import cv2
import numpy as np
import platform
from labels import labels  # 确保这个文件存在
from rknnlite.api import RKNNLite
import time

model_path = '/userdata/reenrr/inference_with_lite/mobilenetv2_640.rknn'
image_path = '/userdata/reenrr/inference_with_lite/222.jpg'
target_size = (640, 640)

# 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
    
# 模型路径配置    
RK3566_RK3568_RKNN_MODEL = 'resnet18_for_rk3566_rk3568.rknn'
RK3588_RKNN_MODEL = model_path
RK3562_RKNN_MODEL = 'resnet18_for_rk3562.rknn'
RK3576_RKNN_MODEL = 'resnet18_for_rk3576.rknn'

def show_top5(result):
    if result is None:
        print("Inference failed: result is None")
        return
        
    output = result[0].reshape(-1)
    # Softmax
    output = np.exp(output) / np.sum(np.exp(output))
    # Get the indices of the top 5 largest values
    output_sorted_indices = np.argsort(output)[::-1][:5]
    top5_str = 'resnet18\n-----TOP 5-----\n'
    for i, index in enumerate(output_sorted_indices):
        value = output[index]
        if value > 0:
            topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index, value, labels[index])
        else:
            topi = '-1: 0.0\n'
        top5_str += topi
    print(top5_str)

if __name__ == '__main__':

    # Get device information
    host_name = get_host()
    if host_name == 'RK3566_RK3568':
        rknn_model = RK3566_RK3568_RKNN_MODEL
    elif host_name == 'RK3562':
        rknn_model = RK3562_RKNN_MODEL
    elif host_name == 'RK3576':
        rknn_model = RK3576_RKNN_MODEL
    elif host_name == 'RK3588':
        rknn_model = RK3588_RKNN_MODEL
    else:
        print("This demo cannot run on the current platform: {}".format(host_name))
        exit(-1)

    rknn_lite = RKNNLite()

    # Load RKNN model
    print('--> Load RKNN model')
    ret = rknn_lite.load_rknn(rknn_model)
    if ret != 0:
        print('Load RKNN model failed')
        exit(ret)
    print('done')

    # 读取并预处理图像 - 这是关键修改部分
    ori_img = cv2.imread(image_path)        
    img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
    # 调整尺寸
    img = cv2.resize(img, target_size)
    img = np.expand_dims(img, 0)  # 添加batch维度

    # Init runtime environment
    print('--> Init runtime environment')
    if host_name in ['RK3576', 'RK3588']:
        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
    else:
        ret = rknn_lite.init_runtime()
    if ret != 0:
        print('Init runtime environment failed')
        exit(ret)
    print('done')

    print("host_name:", host_name)
    print("RKNNLite.NPU_CORE_0:", RKNNLite.NPU_CORE_0)    

    # Inference
    print('--> Running model')
    start_time = time.time()*1000   # 转为毫秒
    outputs = rknn_lite.inference(inputs=[img])
    end_time = time.time()*1000
    print("outputs:", outputs)
    print('Inference completed')
    print("inference_time:", end_time-start_time,"ms")

    # Show the classification results
    show_top5(outputs)

    rknn_lite.release()