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123 lines
4.0 KiB
123 lines
4.0 KiB
3 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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import os
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import cv2
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import glob
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import numpy as np
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from PIL import Image
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from tqdm import tqdm
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import paddle
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from ppgan.models.generators import RRDBNet
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from ppgan.utils.video import frames2video, video2frames
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from ppgan.utils.download import get_path_from_url
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from ppgan.utils.logger import get_logger
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from .base_predictor import BasePredictor
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REALSR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DF2K_JPEG.pdparams'
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class RealSRPredictor(BasePredictor):
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def __init__(self, output='output', weight_path=None):
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self.input = input
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self.output = os.path.join(output, 'RealSR')
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self.model = RRDBNet(3, 3, 64, 23)
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if weight_path is None:
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weight_path = get_path_from_url(REALSR_WEIGHT_URL)
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state_dict = paddle.load(weight_path)
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self.model.load_dict(state_dict)
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self.model.eval()
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def norm(self, img):
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img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0
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return img.astype('float32')
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def denorm(self, img):
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img = img.transpose((1, 2, 0))
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return (img * 255).clip(0, 255).astype('uint8')
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def run_image(self, img):
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if isinstance(img, str):
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ori_img = Image.open(img).convert('RGB')
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elif isinstance(img, np.ndarray):
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ori_img = Image.fromarray(img).convert('RGB')
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elif isinstance(img, Image.Image):
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ori_img = img
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img = self.norm(ori_img)
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x = paddle.to_tensor(img[np.newaxis, ...])
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with paddle.no_grad():
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out = self.model(x)
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pred_img = self.denorm(out.numpy()[0])
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pred_img = Image.fromarray(pred_img)
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return pred_img
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def run_video(self, video):
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base_name = os.path.basename(video).split('.')[0]
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output_path = os.path.join(self.output, base_name)
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pred_frame_path = os.path.join(output_path, 'frames_pred')
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if not os.path.exists(output_path):
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os.makedirs(output_path)
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if not os.path.exists(pred_frame_path):
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os.makedirs(pred_frame_path)
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cap = cv2.VideoCapture(video)
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fps = cap.get(cv2.CAP_PROP_FPS)
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out_path = video2frames(video, output_path)
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frames = sorted(glob.glob(os.path.join(out_path, '*.png')))
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for frame in tqdm(frames):
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pred_img = self.run_image(frame)
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frame_name = os.path.basename(frame)
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pred_img.save(os.path.join(pred_frame_path, frame_name))
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frame_pattern_combined = os.path.join(pred_frame_path, '%08d.png')
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vid_out_path = os.path.join(output_path,
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'{}_realsr_out.mp4'.format(base_name))
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frames2video(frame_pattern_combined, vid_out_path, str(int(fps)))
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return frame_pattern_combined, vid_out_path
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def run(self, input):
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if not os.path.exists(self.output):
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os.makedirs(self.output)
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if not self.is_image(input):
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return self.run_video(input)
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else:
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pred_img = self.run_image(input)
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out_path = None
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if self.output:
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try:
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base_name = os.path.splitext(os.path.basename(input))[0]
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except:
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base_name = 'result'
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out_path = os.path.join(self.output, base_name + '.png')
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pred_img.save(out_path)
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logger = get_logger()
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logger.info('Image saved to {}'.format(out_path))
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return pred_img, out_path
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