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]}")