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https://github.com/nonezonyx/field_segmentation
Determine which agricultural fields are currently cultivated with plants and which are resting until next growing season using deep learning segmentation models.
https://github.com/nonezonyx/field_segmentation
fastapi pyside6 python semantic-segmentation unet
Last synced: 7 days ago
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Determine which agricultural fields are currently cultivated with plants and which are resting until next growing season using deep learning segmentation models.
- Host: GitHub
- URL: https://github.com/nonezonyx/field_segmentation
- Owner: nonezonyx
- License: mit
- Created: 2025-02-02T16:48:51.000Z (9 days ago)
- Default Branch: main
- Last Pushed: 2025-02-02T17:23:40.000Z (9 days ago)
- Last Synced: 2025-02-02T18:26:00.749Z (9 days ago)
- Topics: fastapi, pyside6, python, semantic-segmentation, unet
- Language: Jupyter Notebook
- Homepage:
- Size: 25.6 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Field Segmentation Project
Determine which agricultural fields are currently cultivated with plants and which are resting until next growing season using deep learning segmentation models.
## Table of Contents
- [Project Overview](#project-overview)
- [Installation](#installation)
- [API Documentation](#api)
- [Notebook Timeline](#notebook-timeline)
- [UI Interface](#ui)
- [Metrics](#metrics)
## Project Overview
This project combines computer vision and deep learning to analyze satellite/aerial imagery of agricultural fields. The system identifies:
- 🌱 **Growing Land**: Fields currently under cultivation
- 🛌 **Resting Land**: Fields lying fallow for seasonal recovery
**Key Features:**
- U-Net and DeepLabV3 segmentation models
- FastAPI backend for processing requests
- Dockerized deployment
- Data augmentation pipeline
- UI with PySide6
## Installation
### Prerequisites
- Python 3.8+
- Docker 20.10+
- NVIDIA GPU (recommended for training)
### Local Setup
```bash
git clone https://github.com/yourusername/field-segmentation.git
cd field-segmentation
# Install dependencies
pip install -r requirements.txt
# Start API server
uvicorn main:app --reload
# or run ui
python3 ui.py
```
### Docker Deployment
```bash
docker-compose up --build
```
### API
Endpoint: POST /process-land
Input parameters:
+ image: JPEG/PNG file upload
+ width: float > 0
+ length: float > 0
Response format:
```json
{
"processed_image": "base64_string",
"growing_land": float,
"resting_land": float
}
```
Access interactive docs at http://localhost:8000/docs after deployment.
### Notebook Timeline
1. mask_creation.ipynb - Creating masks from annotations
2. data_augmentation.ipynb - Image augmentation strategies
3. unet_example.ipynb - U-Net model implementation
4. DeepLabv3.ipynb - DeepLabV3+ configuration
5. metrics.ipynb - UNet vs DeepLabv3 results comparison
### UI
Simple UI was created using PySide6
### Metrics
Overall DeepLabv3 showed better results (see ```metrics.ipynb```)
