# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function

import os
import glob
import numpy as np
import argparse

from PIL import Image
from scipy.misc import imsave

import paddle
import paddle.fluid as fluid
from paddle.static import InputSpec as Input

from check import check_gpu, check_version
from cyclegan import Generator, GeneratorCombine


def main():
    place = paddle.set_device(FLAGS.device)
    fluid.enable_dygraph(place) if FLAGS.dynamic else None

    im_shape = [-1, 3, 256, 256]
    input_A = Input(im_shape, 'float32', 'input_A')
    input_B = Input(im_shape, 'float32', 'input_B')

    # Generators
    g_AB = Generator()
    g_BA = Generator()

    g = paddle.Model(
        GeneratorCombine(
            g_AB, g_BA, is_train=False),
        inputs=[input_A, input_B])
    g.prepare()

    g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True)

    out_path = FLAGS.output + "/single"
    if not os.path.exists(out_path):
        os.makedirs(out_path)
    for f in glob.glob(FLAGS.input):
        image_name = os.path.basename(f)
        image = Image.open(f).convert('RGB')
        image = image.resize((256, 256), Image.BICUBIC)
        image = np.array(image) / 127.5 - 1

        image = image[:, :, 0:3].astype("float32")
        data = image.transpose([2, 0, 1])[np.newaxis, :]

        if FLAGS.input_style == "A":
            _, fake, _, _ = g.test_batch([data, data])

        if FLAGS.input_style == "B":
            fake, _, _, _ = g.test_batch([data, data])

        fake = np.squeeze(fake[0]).transpose([1, 2, 0])

        opath = "{}/fake{}{}".format(out_path, FLAGS.input_style, image_name)
        imsave(opath, ((fake + 1) * 127.5).astype(np.uint8))
        print("transfer {} to {}".format(f, opath))


if __name__ == "__main__":
    parser = argparse.ArgumentParser("CycleGAN inference")
    parser.add_argument(
        "-d", "--dynamic", action='store_true', help="Enable dygraph mode")
    parser.add_argument(
        "-p",
        "--device",
        type=str,
        default='gpu',
        help="device to use, gpu or cpu")
    parser.add_argument(
        "-i",
        "--input",
        type=str,
        default='./image/testA/123_A.jpg',
        help="input image")
    parser.add_argument(
        "-o",
        '--output',
        type=str,
        default='output',
        help="The test result to be saved to.")
    parser.add_argument(
        "-m",
        "--init_model",
        type=str,
        default='checkpoint/199',
        help="The init model file of directory.")
    parser.add_argument(
        "-s", "--input_style", type=str, default='A', help="A or B")
    FLAGS = parser.parse_args()
    print(FLAGS)
    check_gpu(str.lower(FLAGS.device) == 'gpu')
    check_version()
    main()