# Copyright 2021 Huawei Technologies Co., Ltd
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import os
import glob
import argparse

import torch
from PIL import Image
import numpy as np
from torchvision import transforms
from tqdm import tqdm

def tensor2im(input_image, imtype):
    """"Converts a Tensor array into a numpy image array.

    Parameters:
        input_image (tensor) --  the input image tensor array
        imtype (type)        --  the desired type of the converted numpy array
    """
    if not isinstance(input_image, np.ndarray):
        if isinstance(input_image, torch.Tensor):  # get the data from a variable
            image_tensor = input_image.data
        else:
            return input_image
        image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array
        if image_numpy.shape[0] == 1:  # grayscale to RGB
            image_numpy = np.tile(image_numpy, (3, 1, 1))
        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling
    else:  # if it is a numpy array, do nothing
        image_numpy = input_image
    return image_numpy.astype(imtype)


def make_power(img, base, method):
    ow, oh = img.size
    h = int(round(oh / base) * base)
    w = int(round(ow / base) * base)
    if h == oh and w == ow:
        return img
    return img.resize((w, h), method)


def preprocess(PIL_img, image_shape):
    process = transforms.Compose([
        transforms.Lambda(lambda img: make_power(img, base=4, method=Image.BICUBIC)),
        transforms.Resize(image_shape),
        transforms.ToTensor(),
        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
    return process(PIL_img).unsqueeze(dim=0)  # (batch_size, 3, H, W)


def postprocess(img_tensor):
    inv_normalize = transforms.Normalize(
        mean=(-1, -1, -1),
        std=(2.0, 2.0, 2.0))
    to_PIL_image = transforms.ToPILImage()
    return to_PIL_image(inv_normalize(img_tensor[0]).clamp(0, 1))


def bin2img_tensor(bin_src):
    # read bin
    with open(bin_src, 'rb') as f:
        imageBin = f.read()
    # What is stored in the bin file is a half-precision file, so we need to convert
    # the binary to half-precision, and restore the model output shape 1*3*256*256
    img_tensor = torch.tensor(np.reshape(np.frombuffer(imageBin, 'f4'), (1, 3, 256, 256)))
    return img_tensor


def main():
    
    if not os.path.exists(args.bin2img_file):
        os.makedirs(args.bin2img_file)
    all_file = args.npu_bin_file + '/*.bin'
    npu_bin_list = glob.glob(all_file)
    for npu_bin in tqdm(npu_bin_list):
        b2imtensor = bin2img_tensor(npu_bin)
        image_numpy = tensor2im(b2imtensor, np.uint8)
        image_name = npu_bin.split('/')[-1].split('.')[0]+ '.jpg'
        image_save_path = os.path.join(args.bin2img_file, image_name) 
        image_pil = Image.fromarray(image_numpy)
        image_pil.save(image_save_path)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='provide the file path')
    parser.add_argument('--bin2img_file', default='./result/bin2img_bs1/', type=str, help='bin2img_file')
    parser.add_argument('--npu_bin_file', default='./result/bs1', type=str, help='npu_bin_file')
    args = parser.parse_args()
    
    main()