object-segmentation/PLAN_SUMMARY.md

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)

• 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

{
  "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
• Total size: ~2GB when installed

---

Ready to proceed with implementation? Let's build this! 🚀