Making it installabel package and switching to segmentation mode

README.md

@@ -1,6 +1,6 @@
 # Microscopy Object Detection Application
 
-A desktop application for detecting organelles and membrane branching structures in microscopy images using YOLOv8, featuring comprehensive training, validation, and visualization capabilities.
+A desktop application for detecting and segmenting organelles and membrane branching structures in microscopy images using YOLOv8-seg, featuring comprehensive training, validation, and visualization capabilities with pixel-accurate segmentation masks.
 
 ![Python](https://img.shields.io/badge/python-3.8+-blue.svg)
 ![PySide6](https://img.shields.io/badge/PySide6-6.5+-green.svg)
@@ -8,8 +8,8 @@ A desktop application for detecting organelles and membrane branching structures
 
 ## Features
 
-- **🎯 Object Detection**: Real-time and batch detection of microscopy objects
-- **🎓 Model Training**: Fine-tune YOLOv8s on custom microscopy datasets
+- **🎯 Object Detection & Segmentation**: Real-time and batch detection with pixel-accurate segmentation masks
+- **🎓 Model Training**: Fine-tune YOLOv8s-seg on custom microscopy datasets
 - **📊 Validation & Metrics**: Comprehensive model validation with visualization
 - **💾 Database Storage**: SQLite database for detection results and metadata
 - **📈 Visualization**: Interactive plots and charts using pyqtgraph
@@ -34,14 +34,24 @@ A desktop application for detecting organelles and membrane branching structures
 
 ## Installation
 
-### 1. Clone the Repository
+### Option 1: Install from PyPI (Recommended)
+
+```bash
+pip install microscopy-object-detection
+```
+
+This will install the package and all its dependencies.
+
+### Option 2: Install from Source
+
+#### 1. Clone the Repository
 
 ```bash
 git clone <repository-url>
 cd object_detection
 ```
 
-### 2. Create Virtual Environment
+#### 2. Create Virtual Environment
 
 ```bash
 # Linux/Mac
@@ -53,25 +63,44 @@ python -m venv venv
 venv\Scripts\activate
 ```
 
-### 3. Install Dependencies
+#### 3. Install in Development Mode
 
 ```bash
-pip install -r requirements.txt
+# Install in editable mode with dev dependencies
+pip install -e ".[dev]"
+
+# Or install just the package
+pip install .
 ```
 
 ### 4. Download Base Model
 
-The application will automatically download the YOLOv8s.pt model on first use, or you can download it manually:
+The application will automatically download the YOLOv8s-seg.pt segmentation model on first use, or you can download it manually:
 
 ```bash
 # The model will be downloaded automatically by ultralytics
 # Or download manually from: https://github.com/ultralytics/assets/releases
 ```
 
+**Note:** YOLOv8s-seg is a segmentation model that provides pixel-accurate masks for detected objects, enabling more precise analysis than standard bounding box detection.
+
 ## Quick Start
 
 ### 1. Launch the Application
 
+After installation, you can launch the application in two ways:
+
+**Using the GUI launcher:**
+```bash
+microscopy-detect-gui
+```
+
+**Or using Python directly:**
+```bash
+python -m microscopy_object_detection
+```
+
+**If installed from source:**
 ```bash
 python main.py
 ```
@@ -85,11 +114,12 @@ python main.py
 ### 3. Perform Detection
 
 1. Navigate to the **Detection** tab
-2. Select a model (default: yolov8s.pt)
+2. Select a model (default: yolov8s-seg.pt)
 3. Choose an image or folder
 4. Set confidence threshold
 5. Click **Detect**
-6. View results and save to database
+6. View results with segmentation masks overlaid
+7. Save results to database
 
 ### 4. Train Custom Model
 
@@ -212,8 +242,8 @@ The application uses SQLite with the following main tables:
 
 - **models**: Stores trained model information and metrics
 - **images**: Stores image metadata and paths
-- **detections**: Stores detection results with bounding boxes
-- **annotations**: Stores manual annotations (future feature)
+- **detections**: Stores detection results with bounding boxes and segmentation masks (polygon coordinates)
+- **annotations**: Stores manual annotations with optional segmentation masks (future feature)
 
 See [`ARCHITECTURE.md`](ARCHITECTURE.md) for detailed schema information.
 
@@ -230,7 +260,7 @@ image_repository:
   allowed_extensions: [".jpg", ".jpeg", ".png", ".tif", ".tiff"]
 
 models:
-  default_base_model: "yolov8s.pt"
+  default_base_model: "yolov8s-seg.pt"
   models_directory: "data/models"
 
 training:
@@ -258,7 +288,7 @@ visualization:
 from src.model.yolo_wrapper import YOLOWrapper
 
 # Initialize wrapper
-yolo = YOLOWrapper("yolov8s.pt")
+yolo = YOLOWrapper("yolov8s-seg.pt")
 
 # Train model
 results = yolo.train(
@@ -393,10 +423,10 @@ make html
 
 **Issue**: Model not found error
 
-**Solution**: Ensure YOLOv8s.pt is downloaded. Run:
+**Solution**: Ensure YOLOv8s-seg.pt is downloaded. Run:
 ```python
 from ultralytics import YOLO
-model = YOLO('yolov8s.pt')  # Will auto-download
+model = YOLO('yolov8s-seg.pt')  # Will auto-download
 ```