Update app.py

app.py

@@ -1,37 +1,50 @@
 import streamlit as st
-from PIL import Image
+from PIL import Image, ImageEnhance, ImageOps
 import numpy as np
 
-def mock_encoder(image):
-    """Simulates encoding an image into a latent representation."""
-    return np.random.normal(0, 1, (1, 2)), np.random.normal(0, 1, (1, 2)), np.random.normal(0, 1, (1, 2))
-
-def mock_decoder(latent_representation):
-    """Simulates decoding a latent representation back into an image."""
-    return np.random.rand(28, 28, 1) * 255  # Random image for demonstration
+def apply_basic_augmentations(image):
+    """Applies basic augmentations such as rotation and color jitter."""
+    # Rotate the image
+    image = image.rotate(np.random.uniform(-30, 30))
+    
+    # Apply color jitter
+    enhancer = ImageEnhance.Color(image)
+    image = enhancer.enhance(np.random.uniform(0.75, 1.25))
+    
+    # Mirror image randomly
+    if np.random.rand() > 0.5:
+        image = ImageOps.mirror(image)
+    
+    return image
 
-def latent_space_augmentation(image, encoder, decoder, noise_scale=0.1):
-    z_mean, z_log_var, _ = encoder(image)
-    epsilon = np.random.normal(size=z_mean.shape)
-    z_augmented = z_mean + np.exp(0.5 * z_log_var) * epsilon * noise_scale
-    augmented_image = decoder(z_augmented)
-    return np.squeeze(augmented_image)
+def simulate_latent_space_noising(image, noise_scale=25):
+    """Simulates latent space manipulation by adding noise to the image."""
+    # Convert image to numpy array
+    image_array = np.array(image)
+    
+    # Generate noise
+    noise = np.random.normal(0, noise_scale, image_array.shape)
+    
+    # Add noise to image
+    noised_image_array = np.clip(image_array + noise, 0, 255).astype(np.uint8)
+    
+    # Convert back to PIL image
+    noised_image = Image.fromarray(noised_image_array)
+    
+    return noised_image
 
-st.title("VAE-based Image Augmentation Demo")
+st.title("Hybrid Image Augmentation Demo")
 
 uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 if uploaded_image is not None:
-    image = Image.open(uploaded_image).convert('L')
-    image = image.resize((28, 28))  # Resizing for simplicity
+    image = Image.open(uploaded_image)
     st.image(image, caption="Uploaded Image", use_column_width=True)
     
     if st.button("Augment Image"):
-        # Convert PIL image to numpy array
-        image_array = np.array(image) / 255.0  # Normalize the image
-        image_array = image_array.reshape((28, 28, 1))  # Reshape for the mock encoder/decoder
+        # Apply basic transformations
+        transformed_image = apply_basic_augmentations(image)
         
-        # Perform augmentation
-        augmented_image = latent_space_augmentation(image_array, mock_encoder, mock_decoder)
+        # Simulate VAE latent space manipulation by adding noise
+        augmented_image = simulate_latent_space_noising(transformed_image)
         
-        # Display the augmented image
-        st.image(augmented_image, caption="Augmented Image", clamp=True, use_column_width=True)
+        st.image(augmented_image, caption="Augmented Image", use_column_width=True)