services/thermal_service.py

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

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

def detect_thermal_anomalies(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]

    boxes = []
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        if score >= 0.9:
            boxes.append(box.tolist())

    return boxes