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'''
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Author: Egrt
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Date: 2022-01-04 21:46:25
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LastEditors: Egrt
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LastEditTime: 2022-01-07 19:49:19
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FilePath: \License-super-resolution-master\app.py
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'''
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from Utilities.io import DataLoader
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from Models.RRDBNet import RRDBNet
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import numpy as np
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import gradio as gr
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import cv2
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import os
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loader = DataLoader()
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MODEL_PATH = 'Pretrained/rrdb'
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model = RRDBNet(blockNum=10)
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model.load_weights(MODEL_PATH)
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def inference(file):
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input_image= loader.input_image(file.name)
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input_image= np.expand_dims(input_image, axis=0)
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yPred = model.predict(input_image)
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yPred = np.squeeze(np.clip(yPred, a_min=0, a_max=1))
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return yPred
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title = "车牌超分辨率"
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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"
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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>"
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example_img_dir = 'Samples'
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example_img_name = os.listdir(example_img_dir)
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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
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).launch(debug=True)
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