets
+- **Parameter Configuration**: Epochs, batch size, image size, learning rate
+- **Progress Monitoring**: Real-time training metrics and loss curves
+- **Model Versioning**: Automatic model naming and version tracking
+- **Validation Metrics**: Track mAP, precision, recall during training
+
+### 3. Validation Tab 📊
+- **Model Evaluation**: Validate models on test datasets
+- **Metrics Visualization**: 
+  - Confusion matrix
+  - Precision-Recall curves
+  - Class-wise performance
+- **Model Comparison**: Compare multiple model versions
+
+### 4. Results Tab 📈
+- **Detection Browser**: Searchable table of all detections
+- **Advanced Filtering**: By date, model, class, confidence
+- **Statistics Dashboard**:
+  - Detection count by class
+  - Confidence distribution
+  - Timeline visualization
+- **Export Options**: CSV, JSON, Excel formats
+
+### 5. Annotation Tab 🖊️ (Future Feature)
+- **Image Browser**: Navigate through image repository
+- **Drawing Tools**: Create bounding boxes
+- **Class Assignment**: Label objects
+- **YOLO Export**: Export annotations for training
+
+## Database Design
+
+### Tables Overview
+
+| Table | Purpose | Key Fields |
+|-------|---------|------------|
+| **models** | Store trained models | name, version, path, metrics |
+| **images** | Image metadata | path, dimensions, checksum |
+| **detections** | Detection results | bbox, class, confidence |
+| **annotations** | Manual labels | bbox, class, annotator |
+
+### Key Relationships
+- Each detection links to one image and one model
+- Each image can have multiple detections from multiple models
+- Annotations are separate from automated detections
+
+## Implementation Phases
+
+### Phase 1: Core Foundation (Weeks 1-2)
+- [x] Architecture design and documentation
+- [ ] Project structure setup
+- [ ] Database schema implementation
+- [ ] YOLO wrapper basic functionality
+- [ ] Database manager CRUD operations
+
+### Phase 2: GUI Development (Weeks 3-4)
+- [ ] Main window and tab structure
+- [ ] Detection tab implementation
+- [ ] Training tab implementation
+- [ ] Basic visualization widgets
+- [ ] Configuration management
+
+### Phase 3: Advanced Features (Weeks 5-6)
+- [ ] Validation tab with metrics
+- [ ] Results tab with filtering
+- [ ] Export functionality
+- [ ] Error handling and logging
+- [ ] Performance optimization
+
+### Phase 4: Polish & Testing (Week 7)
+- [ ] Unit tests for all components
+- [ ] Integration testing
+- [ ] User documentation
+- [ ] Bug fixes and refinements
+- [ ] Deployment preparation
+
+### Phase 5: Future Enhancements (Post-Launch)
+- [ ] Annotation tool
+- [ ] Real-time camera detection
+- [ ] Multi-model ensemble
+- [ ] Cloud integration
+
+## File Structure
+
+```
+object_detection/
+├── main.py                    # Entry point
+├── requirements.txt           # Dependencies
+├── README.md                  # User documentation
+├── ARCHITECTURE.md            # Technical architecture
+├── IMPLEMENTATION_GUIDE.md    # Development guide
+├── PLAN_SUMMARY.md           # This file
+│
+├── config/
+│   └── app_config.yaml       # Application settings
+│
+├── src/
+│   ├── database/             # Database layer
+│   │   ├── db_manager.py     # Main database operations
+│   │   ├── models.py         # Data classes
+│   │   └── schema.sql        # SQL schema
+│   │
+│   ├── model/                # ML layer
+│   │   ├── yolo_wrapper.py   # YOLO interface
+│   │   └── inference.py      # Detection engine
+│   │
+│   ├── gui/                  # Presentation layer
+│   │   ├── main_window.py    # Main app window
+│   │   ├── tabs/             # Feature tabs
+│   │   ├── dialogs/          # Popup dialogs
+│   │   └── widgets/          # Custom widgets
+│   │
+│   └── utils/                # Utilities
+│       ├── config_manager.py # Config handling
+│       ├── logger.py         # Logging setup
+│       └── file_utils.py     # File operations
+│
+├── data/                     # Runtime data
+│   ├── models/               # Trained models
+│   ├── datasets/             # Training data
+│   └── results/              # Detection outputs
+│
+├── tests/                    # Test suite
+│   ├── test_database.py
+│   ├── test_model.py
+│   └── test_gui.py
+│
+├── logs/                     # Application logs
+└── docs/                     # Additional docs
+```
+
+## User Workflows
+
+### Workflow 1: First-Time Setup
+1. Launch application
+2. Configure image repository path in Settings
+3. Download/verify YOLOv8s.pt base model
+4. Ready to detect!
