Aherobo Ovie Victor
0b7af4050e
✅ Added Simple API Documentation: - Created API_DOCS.md with comprehensive endpoint documentation - Added /docs endpoint to serve formatted API documentation - Updated /api endpoint to include documentation reference - Simple HTML formatting for better readability ✅ Documentation Features: - Complete endpoint descriptions with examples - Request/response formats for all endpoints - cURL and Python usage examples - Error response documentation - Model information and specifications ✅ Access Points: - /api - Basic API info (JSON) - /docs - Detailed documentation (HTML) - API_DOCS.md - Raw markdown documentation Simple, clean documentation without complex Swagger UI overhead.
215 lines
4.0 KiB
Markdown
215 lines
4.0 KiB
Markdown
# Memory Module Detection API Documentation
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## Base URL
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```
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http://localhost:5002
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```
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## Endpoints
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### 1. Health Check
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**GET** `/health`
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Check if the API is running and model is loaded.
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**Response:**
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```json
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{
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"status": "healthy",
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"model_loaded": true,
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"timestamp": "2025-07-12T07:41:46.123456"
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}
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```
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### 2. API Information
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**GET** `/api`
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Get basic API information and available endpoints.
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**Response:**
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```json
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{
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"name": "Memory Module Detection API",
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"version": "1.0",
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"description": "AI-powered memory module detection using YOLOv8",
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"model_loaded": true,
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"endpoints": ["/health", "/api", "/detect", "/detect/hardcoded", "/detect/base64"]
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}
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```
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### 3. Upload Image Detection
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**POST** `/detect`
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Upload an image file for memory module detection.
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**Request:**
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- Content-Type: `multipart/form-data`
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- Body: Form data with `file` field containing image
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- Optional: `confidence` parameter (default: 0.8)
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**Response:**
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```json
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{
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"success": true,
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"detections": [
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{
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"x1": 100.5,
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"y1": 150.2,
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"x2": 200.8,
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"y2": 250.6,
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"confidence": 0.95,
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"class": "memory_module"
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}
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],
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"num_detections": 1,
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"annotated_image": "base64_encoded_image_string",
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"confidence_threshold": 0.8
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}
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```
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### 4. Hardcoded Test Image
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**GET** `/detect/hardcoded`
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Process the hardcoded test image for detection.
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**Query Parameters:**
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- `confidence` (optional): Confidence threshold (default: 0.8)
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**Example:**
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```
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GET /detect/hardcoded?confidence=0.9
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```
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**Response:**
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```json
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{
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"success": true,
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"detections": [...],
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"num_detections": 2,
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"annotated_image": "base64_encoded_image_string",
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"confidence_threshold": 0.9,
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"test_image_path": "training/memory/out1.png"
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}
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```
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### 5. Base64 Image Detection
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**POST** `/detect/base64`
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Process a base64 encoded image for detection.
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**Request:**
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```json
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{
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"image_data": "base64_encoded_image_string",
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"confidence": 0.8
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}
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```
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**Response:**
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```json
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{
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"success": true,
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"detections": [...],
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"num_detections": 1,
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"annotated_image": "base64_encoded_image_string",
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"confidence_threshold": 0.8
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}
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```
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## Error Responses
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All endpoints return error responses in this format:
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```json
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{
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"error": "Error message description",
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"success": false
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}
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```
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Common HTTP status codes:
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- `200` - Success
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- `400` - Bad Request (invalid file, missing parameters)
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- `404` - Not Found (endpoint or file not found)
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- `413` - File Too Large (max 16MB)
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- `500` - Internal Server Error (model not loaded, processing error)
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## Supported Image Formats
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- PNG
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- JPG/JPEG
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- GIF
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- BMP
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## File Size Limits
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- Maximum upload size: 16MB
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## Detection Response Format
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Each detection object contains:
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- `x1, y1`: Top-left corner coordinates
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- `x2, y2`: Bottom-right corner coordinates
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- `confidence`: Detection confidence score (0.0-1.0)
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- `class`: Detected object class ("memory_module")
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## Usage Examples
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### cURL Examples
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**Health Check:**
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```bash
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curl http://localhost:5002/health
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```
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**Upload Image:**
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```bash
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curl -X POST -F "file=@image.jpg" -F "confidence=0.8" http://localhost:5002/detect
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```
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**Hardcoded Test:**
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```bash
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curl "http://localhost:5002/detect/hardcoded?confidence=0.9"
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```
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### Python Examples
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**Health Check:**
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```python
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import requests
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response = requests.get('http://localhost:5002/health')
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print(response.json())
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```
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**Upload Image:**
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```python
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import requests
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with open('image.jpg', 'rb') as f:
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files = {'file': f}
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data = {'confidence': 0.8}
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response = requests.post('http://localhost:5002/detect', files=files, data=data)
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print(response.json())
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```
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**Base64 Detection:**
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```python
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import requests
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import base64
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with open('image.jpg', 'rb') as f:
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image_data = base64.b64encode(f.read()).decode()
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payload = {
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'image_data': image_data,
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'confidence': 0.8
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}
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response = requests.post('http://localhost:5002/detect/base64', json=payload)
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print(response.json())
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```
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## Model Information
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- **Architecture:** YOLOv8 Nano
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- **Classes:** memory_module
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- **Input Size:** 640x640
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- **Accuracy:** 99.5% mAP50
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- **Inference Time:** ~37ms on CPU
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