realsr_predictor.py

#  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
from tqdm import tqdm

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 .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, ...])
        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:
                base_name = os.path.splitext(os.path.basename(input))[0]
                out_path = os.path.join(self.output, base_name + '.png')
                pred_img.save(out_path)

            return pred_img, out_path