+
+### Workflow 2: Quick Detection
+1. Open Detection tab
+2. Select pre-trained model
+3. Choose image or folder
+4. Adjust confidence threshold
+5. Click "Detect"
+6. View results and save to database
+
+### Workflow 3: Train Custom Model
+1. Prepare dataset in YOLO format
+2. Open Training tab
+3. Select data.yaml file
+4. Configure hyperparameters
+5. Start training
+6. Monitor progress
+7. Model saved automatically with metrics
+
+### Workflow 4: Analyze Results
+1. Open Results tab
+2. Apply filters (date range, class, model)
+3. View statistics dashboard
+4. Export filtered results to CSV/JSON
+
+## Data Formats
+
+### YOLO Annotation Format
+```
+<class_id> <x_center> <y_center> <width> <height>
+```
+- All coordinates normalized (0-1)
+- One line per object
+- Separate .txt file for each image
+
+### Detection Output Format
+```json
+{
+  "image_id": 123,
+  "image_path": "relative/path/to/image.jpg",
+  "model_id": 5,
+  "model_name": "organelle_detector_v1",
+  "detections": [
+    {
+      "class_name": "organelle",
+      "confidence": 0.95,
+      "bbox": [0.1, 0.2, 0.3, 0.4],
+      "detected_at": "2024-12-04T15:30:00Z"
+    }
+  ]
+}
+```
+
+## Performance Requirements
+
+| Metric | Target | Notes |
+|--------|--------|-------|
+| Detection Speed | < 1s per image | On GPU for 640x640 images |
+| Training Start | < 5s | Model loading time |
+| Database Query | < 100ms | For 10k detections |
+| GUI Responsiveness | < 100ms | UI interactions |
+| Memory Usage | < 4GB | During inference |
+| Batch Processing | 100+ images | Without memory issues |
+
+## Risk Mitigation
+
+| Risk | Impact | Mitigation Strategy |
+|------|--------|-------------------|
+| CUDA/GPU issues | High | Fallback to CPU mode |
+| Database corruption | Medium | Regular backups, transactions |
+| Large datasets | Medium | Lazy loading, pagination |
+| Memory overflow | High | Batch size limits, monitoring |
+| Model compatibility | Low | Version checking, validation |
+
+## Success Criteria
+
+- ✅ Successfully detect objects in microscopy images
+- ✅ Train custom models with >80% mAP50
+- ✅ Store and retrieve detection results efficiently
+- ✅ Intuitive GUI requiring minimal training
+- ✅ Process 100+ images in batch mode
+- ✅ Export results in multiple formats
+- ✅ Comprehensive documentation
+
+## Next Steps
+
+1. **Review this plan** with stakeholders
+2. **Gather feedback** on features and priorities
+3. **Confirm requirements** are fully captured
+4. **Switch to Code mode** to begin implementation
+5. **Follow the implementation guide** for development
+6. **Iterate based on testing** and user feedback
+
+## Questions for Stakeholders
+
+Before proceeding to implementation, please consider:
+
+1. Are there any additional object classes beyond organelles and membrane branches?
+2. What is the typical size of your microscopy images?
+3. Do you need multi-user support or is single-user sufficient?
+4. Are there specific export formats you need beyond CSV/JSON/Excel?
+5. What is your expected dataset size (number of images)?
+6. Do you need support for 3D/volumetric microscopy images?
+7. Any specific visualization requirements?
+8. Integration with other tools or workflows?
+
+## Resources Required
+
+### Development Time
+- Phase 1-3: ~6 weeks development
+- Phase 4: ~1 week testing & polish
+- Total: ~7 weeks for initial release
+
+### Hardware Requirements
+- Development machine with CUDA GPU (recommended)
+- 16GB RAM minimum
+- 50GB storage for development
+
+### Software Dependencies
+- All listed in [`requirements.txt`](requirements.txt)
+- Total size: ~2GB when installed
+
+---
+
+**Ready to proceed with implementation? Let's build this! 🚀**