# vision/alignment_detector.py
from vision.align_model.yolo11_main import run_yolo_classification
def detect_vehicle_alignment(image_array):
    """
    通过图像检测模具车是否对齐
    """
    try:
        # 检查模型是否已加载
        if image_array is None:
            print("输入图像为空")
            return False

        # 直接使用模型进行推理
        # results = alignment_model(image_array)
        # pared_probs = results[0].probs.data.cpu().numpy().flatten()

        # # 类别0: 未对齐, 类别1: 对齐
        # class_id = int(pared_probs.argmax())
        # confidence = float(pared_probs[class_id])

        # # 只有当对齐且置信度>95%时才认为对齐
        # if class_id == 1 and confidence > 0.95:
        #     return True
        # return False

        # 使用yolov11_cls_inference函数进行推理
        results = run_yolo_classification(image_array)
        if results=="盖板对齐":
           return True
        else:
            return False

    except Exception as e:
        print(f"对齐检测失败: {e}")
        return False