update project structure and improve scripts
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# Memory Module Detection API Documentation
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# Memory Module Detection System
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## Overview
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Flask API for detecting memory modules on motherboard images using YOLOv8. Processes uploaded images and returns bounding box coordinates with confidence scores.
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A computer vision system for detecting memory modules in images and videos using YOLO object detection.
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## Base URL
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`http://localhost:5000`
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## Features
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## Endpoints
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- **Image Detection**: Upload images to detect memory modules
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- **Video Processing**: Process video files frame-by-frame
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- **Bounding Box Visualization**: See detected objects with confidence scores
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- **Detailed Analytics**: Get detection statistics and metrics
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- **REST API**: Fully documented API endpoints
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- **Web Interface**: User-friendly browser interface
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### 1. Root Endpoint
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**GET** `/`
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- Returns the test interface HTML page
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- Response: `test.html`
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## Requirements
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### 2. Image Detection
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**POST** `/detect`
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- Accepts image uploads for processing
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- **Request:**
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```bash
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curl -X POST -F "image=@motherboard.jpg" http://localhost:5000/detect
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```
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- **Successful Response (200):**
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```json
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{
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"detections": [
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{
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"box": [x1,y1,x2,y2],
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"confidence": 0.95,
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"class": 0
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}
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],
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"result_image": "/results/filename.jpg"
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}
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```
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- **Error Responses:**
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- `400 Bad Request`: Missing/invalid image file
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- `500 Server Error`: Processing failure
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- Python 3.8+
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- CUDA-enabled GPU (recommended for best performance)
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- Docker (optional)
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### 3. Result Retrieval
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**GET** `/results/<filename>`
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- Returns annotated image with bounding boxes
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- Example: `http://localhost:5000/results/out1.jpg`
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## Installation
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## Request/Response Examples
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**Sample Request:**
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```python
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import requests
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response = requests.post(
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'http://localhost:5000/detect',
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files={'image': open('motherboard.jpg', 'rb')}
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)
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### 1. Clone the repository
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```bash
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git clone https://github.com/yourusername/memory-detection.git
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cd memory-detection
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```
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**Sample Response:**
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```json
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{
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"detections": [
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{
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"box": [541,567,661,265],
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"confidence": 0.98,
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"class": 0
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}
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],
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"result_image": "/results/out1.jpg"
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}
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### 2. Set up Python environment
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```bash
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python -m venv venv
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source venv/bin/activate # Linux/MacOS
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venv\Scripts\activate # Windows
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```
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## Technical Specifications
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| Parameter | Value |
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|--------------------|---------------------------|
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| Model | YOLOv8n (custom-trained) |
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| Input Formats | JPG/PNG |
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| Recommended Resolution | 416px |
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| Processing Time (CPU) | 200-500ms per image |
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### 3. Install dependencies
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```bash
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pip install -r requirements.txt
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```
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### 4. Download YOLO model
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Place your `.pt` model file in the `models/` directory and update `config.py` with the correct path.
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## Configuration
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Edit `config.py` to customize:
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- Model paths
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- Confidence thresholds
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- File size limits
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- Server host/port settings
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## Running the Application
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### Development Mode
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```bash
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python backend/app.py
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```
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### Production Mode (with Gunicorn)
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```bash
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gunicorn --bind 0.0.0.0:5000 backend.app:app
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```
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### Docker
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```bash
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docker build -t memory-detection .
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docker run -p 5000:5000 memory-detection
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```
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## API Documentation
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### Endpoints
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/` | GET | Web interface |
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| `/api/health` | GET | Service health check |
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| `/api/v1/detect` | POST | Detect memory modules in image |
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| `/api/v1/detect/video` | POST | Process video for detections |
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| `/api/v1/results/<filename>` | GET | Retrieve result images |
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### Example Requests
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**Image Detection**:
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```bash
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curl -X POST -F "image=@test.jpg" http://localhost:5000/api/v1/detect
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```
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**Video Processing**:
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```bash
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curl -X POST -F "video=@test.mp4" -F "fps=2" -F "max_frames=100" http://localhost:5000/api/v1/detect/video
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```
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## Web Interface
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Access the web interface at `http://localhost:5000`:
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1. **Upload Images**:
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- Supported formats: JPG, PNG
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- Max file size: 10MB (configurable)
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2. **Process Videos**:
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- Supported formats: MP4, AVI, MOV
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- Adjustable FPS and frame limits
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- Detailed frame-by-frame results
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## Project Structure
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```
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memory-detection/
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├── backend/ # Core application
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│ ├── app.py # Flask application
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│ ├── config.py # Configuration
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│ ├── detector.py # YOLO detection logic
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│ ├── exceptions.py # Custom exceptions
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│ ├── utils.py # Helper functions
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│ ├── video_processor.py # Video handling
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│ └── templates/ # Frontend templates
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│ └── index.html # Web interface
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├── models/ # YOLO model files
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├── uploads/ # Temporary upload storage
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├── results/ # Detection result images
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├── tests/ # Unit tests
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├── requirements.txt # Python dependencies
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└── README.md
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```
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## Troubleshooting
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**Common Issues**:
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1. **Model not loading**:
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- Verify model path in `config.py`
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- Check file permissions
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2. **CUDA errors**:
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- Ensure compatible CUDA version is installed
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- Try CPU-only mode by modifying detector.py
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3. **File upload issues**:
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- Check `MAX_FILE_SIZE` in config
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- Verify file extensions are allowed
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