#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#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 os
import cv2
import glob
import numpy as np
from PIL import Image

def tqdm(x):
    return x
import paddle
from ppgan.models.generators import RRDBNet
from ppgan.utils.video import frames2video, video2frames
from ppgan.utils.download import get_path_from_url
from ppgan.utils.logger import get_logger

from .base_predictor import BasePredictor

REALSR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DF2K_JPEG.pdparams'


class RealSRPredictor(BasePredictor):
    def __init__(self, output='output', weight_path=None):
        self.input = input
        self.output = os.path.join(output, 'RealSR')
        self.model = RRDBNet(3, 3, 64, 23)
        if weight_path is None:
            weight_path = get_path_from_url(REALSR_WEIGHT_URL)

        state_dict = paddle.load(weight_path)
        self.model.load_dict(state_dict)
        self.model.eval()

    def norm(self, img):
        img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0
        return img.astype('float32')

    def denorm(self, img):
        img = img.transpose((1, 2, 0))
        return (img * 255).clip(0, 255).astype('uint8')

    def run_image(self, img):
        if isinstance(img, str):
            ori_img = Image.open(img).convert('RGB')
        elif isinstance(img, np.ndarray):
            ori_img = Image.fromarray(img).convert('RGB')
        elif isinstance(img, Image.Image):
            ori_img = img

        img = self.norm(ori_img)
        x = paddle.to_tensor(img[np.newaxis, ...])
        with paddle.no_grad():
            out = self.model(x)

        pred_img = self.denorm(out.numpy()[0])
        pred_img = Image.fromarray(pred_img)
        return pred_img

    def run_video(self, video):
        base_name = os.path.basename(video).split('.')[0]
        output_path = os.path.join(self.output, base_name)
        pred_frame_path = os.path.join(output_path, 'frames_pred')

        if not os.path.exists(output_path):
            os.makedirs(output_path)

        if not os.path.exists(pred_frame_path):
            os.makedirs(pred_frame_path)

        cap = cv2.VideoCapture(video)
        fps = cap.get(cv2.CAP_PROP_FPS)

        out_path = video2frames(video, output_path)

        frames = sorted(glob.glob(os.path.join(out_path, '*.png')))

        for frame in tqdm(frames):
            pred_img = self.run_image(frame)

            frame_name = os.path.basename(frame)
            pred_img.save(os.path.join(pred_frame_path, frame_name))

        frame_pattern_combined = os.path.join(pred_frame_path, '%08d.png')

        vid_out_path = os.path.join(output_path,
                                    '{}_realsr_out.mp4'.format(base_name))
        frames2video(frame_pattern_combined, vid_out_path, str(int(fps)))

        return frame_pattern_combined, vid_out_path

    def run(self, input):
        if not os.path.exists(self.output):
            os.makedirs(self.output)

        if not self.is_image(input):
            return self.run_video(input)
        else:
            pred_img = self.run_image(input)

            out_path = None
            if self.output:
                try:
                    base_name = os.path.splitext(os.path.basename(input))[0]
                except:
                    base_name = 'result'
                out_path = os.path.join(self.output, base_name + '.png')
                pred_img.save(out_path)
                logger = get_logger()
                logger.info('Image saved to {}'.format(out_path))

            return pred_img, out_path