From 30c646342c205eb3c4f8920480776a2df623f5d0 Mon Sep 17 00:00:00 2001 From: kongdebug <583816984@qq.com> Date: Fri, 18 Mar 2022 12:19:52 +0800 Subject: [PATCH] add ppgan --- paddlers/models/ppgan/apps/__init__.py | 2 + .../models/ppgan/apps/lesrcnn_predictor.py | 85 ++++++++++++++++++ paddlers/models/ppgan/apps/pan_predictor.py | 86 +++++++++++++++++++ 3 files changed, 173 insertions(+) create mode 100644 paddlers/models/ppgan/apps/lesrcnn_predictor.py create mode 100644 paddlers/models/ppgan/apps/pan_predictor.py diff --git a/paddlers/models/ppgan/apps/__init__.py b/paddlers/models/ppgan/apps/__init__.py index 221de5c..c635e42 100644 --- a/paddlers/models/ppgan/apps/__init__.py +++ b/paddlers/models/ppgan/apps/__init__.py @@ -15,3 +15,5 @@ from .realsr_predictor import RealSRPredictor from .mpr_predictor import MPRPredictor from .drn_predictor import DRNPredictor +from .pan_predictor import PANPredictor +from .lesrcnn_predictor import LESRCNNPredictor \ No newline at end of file diff --git a/paddlers/models/ppgan/apps/lesrcnn_predictor.py b/paddlers/models/ppgan/apps/lesrcnn_predictor.py new file mode 100644 index 0000000..651dbaa --- /dev/null +++ b/paddlers/models/ppgan/apps/lesrcnn_predictor.py @@ -0,0 +1,85 @@ +# 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 numpy as np +from PIL import Image + +import paddle +from ppgan.models.generators import LESRCNNGenerator +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/models/lesrcnn_x4.pdparams' + +class LESRCNNPredictor(BasePredictor): + def __init__(self, output='output', weight_path=None): + self.input = input + self.output = os.path.join(output, 'LESRCNN') #定义超分的结果保存的路径，为output路径+模型名所在文件夹 + self.model = LESRCNNGenerator() # 实例化模型 + if weight_path is None: + weight_path = get_path_from_url(REALSR_WEIGHT_URL) + state_dict = paddle.load(weight_path) #加载权重 + state_dict = state_dict['generator'] + self.model.load_dict(state_dict) + self.model.eval() + # 标准化 + def norm(self, img): + img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0 + return img.astype('float32') + # 去标准化 + def denorm(self, img): + img = img.transpose((1, 2, 0)) + return (img * 255).clip(0, 255).astype('uint8') + + # 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 + 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) #图像标准化 + x = paddle.to_tensor(img[np.newaxis, ...]) #转成tensor + with paddle.no_grad(): + out = self.model(x) + + + pred_img = self.denorm(out.numpy()[0]) #tensor转成numpy的array并去标准化 + pred_img = Image.fromarray(pred_img) # array转图像 + return pred_img + + #输入图像文件路径 + def run(self, input): + # 如果输出路径不存在则新建一个 + if not os.path.exists(self.output): + os.makedirs(self.output) + + pred_img = self.run_image(input) #对输入的图片进行预测 + 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') #保存路径 + pred_img.save(out_path) #保存输出图片 + logger = get_logger() + logger.info('Image saved to {}'.format(out_path)) + + return pred_img, out_path \ No newline at end of file diff --git a/paddlers/models/ppgan/apps/pan_predictor.py b/paddlers/models/ppgan/apps/pan_predictor.py new file mode 100644 index 0000000..ddb6b26 --- /dev/null +++ b/paddlers/models/ppgan/apps/pan_predictor.py @@ -0,0 +1,86 @@ +# 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 numpy as np +from PIL import Image + +import paddle +from ppgan.models.generators import PAN +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/models/pan_x4.pdparams' + +class PANPredictor(BasePredictor): + def __init__(self, output='output', weight_path=None): + self.input = input + self.output = os.path.join(output, '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 + self.model = PAN(3, 3, 40, 24, 16) # 实例化模型 + if weight_path is None: + weight_path = get_path_from_url(REALSR_WEIGHT_URL) + state_dict = paddle.load(weight_path) #加载权重 + state_dict = state_dict['generator'] + self.model.load_dict(state_dict) + self.model.eval() + # 标准化 + def norm(self, img): + img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0 + return img.astype('float32') + # 去标准化 + def denorm(self, img): + img = img.transpose((1, 2, 0)) + return (img * 255).clip(0, 255).astype('uint8') + + # 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 + 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) #图像标准化 + x = paddle.to_tensor(img[np.newaxis, ...]) #转成tensor + with paddle.no_grad(): + out = self.model(x) + + + pred_img = self.denorm(out.numpy()[0]) #tensor转成numpy的array并去标准化 + pred_img = Image.fromarray(pred_img) # array转图像 + return pred_img + + #输入图像文件路径 + def run(self, input): + # 如果输出路径不存在则新建一个 + if not os.path.exists(self.output): + os.makedirs(self.output) + + pred_img = self.run_image(input) #对输入的图片进行预测 + 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') #保存路径 + pred_img.save(out_path) #保存输出图片 + logger = get_logger() + logger.info('Image saved to {}'.format(out_path)) + + return pred_img, out_path +