services/crack_detection_service.py

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import cv2
import random

# Load model
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

def detect_cracks(frame):
    image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]

    cracks = []
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        if score >= 0.9:
            severity = random.choice(['Minor', 'Moderate', 'Severe'])  # Simulate severity
            cracks.append({
                'box': box.tolist(),
                'severity': severity,
                'confidence': score.item()
            })

    return cracks