aus10powell / MIT-Fishery-Counter

Applying Image Recognition to Enhance Fisheries Management Capabilities

4 stars 1 forks source link

REST API Creation #2

Open aus10powell opened 2 months ago

aus10powell commented 2 months ago

Fishery Counter API

System Design Outline

1. Data Collection

Camera setup at fishery locations
Video/image capture of fish passages

2. Data Processing

Object detection and tracking pipeline
- Phase 1: Detection (every N frames)
- Phase 2: Tracking (between detections)
Image labeling and annotation

3. Machine Learning Model

YOLOv8 model for object detection
Custom trained on herring images

4. API Backend

REST API endpoints
Fish counting logic
Data storage and retrieval

5. Frontend Interface

User dashboard for non-programmers
Visualization of fish counts and statistics

6. Deployment and Infrastructure

Cloud hosting (e.g. AWS, GCP)
Containerization with Docker

Prediction REST API Details

1. Endpoints

POST /predict
- Accepts image/video upload
- Returns fish count and species classification
GET /stats
- Returns summary statistics (e.g. daily/weekly counts)
GET /health
- API health check

2. Request/Response Format

JSON for metadata
Binary for image/video data

3. Authentication

API key for access control

4. Processing Pipeline

Image preprocessing
YOLOv8 model inference
Post-processing of detections
Counting logic (e.g. directional filtering)

5. Output

Fish count
Species classification (River Herring vs Not River Herring)
Confidence scores
Timestamp

6. Additional Features

Batch processing for historical data
Real-time processing for live video feeds
Integration with environmental sensors (temperature, current speed, etc.)

7. Scalability Considerations

Load balancing for multiple concurrent requests
Caching of frequent queries
Asynchronous processing for long-running tasks

8. Monitoring and Logging

Performance metrics
Error tracking
Usage statistics

Getting Started

[Instructions for setting up and running the API locally or in a development environment]

Deployment

[Instructions for deploying the API to production]

Contributing

[Guidelines for contributing to the project]

License

[License information]