# Copyright (c) 2021 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.
import argparse
import copy
import os

import cv2
import numpy as np
import paddle
from ppgan.utils.config import get_config
from skimage.io import imread
from skimage.transform import rescale
from skimage.transform import resize

import paddlehub as hub
from .model import PhotoPenPredictor
from .util import base64_to_cv2
from paddlehub.module.module import moduleinfo
from paddlehub.module.module import runnable
from paddlehub.module.module import serving


@moduleinfo(
    name="photopen", type="CV/style_transfer", author="paddlepaddle", author_email="", summary="", version="1.0.0")
class Photopen:
    def __init__(self):
        self.pretrained_model = os.path.join(self.directory, "photopen.pdparams")
        cfg = get_config(os.path.join(self.directory, "photopen.yaml"))
        self.network = PhotoPenPredictor(weight_path=self.pretrained_model, gen_cfg=cfg.predict)

    def photo_transfer(self,
                       images: list = None,
                       paths: list = None,
                       output_dir: str = './transfer_result/',
                       use_gpu: bool = False,
                       visualization: bool = True):
        '''
        images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR(read by cv2).
        paths (list[str]): paths to images

        output_dir (str): the dir to save the results
        use_gpu (bool): if True, use gpu to perform the computation, otherwise cpu.
        visualization (bool): if True, save results in output_dir.
        '''
        results = []
        paddle.disable_static()
        place = 'gpu:0' if use_gpu else 'cpu'
        place = paddle.set_device(place)
        if images == None and paths == None:
            print('No image provided. Please input an image or a image path.')
            return

        if images != None:
            for image in images:
                image = image[:, :, ::-1]
                out = self.network.run(image)
                results.append(out)

        if paths != None:
            for path in paths:
                image = cv2.imread(path)[:, :, ::-1]
                out = self.network.run(image)
                results.append(out)

        if visualization == True:
            if not os.path.exists(output_dir):
                os.makedirs(output_dir, exist_ok=True)
            for i, out in enumerate(results):
                if out is not None:
                    cv2.imwrite(os.path.join(output_dir, 'output_{}.png'.format(i)), out[:, :, ::-1])

        return results

    @runnable
    def run_cmd(self, argvs: list):
        """
        Run as a command.
        """
        self.parser = argparse.ArgumentParser(
            description="Run the {} module.".format(self.name),
            prog='hub run {}'.format(self.name),
            usage='%(prog)s',
            add_help=True)

        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
        self.arg_config_group = self.parser.add_argument_group(
            title="Config options", description="Run configuration for controlling module behavior, not required.")
        self.add_module_config_arg()
        self.add_module_input_arg()
        self.args = self.parser.parse_args(argvs)
        results = self.photo_transfer(
            paths=[self.args.input_path],
            output_dir=self.args.output_dir,
            use_gpu=self.args.use_gpu,
            visualization=self.args.visualization)
        return results

    @serving
    def serving_method(self, images, **kwargs):
        """
        Run as a service.
        """
        images_decode = [base64_to_cv2(image) for image in images]
        results = self.photo_transfer(images=images_decode, **kwargs)
        tolist = [result.tolist() for result in results]
        return tolist

    def add_module_config_arg(self):
        """
        Add the command config options.
        """
        self.arg_config_group.add_argument('--use_gpu', action='store_true', help="use GPU or not")

        self.arg_config_group.add_argument(
            '--output_dir', type=str, default='transfer_result', help='output directory for saving result.')
        self.arg_config_group.add_argument('--visualization', type=bool, default=False, help='save results or not.')

    def add_module_input_arg(self):
        """
        Add the command input options.
        """
        self.arg_input_group.add_argument('--input_path', type=str, help="path to input image.")