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88 lines
3.3 KiB
88 lines
3.3 KiB
# 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 numpy as np |
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from PIL import Image |
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import paddle |
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from ppgan.models.generators import PAN |
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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/models/pan_x4.pdparams' |
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class PANPredictor(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, |
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'PAN') #定义超分的结果保存的路径，为output路径+模型名所在文件夹 |
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self.model = PAN(3, 3, 40, 24, 16) # 实例化模型 |
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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) #加载权重 |
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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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# 标准化 |
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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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# 去标准化 |
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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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# 对图片输入进行预测，输入可以是图像路径，也可以是cv2读取的矩阵，或者PIL读取的图像文件 |
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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) #图像标准化 |
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x = paddle.to_tensor(img[np.newaxis, ...]) #转成tensor |
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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]) #tensor转成numpy的array并去标准化 |
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pred_img = Image.fromarray(pred_img) # array转图像 |
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return pred_img |
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#输入图像文件路径 |
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def run(self, input): |
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# 如果输出路径不存在则新建一个 |
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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) #对输入的图片进行预测 |
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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') #保存路径 |
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pred_img.save(out_path) #保存输出图片 |
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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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