You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
90 lines
3.5 KiB
90 lines
3.5 KiB
# 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 DRNGenerator |
|
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/DRNSx4.pdparams' |
|
|
|
|
|
class DRNPredictor(BasePredictor): |
|
def __init__(self, output='output', weight_path=None): |
|
self.input = input |
|
self.output = os.path.join(output, |
|
'DRN') #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 = DRNGenerator((2, 4)) # 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') / 1.0 |
|
return img.astype('float32') |
|
|
|
# 去标准化 |
|
def denorm(self, img): |
|
img = img.transpose((1, 2, 0)) |
|
return (img * 1).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 |
|
)[2] # 执行预测，DRN模型会输出三个tensor，第一个是原始低分辨率影像，第二个是放大两倍，第三个才是我们所需要的最后的结果 |
|
|
|
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
|
|
|