feeding

vision/alignment_detector.py

@@ -1,30 +1,35 @@
 # vision/alignment_detector.py
-def detect_vehicle_alignment(image_array, alignment_model):
+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 alignment_model is None:
-            print("对齐检测模型未加载")
-            return False
-
         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()
+        # 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])
+        # # 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
 
-        # 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
     except Exception as e:
         print(f"对齐检测失败: {e}")
         return False