# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



from __future__ import print_function

import sys

sys.path.append('./DG-Net')

from trainer import DGNet_Trainer, to_gray

from utils import get_config

import argparse

from torch.autograd import Variable

import torchvision.utils as vutils

import sys

import torch

import random

import os

import numpy as np

from torchvision import datasets, models, transforms

from PIL import Image

from shutil import copyfile

import tqdm



parser = argparse.ArgumentParser()

parser.add_argument('--output_folder', type=str, default="./bin_path1", help="output image path")

parser.add_argument('--output_folder2', type=str, default="./bin_path2", help="output image path")

parser.add_argument('--input_folder', type=str, default="../Market/pytorch/train_all/", help="input image path")



parser.add_argument('--name', type=str, default="E0.5new_reid0.5_w30000", help="model name")



parser.add_argument('--batchsize', default=1, type=int, help='batchsize')



if __name__ == '__main__':

    opts = parser.parse_args()

    opts.config = './outputs/%s/config.yaml'%opts.name

    config = get_config(opts.config)



    if not os.path.exists(opts.output_folder):

        os.makedirs(opts.output_folder)

    else:

        os.system('rm -rf %s/*'%opts.output_folder)



    if not os.path.exists(opts.output_folder2):

        os.makedirs(opts.output_folder2)

    else:

        os.system('rm -rf %s/*'%opts.output_folder2)



    data_transforms = transforms.Compose([

            transforms.Resize(( config['crop_image_height'], config['crop_image_width']), interpolation=3),

            transforms.ToTensor(),

            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

    ])



    def fliplr(img):

        '''flip horizontal'''

        inv_idx = torch.arange(img.size(3)-1,-1,-1).long()  # N x C x H x W

        img_flip = img.index_select(3,inv_idx)

        return img_flip



    image_datasets = datasets.ImageFolder(opts.input_folder, data_transforms)

    dataloader_content = torch.utils.data.DataLoader(image_datasets, batch_size=opts.batchsize, shuffle=False, pin_memory=True, num_workers=1)

    dataloader_structure = torch.utils.data.DataLoader(image_datasets, batch_size=opts.batchsize, shuffle=True, pin_memory=True, num_workers=1)

    image_paths = image_datasets.imgs



    gray = to_gray(False)

    pbar = tqdm.tqdm(total=len(dataloader_content))

    for data, data2, path in zip(dataloader_content, dataloader_structure, image_paths):

        name = os.path.basename(path[0])

        id_img, label = data

        id_img_flip = Variable(fliplr(id_img).cpu())

        id_img = Variable(id_img.cpu())

        bg_img, label2 = data2

        bg_img = gray(bg_img)

        bg_img = Variable(bg_img.cpu())

        dst_path = opts.output_folder

        dst_path2 = opts.output_folder2

        id_img.numpy().tofile(dst_path + '/%03d_%03d_gan%s.bin'%(label2, label, name[:-4])) 

        bg_img.numpy().tofile(dst_path2 + '/%03d_%03d_gan%s.bin'%(label2, label, name[:-4])) 

        pbar.update(1)



    pbar.close()