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| ''' | |
| Author: Egrt | |
| Date: 2022-01-13 13:34:10 | |
| LastEditors: [egrt] | |
| LastEditTime: 2022-04-08 21:24:36 | |
| FilePath: \LicenseGAN\app.py | |
| ''' | |
| import os | |
| from PIL import Image | |
| from esrgan import ESRGAN | |
| import gradio as gr | |
| import os | |
| esrgan = ESRGAN() | |
| # --------模型推理---------- # | |
| def inference(img): | |
| lr_shape = [32, 56] | |
| img = img.resize((lr_shape[1], lr_shape[0]), Image.BICUBIC) | |
| r_image = esrgan.generate_1x1_image(img) | |
| return r_image | |
| # --------网页信息---------- # | |
| title = "车牌超分辨率重建" | |
| description = "使用生成对抗网络对低分辨率车牌图片进行八倍的超分辨率重建，能够有效的恢复出车牌号。 @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'>LicenseGAN: Image Restoration Using Swin Transformer</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>" | |
| example_img_dir = 'img' | |
| 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', '.png', '.tif'))] | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Image(type="pil", label="Input")], | |
| gr.outputs.Image(type="pil", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True, | |
| examples=examples | |
| ).launch(debug=True) | |