| import gradio as gr | |
| import deepinv as dinv | |
| import torch | |
| import numpy as np | |
| def pil_to_torch(image): | |
| image = np.array(image) | |
| image = image.transpose((2, 0, 1)) | |
| image = torch.tensor(image).float() | |
| return image.unsqueeze(0) | |
| def torch_to_pil(image): | |
| image = image.squeeze(0) | |
| image = image.numpy() | |
| image = image.transpose((1, 2, 0)) | |
| return image | |
| def image_mod(image): | |
| image = pil_to_torch(image) | |
| denoiser = dinv.models.MedianFilter() | |
| noisy = image + torch.randn_like(image) * 0.1 | |
| estimated = denoiser(image, 0.1) | |
| return torch_to_pil(noisy), torch_to_pil(estimated) | |
| input_image = gr.Image(label='Input Image') | |
| output_images = gr.Image(label='Denoised Image') | |
| noise_image = gr.Image(label='Noisy Image') | |
| input_image_output = gr.Image(label='Input Image') | |
| demo = gr.Interface( | |
| image_mod, | |
| inputs=input_image, | |
| outputs=[noise_image, output_images], | |
| title="Image Denoising with DeepInverse", | |
| ) | |
| demo.launch() |