Solar Coronal Structure Segmentation (SCSSNet)

A Python project for segmenting and analyzing solar coronal structures using deep learning.
Includes a Flask API backend and a client for interacting with the service.

Features

• Deep learning segmentation of solar images (Active Regions, Coronal Holes)
• REST API for predictions and analysis
• Image preprocessing, disk detection, and region analysis
• Returns annotated images and statistics

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/your-repo.git
   cd your-repo

2. Install Python dependencies:

   pip install -r requirements.txt

3. (Optional) Install client dependencies (if using React/JS):

   cd client
   npm install

Usage

1. Start the back-end service:

   cd service
    python service/app.py

The API will be available at http://localhost:5000.

2. Start the FITS conversion service:

   cd service
    python service/conver_fits.py

The API will be available at http://localhost:5500.

3. Use the client to interact with the API:
   • If using a web client, run:

     cd flask-client
     npm start

API Endpoints

• POST /predict

Send a JSON payload with image data and parameters to get segmentation results. Example request:

{
  "tasktype": "AR",
  "threshold": "medium",
  "image": "<base64-encoded PNG>",
  "instrument": "AIA",
  "date": "2024-06-01",
  "time": "12:00:00"
  }
}

Model Files

Place pre-trained model files in service/models/ as required by app.py. Two models are needed, one for Active Regions (AR) and one for Coronal Holes (CH).