Update app.py

app.py

@@ -1,62 +1,61 @@
 import streamlit as st
-from PIL import Image, ImageEnhance, ImageOps
+from PIL import Image
 import numpy as np
-import os
-import zipfile
 import io
+import zipfile
+
+def mock_encoder(image):
+    """Simulates encoding an image into a latent representation."""
+    # This is a placeholder. In practice, this would be your trained encoder's output.
+    return np.random.normal(0, 1, (1, 100)), np.random.normal(0, 1, (1, 100)), np.random.normal(0, 1, (1, 100))
 
-def apply_basic_augmentations(image):
-    """Applies basic augmentations such as rotation and color jitter."""
-    image = image.rotate(np.random.uniform(-30, 30))
-    enhancer = ImageEnhance.Color(image)
-    image = enhancer.enhance(np.random.uniform(0.75, 1.25))
-    if np.random.rand() > 0.5:
-        image = ImageOps.mirror(image)
-    return image
-
-def simulate_latent_space_noising(image, noise_scale=25):
-    """Simulates latent space manipulation by adding noise."""
-    image_array = np.array(image)
-    noise = np.random.normal(0, noise_scale, image_array.shape)
-    noised_image_array = np.clip(image_array + noise, 0, 255).astype(np.uint8)
-    return Image.fromarray(noised_image_array)
-
-def augment_image(image, augmentations_count):
-    """Generates augmented versions of a single image."""
-    augmented_images = []
-    for _ in range(augmentations_count):
-        augmented_image = apply_basic_augmentations(image)
-        augmented_image = simulate_latent_space_noising(augmented_image)
-        augmented_images.append(augmented_image)
-    return augmented_images
+def mock_decoder(latent_representation):
+    """Simulates decoding a latent representation back into an image."""
+    # Returns a random image for demonstration
+    return np.random.rand(28, 28, 1) * 255
+
+def latent_space_augmentation(image, encoder, decoder, noise_scale=0.1):
+    """Performs latent space augmentation by adding noise to the latent representation."""
+    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 create_downloadable_zip(augmented_images):
     """Creates a ZIP file in memory for downloading."""
     zip_buffer = io.BytesIO()
     with zipfile.ZipFile(zip_buffer, "a", zipfile.ZIP_DEFLATED, False) as zip_file:
-        for idx, image in enumerate(augmented_images):
-            img_byte_arr = io.BytesIO()
-            image.save(img_byte_arr, format="JPEG")
-            zip_file.writestr(f"augmented_image_{idx+1}.jpg", img_byte_arr.getvalue())
+        for idx, image_data in enumerate(augmented_images):
+            img_byte_arr = io.BytesIO(image_data)
+            zip_file.writestr(f"augmented_image_{idx+1}.jpeg", img_byte_arr.getvalue())
     zip_buffer.seek(0)
     return zip_buffer
 
-st.title("Batch Image Augmentation for Dataset Creation")
+st.title("Batch Image Augmentation with Latent Space Manipulation")
 
 uploaded_files = st.file_uploader("Choose images (1-10)", accept_multiple_files=True, type=["jpg", "jpeg", "png"])
 augmentations_count = st.number_input("Number of augmented samples per image", min_value=1, max_value=10, value=3)
 
-if uploaded_files:
+if uploaded_files and st.button("Generate Augmented Images"):
     all_augmented_images = []
     for uploaded_file in uploaded_files:
         image = Image.open(uploaded_file).convert("RGB")
-        augmented_images = augment_image(image, augmentations_count)
-        all_augmented_images.extend(augmented_images)
-
-    if st.button("Download Augmented Dataset"):
+        image = image.resize((28, 28))  # Resize for simplicity with the mock decoder
+
+        # Convert to numpy for processing
+        image_np = np.array(image) / 255.0  # Normalize
+        for _ in range(augmentations_count):
+            augmented_image_np = latent_space_augmentation(image_np, mock_encoder, mock_decoder)
+            augmented_image = (augmented_image_np * 255).astype(np.uint8)  # Denormalize
+            augmented_images_io = io.BytesIO()
+            Image.fromarray(augmented_image).save(augmented_images_io, format="JPEG")
+            all_augmented_images.append(augmented_images_io.getvalue())
+
+    if all_augmented_images:
         zip_buffer = create_downloadable_zip(all_augmented_images)
         st.download_button(
-            label="Download ZIP",
+            label="Download Augmented Dataset",
             data=zip_buffer,
             file_name="augmented_images.zip",
             mime="application/zip"