ab0e9b973a
- Set up FastAPI project structure with SQLite database - Create database models for tomato images and severity classifications - Implement image upload and processing endpoints - Develop a segmentation model for tomato disease severity detection - Add API endpoints for analysis and results retrieval - Implement health check endpoint - Set up Alembic for database migrations - Update project documentation
142 lines
3.6 KiB
Markdown
142 lines
3.6 KiB
Markdown
# Tomato Severity Segmentation Model API
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A FastAPI-based application for detecting and analyzing disease severity in tomato plants using image segmentation techniques.
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## Overview
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This API allows users to upload tomato plant images and analyze them for various diseases and their severity. The system uses a segmentation model to identify affected areas and classify the severity of diseases such as early blight, late blight, bacterial spot, and septoria leaf spot.
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## Features
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- **Image Upload**: Upload tomato plant images for analysis
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- **Disease Detection**: Identify multiple disease types in a single image
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- **Severity Classification**: Classify the severity of detected diseases
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- **Segmentation Maps**: Generate segmentation maps highlighting affected areas
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- **Health Monitoring**: Built-in health endpoint for monitoring application status
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## Installation
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### Prerequisites
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- Python 3.8+
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- pip (Python package manager)
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### Setup
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1. Clone the repository:
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```bash
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git clone <repository-url>
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cd tomatoseveritysegmentationmodel
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Run database migrations:
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```bash
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alembic upgrade head
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```
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4. Start the application:
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```bash
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uvicorn main:app --reload
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```
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The API will be available at `http://localhost:8000`
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## API Endpoints
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### Health Check
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- `GET /health`: Check the health of the application
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### Image Management
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- `POST /api/tomatoes/upload`: Upload a tomato image
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- `GET /api/tomatoes`: List all uploaded tomato images
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- `GET /api/tomatoes/{image_id}`: Get details of a specific image
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- `DELETE /api/tomatoes/{image_id}`: Delete an image and its analysis data
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### Analysis
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- `POST /api/model/analyze/{image_id}`: Analyze a tomato image for disease severity
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- `GET /api/model/results/{image_id}`: Get all analysis results for a specific image
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- `GET /api/model/info`: Get information about the segmentation model
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## Database Schema
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The application uses SQLite with the following main tables:
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- `tomato_images`: Stores uploaded image metadata
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- `analysis_results`: Stores analysis results for each image
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- `severity_details`: Stores detailed severity data for each analysis
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## Segmentation Model
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The current implementation uses conventional computer vision techniques for segmentation as a proof of concept. In a production environment, this would be replaced with a trained deep learning model like U-Net or DeepLabV3.
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The model classifies the following disease categories:
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- Healthy
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- Early Blight
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- Late Blight
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- Bacterial Spot
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- Septoria Leaf Spot
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## Example Usage
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### Upload an image
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```bash
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curl -X POST "http://localhost:8000/api/tomatoes/upload" \
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-H "accept: application/json" \
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-H "Content-Type: multipart/form-data" \
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-F "file=@tomato_plant.jpg"
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```
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### Analyze the image
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```bash
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curl -X POST "http://localhost:8000/api/model/analyze/{image_id}" \
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-H "accept: application/json"
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```
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## Development
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### Project Structure
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```
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tomatoseveritysegmentationmodel/
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├── app/
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│ ├── api/
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│ │ └── routes/
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│ ├── core/
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│ ├── db/
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│ ├── models/
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│ ├── schemas/
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│ ├── services/
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│ └── utils/
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├── migrations/
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├── storage/
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│ ├── db/
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│ ├── images/
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│ └── models/
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├── alembic.ini
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├── main.py
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└── requirements.txt
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```
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### Adding New Features
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To add new features:
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1. Define models in `app/models/`
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2. Create Pydantic schemas in `app/schemas/`
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3. Add database migrations with Alembic
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4. Implement routes in `app/api/routes/`
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5. Update tests
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## License
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This project is licensed under the MIT License - see the LICENSE file for details. |