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87 lines
2.8 KiB
87 lines
2.8 KiB
# Copyright (c) 2022 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 ..models.generators import RRDBNet |
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from ..utils.download import get_path_from_url |
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from ..utils.logger import get_logger |
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from .base_predictor import BasePredictor |
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SR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/esrgan_x4.pdparams' |
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class ESRGANPredictor(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, 'ESRGAN') |
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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(SR_WEIGHT_URL) |
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state_dict = paddle.load(weight_path) |
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state_dict = state_dict['generator'] |
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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(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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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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