demo.py

import numpy as np
import gradio as gr
import torch
import torch.nn as nn
import torch.nn.functional as F
from model import SRCNNModel, pred_SRCNN
from PIL import Image


title = "Super Resolution with CNN"
description = """

Your low resolution image will be reconstructed to high resolution with a scale of 2 with a convolutional neural network!

CNN output on the left, bicubic interpolation output on the right.


"""

article = "Check out the origianl [paper](https://arxiv.org/abs/1501.00092) proposed by Dong *et al*."

# load model
print("Loading  SRCNN model...")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = SRCNNModel().to(device)
model.load_state_dict(torch.load('SRCNNmodel_trained.pt'))
model.eval()
print("SRCNN model loaded!")

def image_grid(imgs, rows, cols):
    '''
    imgs:list of PILImage
    '''
    assert len(imgs) == rows*cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols*w, rows*h))
    grid_w, grid_h = grid.size
    
    for i, img in enumerate(imgs):
        grid.paste(img, box=(i%cols*w, i//cols*h))
    return grid

def sepia(image_path):
    # gradio open image as np array
    image = Image.fromarray(image_path,mode='RGB')
    out_final,image_bicubic,image = pred_SRCNN(model=model,image=image,device=device)
    grid = image_grid([out_final,image_bicubic],1,2)
    return grid

demo = gr.Interface(fn = sepia, inputs=gr.Image(shape=(200, 200)), outputs="image",title=title,description = description,article = article,examples=['LR_image.png','barbara.png'])

demo.launch(share=True)