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class_xiantiao_rknn/main_quexian_cls.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: '无缺陷', 1: '有缺陷'}
+
+DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible'
+
+
+# ------------------- 主机信息 -------------------
+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
+
+
+# ------------------- RKNN 模型初始化（只加载一次） -------------------
+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:
+            raise RuntimeError(f'Load model failed: {ret}')
+        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+        if ret != 0:
+            raise RuntimeError(f'Init runtime failed: {ret}')
+        _global_rknn_instance = rknn_lite
+        print(f'[INFO] RKNN model loaded: {model_path}')
+    return _global_rknn_instance
+
+
+# ------------------- 图像预处理（无 ROI，直接 resize 整图） -------------------
+def preprocess(raw_image, target_size=(640, 640)):
+    """
+    直接对整张图像 resize 到模型输入尺寸，并添加 batch 维度
+    """
+    img_resized = cv2.resize(raw_image, target_size)
+    img_batch = np.expand_dims(img_resized, 0)  # (H, W, C) -> (1, H, W, C)
+    return img_batch
+
+
+# ------------------- 推理函数 -------------------
+def quexian_cls_inference_once(rknn, raw_image, target_size=(640, 640)):
+    """
+    使用已加载的 rknn 实例进行推理
+    返回: (class_id, boolean)
+    """
+    img = preprocess(raw_image, target_size)
+    outputs = rknn.inference([img])
+    output = outputs[0].reshape(-1)
+    class_id = int(np.argmax(output))
+    bool_value = class_id == 1  # 1 表示“有线条”
+    return class_id, bool_value
+
+
+# ------------------- 测试 -------------------
+if __name__ == '__main__':
+    image_path = "./test_image/class1/2.jpg"
+    model_path = "xiantiao_rk3588.rknn"
+
+    bgr_image = cv2.imread(image_path)
+    if bgr_image is None:
+        raise RuntimeError(f"Failed to read image: {image_path}")
+    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
+
+    # 只初始化一次模型
+    rknn_model = init_rknn_model(model_path)
+
+    # 推理
+    class_id, bool_value = quexian_cls_inference_once(rknn_model, rgb_image)
+    print(f"类别ID: {class_id}, 布尔值: {bool_value}")
+    print(f"预测结果: {labels[class_id]}")