Delete main.py

main.py

@@ -1,58 +0,0 @@
-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)