README.md

---
language:
- id
- en
license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- object-detection
- yolo
- yolov8
- ultralytics
- pytorch
- license-plate-detection
- computer-vision
- anpr
- alpr
- smart-city
metrics:
- mAP50: 0.981
- precision: 0.978
---

# 🚗 OpenPathAI - Vehicle License Plate Detector (YOLOv8n)

**OpenPathAI/YOLO-detection-vehcile-plate-2287 License Plate Detection** adalah model Computer Vision ringan berbasis **YOLOv8 Nano** yang di-fine-tune khusus untuk mendeteksi posisi plat nomor kendaraan secara presisi dan cepat (*real-time*). 

Model ini dirancang untuk diintegrasikan ke dalam sistem **ANPR (Automatic Number Plate Recognition)**, pemantauan lalu lintas CCTV, sistem parkir otomatis, maupun perangkat *edge* berspesifikasi rendah seperti Raspberry Pi atau server VPS berbasis CPU.

---

## 📊 Performa Model (Evaluation Metrics)

1. Grafik utama yang menampilkan penurunan Loss dan peningkatan nilai mAP50 dan Precision
![Grafik Performa Pelatihan](results.png)

2. Menunjukkan seberapa akurat model membedakan antara area plat nomor (license-plate) dan latar belakang (background). 
![Akurasi Model](confusion_matrix.png)

3. Grafik kurva keseimbangan antara Precision dan Recall.
![Grafik Kurva](BoxPR_curve.png)
![Grafik Kurva](BoxF1_curve.png)

4. Contoh foto pengujian 
![Visualisasi Prediksi Validasi](val_batch0_pred.jpg)


---

## 🚀 Cara Penggunaan (Usage)

### 1. Instalasi Dependency
Pastikan Anda sudah menginstal pustaka `ultralytics` dan `huggingface_hub`:

```bash
pip install ultralytics huggingface_hub opencv-python
```
---

### 2. Contoh script
Tempel script ini ke terminal anda, dan jalankan, secara otomatis akan mendownload dan menjalankan backend.

```bash
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
from PIL import Image
import io
import base64

app = FastAPI(title="OpenPath - License Plate Detection API")

# Allow CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load Model OpenPath
print("Loading OpenPath Model from Hugging Face...")
MODEL_PATH = hf_hub_download(
    repo_id="OpenPathAI/YOLO-detection-vehcile-plate-2287",
    filename="weights/best.pt"
)
model = YOLO(MODEL_PATH)
print("✅ Model OpenPath Ready!")

@app.get("/")
def root():
    return {"status": "online", "message": "OpenPath License Plate Detection Server Ready"}

@app.post("/detect")
async def detect_license_plate(file: UploadFile = File(...)):
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="File harus berupa gambar")

    contents = await file.read()
    image = Image.open(io.BytesIO(contents)).convert("RGB")

    results = model(image, conf=0.4)
    detections = []
    
    for result in results:
        for box in result.boxes:
            x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
            confidence = float(box.conf[0])

            cropped_plate = image.crop((x1, y1, x2, y2))
            
            buffered = io.BytesIO()
            cropped_plate.save(buffered, format="JPEG")
            crop_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")

            detections.append({
                "confidence": round(confidence, 2),
                "box": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
                "crop_base64": f"data:image/jpeg;base64,{crop_base64}"
            })

    return {
        "success": True,
        "total_plates_found": len(detections),
        "detections": detections
    }
```