import os
import pdb
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
from rknn.api import RKNN
import yaml
with open('../configs/transform.yml', 'r') as f:
    conf = yaml.load(f, Loader=yaml.FullLoader)
# ONNX_MODEL = 'resnet50v2.onnx'
# RKNN_MODEL = 'resnet50v2.rknn'
ONNX_MODEL = conf['models']['onnx_model']
RKNN_MODEL = conf['models']['rknn_model']


# ONNX_MODEL = 'v3_small_0424.onnx'
# RKNN_MODEL = 'v3_small_0424.rknn'

def show_outputs(outputs):
    # print('***************outputs', outputs)
    output = outputs[0][0]
    # print('len(outputs)',len(output), output)
    output_sorted = sorted(output, reverse=True)
    top5_str = 'resnet50v2\n-----TOP 5-----\n'
    for i in range(5):
        value = output_sorted[i]
        index = np.where(output == value)
        for j in range(len(index)):
            if (i + j) >= 5:
                break
            if value > 0:
                topi = '{}: {}\n'.format(index[j], value)
            else:
                topi = '-1: 0.0\n'
            top5_str += topi
    # pdb.set_trace()
    print(top5_str)


def readable_speed(speed):
    speed_bytes = float(speed)
    speed_kbytes = speed_bytes / 1024
    if speed_kbytes > 1024:
        speed_mbytes = speed_kbytes / 1024
        if speed_mbytes > 1024:
            speed_gbytes = speed_mbytes / 1024
            return "{:.2f} GB/s".format(speed_gbytes)
        else:
            return "{:.2f} MB/s".format(speed_mbytes)
    else:
        return "{:.2f} KB/s".format(speed_kbytes)


def show_progress(blocknum, blocksize, totalsize):
    speed = (blocknum * blocksize) / (time.time() - start_time)
    speed_str = " Speed: {}".format(readable_speed(speed))
    recv_size = blocknum * blocksize

    f = sys.stdout
    progress = (recv_size / totalsize)
    progress_str = "{:.2f}%".format(progress * 100)
    n = round(progress * 50)
    s = ('#' * n).ljust(50, '-')
    f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str)
    f.flush()
    f.write('\r\n')


if __name__ == '__main__':

    # Create RKNN object
    rknn = RKNN(verbose=True)

    # If resnet50v2 does not exist, download it.
    # Download address:
    # https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx
    if not os.path.exists(ONNX_MODEL):
        print('--> Download {}'.format(ONNX_MODEL))
        url = 'https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx'
        download_file = ONNX_MODEL
        try:
            start_time = time.time()
            urllib.request.urlretrieve(url, download_file, show_progress)
        except:
            print('Download {} failed.'.format(download_file))
            print(traceback.format_exc())
            exit(-1)
        print('done')

    # pre-process config
    print('--> config model')
    # rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.82, 58.82, 58.82])
    rknn.config(
        mean_values=[[127.5, 127.5, 127.5]],
        std_values=[[127.5, 127.5, 127.5]],
        target_platform='rk3566',
        model_pruning=False,
        compress_weight=False,
        single_core_mode=True,
        enable_flash_attention=True)
    # rknn.config(
    #     mean_values=[[127.5, 127.5, 127.5]],  # 对于单通道图像，可以设置为 [[127.5]]
    #     std_values=[[127.5, 127.5, 127.5]],  # 对于单通道图像，可以设置为 [[127.5]]
    #     target_platform='rk3588',  # 设置目标平台
    #     # quantize_dtype='int8',
    #     # quantize_algo='normal',
    #     # output_optimize=False,
    #     # output_format='rknnb'
    # )
    print('done')

    # Load model
    print('--> Loading model')
    ret = rknn.load_onnx(model=ONNX_MODEL)
    if ret != 0:
        print('Load model failed!')
        exit(ret)
    print('done')

    # Build model
    print('--> Building model')
    ret = rknn.build(do_quantization=False,  # True
                     dataset='./dataset.txt',
                     rknn_batch_size=conf['models']['rknn_batch_size'])
    # ret = rknn.build(do_quantization=False, dataset='./dataset.txt')
    if ret != 0:
        print('Build model failed!')
        exit(ret)
    print('done')

    # Export rknn model
    print('--> Export rknn model')
    ret = rknn.export_rknn(RKNN_MODEL)
    if ret != 0:
        print('Export rknn model failed!')
        exit(ret)
    print('done')

    # Set inputs
    img = cv2.imread('./dog_224x224.jpg')
    # img = cv2.imread('./data/gift_test/Havegift/20241213-161415-cb8e0762-f376-45d1-8f36-7dc070990fa5/subimg/cam1_9_tid2_fid(18, 33250169482).png')
    # print('img', img)
    # with open('pixel_values.txt', 'w') as file:

    #    for y in range(img.shape[0]):
    #        for x in range(img.shape[1]):
    #            b, g, r = img[y, x]
    #            file.write(f'{r},{g},{b}\n')

    # img = cv2.imread('./810115161912_810115161912_20240131-145622_0da14e4d-a3da-499f-b512-2d4168ab1c87_front_addGood_70f75407b7ae_29_01.jpg')
    img = cv2.resize(img, (224, 224))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # img = conf.test_transform(img)
    # img = img.numpy()
    # img = img.transpose(1, 2, 0)

    # Init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime()
    # ret = rknn.init_runtime('rk3588')
    if ret != 0:
        print('Init runtime environment failed!')
        exit(ret)
    print('done')

    # Inference
    print('--> Running model')
    T1 = time.time()
    outputs = rknn.inference(inputs=[img])
    # outputs = rknn.inference(inputs=img)
    T2 = time.time()
    print('消耗时间 >>> {}'.format(T2 - T1))
    with open('result_0415_128.txt', 'a') as f:
        f.write(str(outputs))
    # pdb.set_trace()
    print('***outputs', outputs)
    np.save('./onnx_resnet50v2_0.npy', outputs[0])
    x = outputs[0]
    output = np.exp(x) / np.sum(np.exp(x))
    outputs = [output]
    show_outputs(outputs)
    print('done')

    rknn.release()