| ''' |
| Author: Egrt |
| Date: 2022-01-13 13:34:10 |
| LastEditors: [egrt] |
| LastEditTime: 2022-05-04 12:59:41 |
| FilePath: \MaskGAN\app.py |
| ''' |
| import os |
| os.system('pip install requirements.txt') |
| from PIL import Image |
| from maskgan import MASKGAN |
| import gradio as gr |
| import os |
| maskgan = MASKGAN() |
|
|
| |
| def inference(img): |
| lr_shape = [112, 112] |
| img = img.resize(tuple(lr_shape), Image.BICUBIC) |
| r_image = maskgan.generate_1x1_image(img) |
| return r_image |
|
|
| |
| title = "MaskGAN:融合无监督的口罩遮挡人脸修复" |
| description = "使用生成对抗网络对口罩遮挡人脸进行修复,能够有效的恢复被遮挡区域人脸。 @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'>MaskGAN: Face 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')] |
| 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) |
|
|