README.md

metadata

title: Anomaly Detection API
emoji: 🔍
colorFrom: blue
colorTo: red
sdk: docker
pinned: false
license: mit

🔍 Anomaly Detection API

Real-time anomaly detection for electrical components using PatchCore + OpenCV classification.

🚀 Quick Start

API Endpoint

POST /infer

Request:

{
  "image_url": "https://example.com/your-image.jpg"
}

Response:

{
  "label": "Normal",
  "boxed_url": "https://cloudinary.com/boxed_image.jpg",
  "mask_url": "https://cloudinary.com/anomaly_mask.png",
  "filtered_url": "https://cloudinary.com/filtered_anomalies.png",
  "boxes": []
}

Example Usage

curl -X POST "https://YOUR_USERNAME-anomaly-detection-api.hf.space/infer" \
  -H "Content-Type: application/json" \
  -d '{"image_url": "https://example.com/test.jpg"}'

import requests

response = requests.post(
    "https://YOUR_USERNAME-anomaly-detection-api.hf.space/infer",
    json={"image_url": "https://example.com/test.jpg"}
)

result = response.json()
print(f"Classification: {result['label']}")
print(f"Boxed Image: {result['boxed_url']}")

📋 Classification Labels

• Normal - No anomalies detected
• Full Wire Overload - Entire wire showing overload
• Point Overload (Faulty) - Localized overload points

🔧 Technical Details

• Model: PatchCore (anomaly detection)
• Classification: OpenCV-based heuristics
• Response Time: ~5 seconds
• Max Image Size: Unlimited (auto-resized)

🌐 Endpoints

Endpoint	Method	Description
/	GET	API documentation
/health	GET	Health check
/infer	POST	Run inference

📦 Output Files

All processed images are uploaded to Cloudinary:

• boxed_url: Original image with bounding boxes
• mask_url: Grayscale anomaly heatmap
• filtered_url: Filtered image showing only anomalous regions

🛠️ Built With

• PyTorch 2.4.1
• Anomalib (PatchCore)
• OpenCV
• Flask
• Cloudinary

📄 License

MIT License