app.py

import gradio as gr
import deepinv as dinv
import torch
import numpy as np
import PIL.Image


def pil_to_torch(image):
    image = np.array(image)
    image = image.transpose((2, 0, 1))
    image = torch.tensor(image).float() / 255
    return image.unsqueeze(0)


def torch_to_pil(image):
    image = image.squeeze(0).cpu().detach().numpy()
    image = image.transpose((1, 2, 0))
    image = (image * 255).astype(np.uint8)
    image = PIL.Image.fromarray(image)
    return image


def image_mod(image, noise_level, denoiser):
    image = pil_to_torch(image)
    if denoiser == 'DnCNN':
        denoiser = dinv.models.DnCNN()
    elif denoiser == 'MedianFilter':
        denoiser = dinv.models.MedianFilter()
    elif denoiser == 'BM3D':
        denoiser = dinv.models.BM3D()
    elif denoiser == 'DRUNet':
        denoiser = dinv.models.DRUNet()
    else:
        raise ValueError("Invalid denoiser")
    noisy = image + torch.randn_like(image) * noise_level
    estimated = denoiser(image, noise_level)
    return torch_to_pil(noisy), torch_to_pil(estimated)


input_image = gr.Image(label='Input Image', examples=[])
output_images = gr.Image(label='Denoised Image')
noise_image = gr.Image(label='Noisy Image')
input_image_output = gr.Image(label='Input Image')

noise_levels = gr.Dropdown(choices=[0.1, 0.2, 0.3, 0.4, 0.5], value=0.1, label='Noise Level')

denoiser = gr.Dropdown(choices=['DnCNN', 'DRUNet', 'BM3D', 'MedianFilter'], value='DnCNN', label='Denoiser')

demo = gr.Interface(
    image_mod,
    inputs=[input_image, noise_levels, denoiser],
    examples=[['https://deepinv.github.io/deepinv/_static/deepinv_logolarge.png', 0.1, 'DnCNN']],
    outputs=[noise_image, output_images],
    title="Image Denoising with DeepInverse",
    description="Denoise an image using a variety of denoisers and noise levels using the deepinverse library (https://deepinv.github.io/). We only include lightweight models like DnCNN and MedianFilter as this example is intended to be run on a CPU.",
)

demo.launch()