Detect Casting Defects with YOLO26 and MetaLog

                      
                     
import os
import cv2
import numpy as np
from typing import List, Dict
from fastapi import FastAPI, HTTPException

# Configuration
MODEL_PATH = os.getenv('MODEL_PATH')  # Path to YOLO26 model weights
LABELS_PATH = os.getenv('LABELS_PATH')  # Path to labels file
CONFIDENCE_THRESHOLD = 0.5

# Load YOLO26 model
net = cv2.dnn.readNetFromDarknet(MODEL_PATH, LABELS_PATH)

# Initialize FastAPI
app = FastAPI()

# Function to detect defects
def detect_defects(image: np.ndarray) -> List[Dict[str, float]]:
    blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    outputs = net.forward(output_layers)

    detections = []
    for output in outputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > CONFIDENCE_THRESHOLD:
                detections.append({
                    'class_id': class_id,
                    'confidence': float(confidence)
                })
    return detections

# API endpoint for defect detection
@app.post('/detect/')
async def detect(image_path: str):
    try:
        image = cv2.imread(image_path)
        if image is None:
            raise HTTPException(status_code=400, detail='Image not found')
        results = detect_defects(image)
        return {'success': True, 'detections': results}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)