| import os | |
| import gradio as gr | |
| from PIL import Image | |
| os.system( | |
| 'wget https://github.com/TentativeGitHub/SRMNet/releases/download/0.0/AWGN_denoising_SRMNet.pth -P experiments/pretrained_models') | |
| def inference(img): | |
| os.system('mkdir test') | |
| basewidth = 256 | |
| wpercent = (basewidth / float(img.size[0])) | |
| hsize = int((float(img.size[1]) * float(wpercent))) | |
| img = img.resize((basewidth, hsize), Image.ANTIALIAS) | |
| img.save("test/1.png", "PNG") | |
| os.system( | |
| 'python main_test_SRMNet.py --weights experiments/pretrained_models/AWGN_denoising_SRMNet.pth') | |
| return 'result/out.png' | |
| title = "Selective Residual M-Net (SRMNet)" | |
| description = "Gradio demo for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them." | |
| 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>" | |
| examples = [['Noise.png']] | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Image(type="pil", label="Input")], | |
| gr.outputs.Image(type="file", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True, | |
| examples=examples | |
| ).launch(debug=True) |