Merge branch 'main' of https://huggingface.co/spaces/SuwoE/SuperResolution

demo.py

@@ -1,54 +0,0 @@
-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)