Upload 2 files

demo_app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import requests
+import os.path
+import torch
+from PIL import Image
+from utils import utils_model
+from utils import utils_image as util
+import numpy as np
+import cv2
+
+st.set_page_config(layout="wide", page_title="Image Denoising Demo")
+
+st.title("Image Real Denoising Demo")
+st.write("The model removes the noise from real world images.")
+st.sidebar.write("## Upload and download :gear:")
+
+# Create the columns
+col1, col2 = st.columns(2)
+
+upfile = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+if upfile is not None:
+    img = Image.open(upfile)
+    img.save('test1.png')
+
+    n_channels = 3
+    img_U = util.imread_uint('test1.png', n_channels=n_channels)
+    #img = Image.open(upfile)
+    col1.write("Original Noisy Image")
+    col1.image(upfile)
+    
+    
+    model_name = 'team15_SAKDNNet'
+   
+    model_path = os.path.join('model_zoo', model_name+'.pth')
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    from models.team15_SAKDNNet import SAKDNNet as net
+    model = net(in_nc=n_channels,config=[4,4,4,4,4,4,4],dim=64)
+
+    model.load_state_dict(torch.load(model_path), strict=True)
+    model.eval()
+    for k, v in model.named_parameters():
+        v.requires_grad = False
+        
+    model = model.to(device)
+    img_N = util.uint2tensor4(img_U)
+    img_N = img_N.to(device)
+    
+    img_DN = utils_model.inference(model, img_N, refield=64, min_size=512, mode=2)
+    img_DN = model(img_N)
+    img_DN = util.tensor2uint(img_DN)
+    
+    col2.write("Denoised Image")
+    col2.image(img_DN)
+    
+    #st.sidebar.markdown("\\n")
+
+    st.write("The method is included in NTIRE 2023 Image Denoising Challenge at CVPR 2023")

requirements.txt

@@ -0,0 +1,6 @@
+torch
+numpy
+opencv-contrib-python
+torchvision
+einops
+timm