Models Package

A comprehensive Python package for water and river segmentation using deep learning models with satellite imagery data.

🌊 Features

• Abstract Base Model: Extensible architecture for different segmentation models
• Water Segmentation: Advanced model using Sentinel-1, Sentinel-2, and terrain data
• River Segmentation: Specialized model for river detection and analysis
• Data Integration: Built-in support for MinIO, TimescaleDB, and Kafka streaming
• Data Validation: Pydantic schemas for robust data handling
• Production Ready: Optimized for real-world deployment scenarios

📦 Installation

pip install models-package

Development Installation

pip install models-package[dev]

🚀 Quick Start

Water Segmentation

from models_package import WaterSegmentationModel

# Initialize the water segmentation model
model = WaterSegmentationModel(
    model_path="path/to/model.pth",
    model_name="WaterSegmentation",
    model_indx=0  # 0 for 7-band model, 1 for 9-band model
)

# Load the model weights
model.load_model()

# Setup connections (optional)
model.minIOConnection(
    address="localhost",
    port=9000,
    target="bucket-name",
    access_key="access_key",
    secret_key="secret_key"
)

# Make predictions
result = model.predict({
    "folder_link": "path/to/satellite/data",
    "location": "area_name"
})

print(f"Water coverage: {result['water_coverage_stats']['water_percentage']:.2f}%")

River Segmentation

from models_package import RiverSegmentationModel

# Initialize the river segmentation model
model = RiverSegmentationModel(
    model_path="path/to/river_model.pth",
    model_name="RiverSegmentation",
    input_size=(512, 512),
    model_architecture="unetplusplus",
    encoder_name="efficientnet-b3"
)

# Load model and make predictions
model.load_model()
result = model.predict({
    "image_link": "path/to/image",
    "filename": "river_image.jpg"
})

📋 Requirements

• Python >= 3.8
• PyTorch >= 1.9.0
• CUDA compatible GPU (recommended)
• See pyproject.toml for complete dependency list

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Create a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

• Author: Jason
• Email: ikakandris@gmail.com
• Repository: https://github.com/deepblue597/models_package

Requirements

• Python >= 3.8
• PyTorch >= 1.9.0
• See requirements.txt for full dependencies

License

MIT License