Upload pca_image_compression.py

pca_image_compression.py

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+import streamlit as st
+import numpy as np
+from sklearn.decomposition import PCA
+from PIL import Image
+
+# Add a file uploader to allow image uploads
+uploaded_file = st.file_uploader("Choose an image...", type=['jpg', 'jpeg', 'png'])
+
+def process_display_image(uploaded_file):
+    # Load the image and convert it to grayscale
+    original_img = Image.open(uploaded_file).convert('L')
+    img_array = np.array(original_img)
+
+    # Number of components in the original image
+    original_components = img_array.shape[1]
+
+    # Number of components to keep for PCA
+    n_components = 20  # Set an initial value for the slider
+    n_components = st.sidebar.slider('Number of PCA Components', 1, original_components, n_components)
+
+    # Initialize PCA and apply it to the image data
+    pca = PCA(n_components=n_components)
+    pca.fit(img_array)
+    img_transformed = pca.transform(img_array)
+
+    # Inverse transform to reconstruct the image
+    img_reconstructed = pca.inverse_transform(img_transformed)
+
+    # Normalize the pixel values to be in the range [0, 255]
+    img_reconstructed = np.clip(img_reconstructed, 0, 255)  # Ensures values are within [0, 255]
+    img_reconstructed = img_reconstructed.astype(np.uint8)  # Converts the values to uint8
+
+    # Number of components in the processed image
+    processed_components = pca.n_components_
+
+    # Display the original and the reconstructed images
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.image(img_array, caption=f'Original Image\n{original_components} components', use_column_width=True)
+
+    with col2:
+        st.image(img_reconstructed, caption=f'Reconstructed Image\n{processed_components} components', use_column_width=True)
+
+# Check if an image was uploaded before processing it
+if uploaded_file is not None:
+    process_display_image(uploaded_file)
+else:
+    st.write("Please upload an image to process.")