# 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.

import cv2

import os

import argparse

import glob

import numpy as np

from PIL import Image

import torch

import torch.nn as nn

from torch.autograd import Variable

from models import ADNet

from utils import *

from collections import OrderedDict

import torch.distributed as dist



parser = argparse.ArgumentParser(description="ADNet_Test")

parser.add_argument("--num_of_layers", type=int, default=17, help="Number of total layers")

parser.add_argument("--logdir", type=str, default="logs", help='path of log files')

parser.add_argument("--test_data", type=str, default='BSD68', help='test on Set12 or Set68')

parser.add_argument("--test_noiseL", type=float, default=25, help='noise level used on test set')

parser.add_argument("--DeviceID", type=int, default=0, help='choose a device id to use')

parser.add_argument("--demo_img_path", type=str, default='demo_img')

parser.add_argument("--demo_pth_path", type=str, default='data')

opt = parser.parse_args()





def normalize(data):

    return data / 255.

def proc_nodes_module(checkpoint):

    new_state_dict = OrderedDict()

    for k, v in checkpoint.items():

        if "module." in k:

            name = k.replace("module.", "")

        else:

            name = k

        new_state_dict[name] = v

    return new_state_dict



def main():

    # Build model

    local_device = torch.device(f'npu:{opt.DeviceID}')

    torch.npu.set_device(local_device)

    print("using npu :{}".format(opt.DeviceID))

    print('Loading model ...\n')

    net = ADNet(channels=1, num_of_layers=17)

    model = net #model = nn.DataParallel(net, device_ids=device_ids).cuda()

    checkpoint = torch.load(os.path.join(opt.demo_pth_path, 'best_model.pth'), map_location=local_device)

    checkpoint = proc_nodes_module(checkpoint)

    model.load_state_dict(checkpoint)

    model = model.npu()

    model.eval()

    # load data info

    print('Loading data info ...\n')

    files_source = glob.glob(os.path.join('data', opt.demo_img_path, '*.png'))

    files_source.sort()

    # process data

    psnr_test = 0

    for f in files_source:

        # image

        Img = cv2.imread(f)

        Img = normalize(np.float32(Img[:, :, 0]))

        Img = np.expand_dims(Img, 0)

        Img = np.expand_dims(Img, 1)

        ISource = torch.Tensor(Img)

        # noise

        torch.manual_seed(0)  # set the seed

        noise = torch.FloatTensor(ISource.size()).normal_(mean=0, std=opt.test_noiseL / 255.)

        # noisy image

        INoisy = ISource + noise

        ISource = Variable(ISource)

        INoisy = Variable(INoisy)

        ISource = ISource.npu()

        INoisy = INoisy.npu()

        with torch.no_grad():  # this can save much memory

            Out = torch.clamp(model(INoisy), 0., 1.)

        psnr = batch_PSNR(Out, ISource, 1.)

        psnr_test += psnr

        print("%s PSNR %f" % (f, psnr))

        INoisy = INoisy*255

        INoisy = INoisy.data.cpu().numpy()

        INoisy = np.squeeze(INoisy)

        Imag_noise = Image.fromarray(INoisy.astype('uint8'))

        if not os.path.exists('./data/demo_img/result'):

            os.mkdir('./data/demo_img/result')

        Imag_noise.save(os.path.join('data', opt.demo_img_path, 'result', 'image_add_noise.png'))

        print('original image stored in:', os.path.join('data', opt.demo_img_path))

        print('image added noise stored in:', os.path.join('data', opt.demo_img_path, 'result', 'image_add_noise.png'))

        result = Out*255

        result = result.data.cpu().numpy()

        result = np.squeeze(result)

        result = Image.fromarray(result.astype('uint8'))

        result.save(os.path.join('data', opt.demo_img_path, 'result', 'image_after_processing.png'))

        print('image denoised stored in:', os.path.join('data', opt.demo_img_path, 'result', 'image_after_processing.png'))

    psnr_test /= len(files_source)

    print("\nPSNR on demo image %f" % psnr_test)





if __name__ == "__main__":

    main()