Ayomide
76 lines
1.7 KiB
Markdown
76 lines
1.7 KiB
Markdown
# Memory Module Detection API Documentation
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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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## Base URL
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`http://localhost:5000`
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## Endpoints
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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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### 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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### 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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## 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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```
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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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```
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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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