deep image prior

.github/workflows/python-cicd.yml

@@ -1,4 +1,4 @@
-name: Python CI/CD Workflow
+name: Prettify CI/CD Workflow
 
 on:
   push:
@@ -28,7 +28,6 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r backend/requirements.txt
-
       # # - name: Run Python script
       # #   run: python backend/app.py
 

backend/app.py

@@ -160,4 +160,5 @@ def upload_image():
 
 
 if __name__ == '__main__':
-    app.run(host='localhost', debug=True, port=8000)
+    app.run(host='localhost', debug=True, port=8000)
+    #

backend/dip.py

@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+
+class DIP(nn.Module):
+    def _init_(self, img_path, num_channels=64, num_iterations=5000, learning_rate=0.01, print_interval=100):
+        super()._init_()
+        self.num_channels = num_channels
+        self.num_iterations = num_iterations
+        self.learning_rate = learning_rate
+        self.print_interval = print_interval
+
+        # Load the image
+        self.img = Image.open(img_path)
+        self.transform = transforms.Compose([transforms.ToTensor()])
+        self.img_tensor = self.transform(self.img)
+
+        # Create a random input image
+        self.input_img = torch.randn(self.img_tensor.shape[0], self.num_channels, self.img_tensor.shape[2], self.img_tensor.shape[3])
+
+        # Define the network architecture
+        self.net = nn.Sequential(
+            nn.Conv2d(self.num_channels, self.num_channels, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(self.num_channels, self.num_channels, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(self.num_channels, self.num_channels, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(self.num_channels, self.img_tensor.shape[0], kernel_size=3, stride=1, padding=1)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+    def optimize(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
+
+        for i in range(self.num_iterations):
+            optimizer.zero_grad()
+            output = self.forward(self.input_img)
+            loss = torch.mean((output - self.img_tensor) ** 2)
+            loss.backward()
+            optimizer.step()
+
+            if i % self.print_interval == 0:
+                print('Iteration {}: loss = {}'.format(i, loss.item()))
+
+        # Convert the output image tensor to a PIL image
+        output_img = output.detach().squeeze().permute(1, 2, 0).clamp(0, 1).numpy()
+        output_img = (output_img * 255).astype('uint8')
+        output_img = Image.fromarray(output_img)
+
+        return output_img

backend/requirements.txt

@@ -65,6 +65,7 @@ pycparser==2.21
 Pygments==2.14.0
 PyJWT==2.6.0
 pymongo==4.3.3
+pyngrok==5.2.1
 pyparsing==3.0.9
 pytest==7.2.2
 python-dateutil==2.8.2

backend/test_app.py

@@ -85,4 +85,4 @@ def test_update_user(new_user):
         updated_user = User("updateduser", "[email protected]",'password')
         assert updated_user.name == 'updateduser'
         assert updated_user.email == '[email protected]'
-        assert updated_user.password == 'password'
+        assert updated_user.password == 'password'