完成夹具开合判断的代码框架，等图片来更新rknn

cls_main（框架完成rknn还没训练）/cls_inference.py

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+import cv2
+import numpy as np
+import platform
+from rknnlite.api import RKNNLite
+
+# ------------------- 全局变量 -------------------
+_global_rknn_instance = None
+
+labels = {0: '夹具未夹紧', 1: '夹具夹紧'}
+
+DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible'
+
+def get_host():
+    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
+
+# ------------------- 图像预处理 -------------------
+def preprocess(raw_image, target_size=(640, 640)):
+    img = cv2.resize(raw_image, target_size)
+    img = np.expand_dims(img, 0)  # 添加 batch 维度
+    return img
+
+# ------------------- RKNN 模型初始化 -------------------
+def init_rknn_model(model_path):
+    global _global_rknn_instance
+    if _global_rknn_instance is None:
+        rknn_lite = RKNNLite(verbose=False)
+        ret = rknn_lite.load_rknn(model_path)
+        if ret != 0:
+            print(f'[ERROR] Load model failed (code: {ret})')
+            exit(ret)
+        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+        if ret != 0:
+            print(f'[ERROR] Init runtime failed (code: {ret})')
+            exit(ret)
+        _global_rknn_instance = rknn_lite
+        print(f'[INFO] Model loaded successfully: {model_path}')
+    return _global_rknn_instance
+
+# ------------------- 推理 -------------------
+def yolov11_cls_inference(model_path, raw_image, target_size=(640, 640)):
+    """
+    返回：(class_id, boolean)
+    类别 0 -> False
+    类别 1 -> True
+    """
+    img = preprocess(raw_image, target_size)
+    rknn = init_rknn_model(model_path)
+    outputs = rknn.inference([img])
+
+    # 获取类别 ID
+    output = outputs[0].reshape(-1)
+    class_id = int(np.argmax(output))
+    bool_value = True if class_id == 1 else False
+
+    return class_id, bool_value
+
+# ------------------- 测试 -------------------
+if __name__ == '__main__':
+    image_path = "12.png"
+    bgr_image = cv2.imread(image_path)
+    if bgr_image is None:
+        print(f"Failed to read image from {image_path}")
+        exit(-1)
+
+    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
+    class_id, bool_value = yolov11_cls_inference(
+        model_path="yolov11_cls.rknn",
+        raw_image=rgb_image,
+        target_size=(640, 640)
+    )
+    print(f"类别ID: {class_id}, 布尔值: {bool_value}")

cls_main（框架完成rknn还没训练）/readme.md

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+# yolov11_cls_inference README
+
+## 概述
+该模块用于对米厂输入图像执行二分类推理，用于判断机械臂夹爪是否夹紧。
+
+类别定义：
+
+0 → 夹具未夹紧 (False)
+1 → 夹具夹紧 (True)
+
+rknn模型只加载一次，复用全局实例，提高推理效率。
+
+## 调用示例
+
+您可以直接调用 yolov11_cls_inference 函数，以便集成到其他项目中：
+
+示例 1: 测试仅获取单张图片的类别和布尔值
+
+```bash
+from yolov11_cls_inference import yolov11_cls_inference
+import cv2
+
+# 读取图像
+bgr_image = cv2.imread("11.jpg")
+rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
+
+# 调用推理函数
+class_id, bool_value = yolov11_cls_inference(
+    model_path="yolov11_cls.rknn",
+    raw_image=rgb_image,
+    target_size=(640, 640)
+)
+
+print(f"类别ID: {class_id}, 布尔值: {bool_value}")
+```
+
+示例 2: 直接在其他项目中集成使用
+```bash
+
+from yolov11_cls_inference import yolov11_cls_inference
+
+# raw_image 已经读取或处理好的图像
+class_id, bool_value = yolov11_cls_inference(model_path="yolov11_cls.rknn", raw_image=raw_image)
+if bool_value:
+    print("夹具夹紧")
+else:
+    print("夹具未夹紧")
+```