feeding

vision/align_model/aligment_inference.py

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+import cv2
+import numpy as np
+import platform
+from .labels import labels  # 确保这个文件存在
+
+
+
+# ------------------- 核心：全局变量存储RKNN模型实例（确保只加载一次） -------------------
+# 初始化为None，首次调用时加载模型，后续直接复用
+_global_rknn_instance = None
+
+# device tree for RK356x/RK3576/RK3588
+DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible'
+
+def get_host():
+    # get platform and device type
+    system = platform.system()
+    machine = platform.machine()
+    os_machine = system + '-' + machine
+    if os_machine == 'Linux-aarch64':
+        try:
+            with open(DEVICE_COMPATIBLE_NODE) as f:
+                device_compatible_str = f.read()
+                if 'rk3562' in device_compatible_str:
+                    host = 'RK3562'
+                elif 'rk3576' in device_compatible_str:
+                    host = 'RK3576'
+                elif 'rk3588' in device_compatible_str:
+                    host = 'RK3588'
+                else:
+                    host = 'RK3566_RK3568'
+        except IOError:
+            print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE))
+            exit(-1)
+    else:
+        host = os_machine
+    return host
+
+def get_top1_class_str(result):
+    """
+    从推理结果中提取出得分最高的类别，并返回字符串
+
+    参数：
+        result (list): 模型推理输出结果（格式需与原函数一致，如 [np.ndarray]）
+    返回：
+        str:得分最高类别的格式化字符串
+        若推理失败，返回错误提示字符串
+    """
+    if result is None:
+        print("Inference failed: result is None")
+        return
+
+    # 解析推理输出（与原逻辑一致：展平输出为1维数组）
+    output = result[0].reshape(-1)
+
+    # 获取得分最高的类别索引（np.argmax 直接返回最大值索引，比排序更高效）
+    top1_index = np.argmax(output)
+
+    # 处理标签（确保索引在 labels 列表范围内，避免越界）
+    if 0 <= top1_index < len(labels):
+        top1_class_name = labels[top1_index]
+    else:
+        top1_class_name = "Unknown Class"  # 应对索引异常的边界情况
+
+    # 5. 格式化返回字符串（包含索引、得分、类别名称，得分保留6位小数）
+    return top1_class_name
+
+def preprocess(raw_image, target_size=(640, 640)):
+    """
+    读取图像并执行预处理（BGR转RGB、调整尺寸、添加Batch维度）
+
+    参数:
+        image_path (str): 图像文件的完整路径（如 "C:/test.jpg" 或 "/home/user/test.jpg"）
+        target_size (tuple): 预处理后图像的目标尺寸，格式为 (width, height)，默认 (640, 640)
+    返回:
+        img (numpy.ndarray): 预处理后的图像
+    异常:
+        FileNotFoundError: 图像路径不存在或无法读取时抛出
+        ValueError: 图像读取成功但为空（如文件损坏）时抛出
+    """
+    # img = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)
+    # 调整尺寸
+    img = cv2.resize(raw_image, target_size)
+    img = np.expand_dims(img, 0)  # 添加batch维度
+
+    return img
+
+# ------------------- 新增：模型初始化函数（控制只加载一次） -------------------
+def init_rknn_model(model_path):
+    """
+    初始化RKNN模型（全局唯一实例）：
+    - 首次调用：加载模型+初始化运行时，返回模型实例
+    - 后续调用：直接返回已加载的全局实例，避免重复加载
+    """
+    from rknnlite.api import RKNNLite
+    
+    global _global_rknn_instance  # 声明使用全局变量
+
+    # 若模型未加载过，执行加载逻辑
+    if _global_rknn_instance is None:
+        # 1. 创建RKNN实例（关闭内置日志）
+        rknn_lite = RKNNLite(verbose=False)
+
+        # 2. 加载RKNN模型
+        ret = rknn_lite.load_rknn(model_path)
+        if ret != 0:
+            print(f'[ERROR] Load CLS_RKNN model failed (code: {ret})')
+            exit(ret)
+
+        # 3. 初始化运行时（绑定NPU核心0）
+        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+        if ret != 0:
+            print(f'[ERROR] Init CLS_RKNN runtime failed (code: {ret})')
+            exit(ret)
+
+        # 4. 将加载好的实例赋值给全局变量
+        _global_rknn_instance = rknn_lite
+        print(f'[INFO] CLS_RKNN model loaded successfully (path: {model_path})')
+
+    return _global_rknn_instance
+
+def yolov11_cls_inference(model_path, raw_image, target_size=(640, 640)):
+    """
+    根据平台进行推理，并返回最终的分类结果
+
+    参数:
+    model_path (str): RKNN模型文件路径
+    image_path (str): 图像文件的完整路径（如 "C:/test.jpg" 或 "/home/user/test.jpg"）
+    target_size (tuple): 预处理后图像的目标尺寸，格式为 (width, height)，默认 (640, 640)
+    """
+    rknn_model = model_path
+
+    img = preprocess(raw_image, target_size)
+
+    rknn = init_rknn_model(rknn_model)
+    if rknn is None:
+        return None, img
+    outputs = rknn.inference([img])
+
+    # Show the classification results
+    class_name = get_top1_class_str(outputs)
+
+    # rknn_lite.release()
+
+    return class_name
+
+if __name__ == '__main__':
+
+    # 调用yolov11_cls_inference函数（target_size使用默认值640x640，也可显式传参如(112,112)）
+    image_path = "/userdata/reenrr/inference_with_lite/cover_ready.jpg"
+    bgr_image = cv2.imread(image_path)
+    if bgr_image is None:
+        print(f"Failed to read image from {image_path}")
+        exit(-1)
+
+    rgb_frame = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
+    print(f"Read image from {image_path}, shape: {rgb_frame.shape}")
+
+    result = yolov11_cls_inference(
+        # model_path="/userdata/PyQt_main_test/app/view/yolo/yolov11_cls.rknn",
+        model_path="/userdata/chuyiwen/Feeding_control_system/vision/align_model/yolov11_cls_640v6.rknn",
+        raw_image=rgb_frame,
+        target_size=(640, 640)
+    )
+    # 打印最终结果
+    print(f"\n最终分类结果：{result}")
+

vision/align_model/labels.py

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+# the labels come from synset.txt, download link: https://s3.amazonaws.com/onnx-model-zoo/synset.txt
+
+labels = \
+{0: 'cover_noready',
+ 1: 'cover_ready'
+}

vision/align_model/yolo11_main.py

@@ -0,0 +1,93 @@
+# 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
+