feeding

vision/align_model/yolo11_main.py

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+# yolo11_main.py
+import cv2
+import numpy as np
+from collections import deque
+import os
+
+# 导入模块（不是函数）
+from .aligment_inference import yolov11_cls_inference
+
+# 模型路径
+CLS_MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "yolov11_cls_640v6.rknn")
+
+class ClassificationStabilizer:
+    """分类结果稳定性校验器，处理瞬时噪声帧"""
+
+    def __init__(self, window_size=5, switch_threshold=2):
+        self.window_size = window_size  # 滑动窗口大小（缓存最近N帧结果）
+        self.switch_threshold = switch_threshold  # 状态切换需要连续N帧一致
+        self.result_buffer = deque(maxlen=window_size)  # 缓存最近结果
+        self.current_state = "盖板未对齐"  # 初始状态
+        self.consecutive_count = 0  # 当前状态连续出现的次数
+
+    def stabilize(self, current_frame_result):
+        """
+        输入当前帧的分类结果，返回经过稳定性校验的结果
+        Args:
+            current_frame_result: 当前帧的原始分类结果（str）
+        Returns:
+            str: 经过校验的稳定结果
+        """
+        # 1. 将当前帧结果加入滑动窗口
+        self.result_buffer.append(current_frame_result)
+
+        # 2. 统计窗口内各结果的出现次数（多数投票基础）
+        result_counts = {}
+        for res in self.result_buffer:
+            result_counts[res] = result_counts.get(res, 0) + 1    # 使用 result_counts 字典记录每个元素出现的总次数。
+
+        # 3. 找到窗口中出现次数最多的结果（候选结果）
+        candidate = max(result_counts, key=result_counts.get)
+
+        # 4. 状态切换校验：只有候选结果连续出现N次才允许切换
+        if candidate == self.current_state:
+            # 与当前状态一致，重置连续计数
+            self.consecutive_count = 0
+        else:
+            # 与当前状态不一致，累计连续次数
+            self.consecutive_count += 1
+            # 连续达到阈值，才更新状态
+            if self.consecutive_count >= self.switch_threshold:
+                self.current_state = candidate
+                self.consecutive_count = 0
+
+        return self.current_state
+
+# 初始化稳定性校验器（全局唯一实例，确保状态连续）
+cls_stabilizer = ClassificationStabilizer(
+    window_size=5,  # 缓存最近5帧
+    switch_threshold=2  # 连续2帧一致才切换状态
+)
+
+# ====================== 分类接口（可选，保持原逻辑） ======================
+def run_yolo_classification(rgb_frame):
+    """
+    YOLO 图像分类接口函数
+    Args:
+        rgb_frame: numpy array (H, W, 3), RGB 格式
+    Returns:
+        str: 分类结果（"盖板对齐" / "盖板未对齐" / "异常"）
+    """
+    if not isinstance(rgb_frame, np.ndarray):
+        print(f"[ERROR] 输入类型错误：需为 np.ndarray，当前为 {type(rgb_frame)}")
+        return "异常"
+
+    try:
+        cover_cls = yolov11_cls_inference(CLS_MODEL_PATH, rgb_frame, target_size=(640, 640))
+    except Exception as e:
+        print(f"[WARN] 分类推理失败: {e}")
+        cover_cls = "异常"
+        
+    raw_result = "盖板未对齐"  # 默认值
+    # 结果映射
+    if cover_cls == "cover_ready":
+        raw_result = "盖板对齐"
+    elif cover_cls == "cover_noready":
+        raw_result = "盖板未对齐"
+    else:
+        raw_result = "异常"
+    # 通过稳定性校验器处理，返回最终结果
+    stable_result = cls_stabilizer.stabilize(raw_result)
+    print("raw_result, stable_result:",raw_result, stable_result)
+    return stable_result
+