Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import streamlit as st
+from skimage import io, color
+from numpy.linalg import norm
+
+def svd_compress(image, k):
+    """Compress the image using SVD by keeping only the top k singular values."""
+    U, S, Vt = np.linalg.svd(image, full_matrices=False)
+    compressed_image = np.dot(U[:, :k], np.dot(np.diag(S[:k]), Vt[:k, :]))
+    return compressed_image
+
+def compute_norms(original, compressed):
+    """Compute different norms to compare image quality."""
+    frobenius_norm = norm(original - compressed, 'fro')
+    l2_norm = norm(original - compressed)
+    max_norm = norm(original - compressed, np.inf)
+    return frobenius_norm, l2_norm, max_norm
+
+def plot_images(original, compressed, k):
+    """Plot original and compressed images side by side."""
+    fig, axes = plt.subplots(1, 2, figsize=(12, 6))
+    axes[0].imshow(original, cmap='gray')
+    axes[0].set_title("Original Image")
+    axes[0].axis('off')
+
+    axes[1].imshow(compressed, cmap='gray')
+    axes[1].set_title(f"Compressed Image (Rank {k})")
+    axes[1].axis('off')
+
+    st.pyplot(fig)
+
+# Streamlit app
+st.title("Image Compression using SVD")
+
+# Upload an image
+uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+if uploaded_file is not None:
+    # Load the image
+    image = io.imread(uploaded_file)
+    gray_image = color.rgb2gray(image)
+
+    # Select compression rank
+    k = st.slider("Select the rank for compression", min_value=1, max_value=min(gray_image.shape), value=50)
+
+    # Compress the image
+    compressed_image = svd_compress(gray_image, k)
+
+    # Compute norms
+    frobenius_norm, l2_norm, max_norm = compute_norms(gray_image, compressed_image)
+
+    # Display norms
+    st.write(f"Frobenius Norm: {frobenius_norm}")
+    st.write(f"L2 Norm: {l2_norm}")
+    st.write(f"Max Norm: {max_norm}")
+
+    # Plot images
+    plot_images(gray_image, compressed_image, k)