| '''
|
| Author: Egrt
|
| Date: 2022-01-04 21:46:25
|
| LastEditors: Egrt
|
| LastEditTime: 2022-01-07 19:49:19
|
| FilePath: \License-super-resolution-master\app.py
|
| '''
|
| from Utilities.io import DataLoader
|
| from Models.RRDBNet import RRDBNet
|
| import numpy as np
|
| import gradio as gr
|
| import cv2
|
| import os
|
| loader = DataLoader()
|
|
|
| MODEL_PATH = 'Pretrained/rrdb'
|
| model = RRDBNet(blockNum=10)
|
| model.load_weights(MODEL_PATH)
|
|
|
|
|
| def inference(file):
|
|
|
| input_image= loader.input_image(file.name)
|
|
|
| input_image= np.expand_dims(input_image, axis=0)
|
| yPred = model.predict(input_image)
|
| yPred = np.squeeze(np.clip(yPred, a_min=0, a_max=1))
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| return yPred
|
|
|
|
|
| title = "车牌超分辨率"
|
| description = "基于生成对抗网络的车牌超分辨率，可从24×12像素的超低分辨率车牌图片恢复到正常可视状态@西南科技大学智能控制与图像处理研究室"
|
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.10257' target='_blank'>SwinIR: Image Restoration Using Swin Transformer</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>"
|
| example_img_dir = 'Samples'
|
| example_img_name = os.listdir(example_img_dir)
|
| examples=[[os.path.join(example_img_dir, image_path)] for image_path in example_img_name if image_path.endswith('.jpg')]
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| gr.Interface(
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| inference,
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| [gr.inputs.Image(type="file", label="Input")],
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| gr.outputs.Image(type="numpy", label="Output"),
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| title=title,
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| description=description,
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| article=article,
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| enable_queue=True,
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| examples=examples
|
| ).launch(debug=True)
|
|
|
