add all

requirements.txt

@@ -0,0 +1,4 @@
+numpy==2.0.2
+matplotlib==3.9.4
+scikit-image==0.24.0
+streamlit==1.41.1

svd_compression.py

@@ -0,0 +1,105 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.io import imread
+from skimage.color import rgb2gray
+from skimage.metrics import structural_similarity as ssim
+import streamlit as st
+from io import BytesIO
+
+def load_image(path, gray=False):
+    img = imread(path)
+    if gray:
+        img = rgb2gray(img)
+    return img / 255.0  # Normalize to [0, 1]
+
+def svd_compression(image, num_singular_values):
+    U, S, V = np.linalg.svd(image, full_matrices=False)
+    U_reduced = U[:, :num_singular_values]
+    S_reduced = np.diag(S[:num_singular_values])
+    V_reduced = V[:num_singular_values, :]
+    compressed_image = np.dot(U_reduced, np.dot(S_reduced, V_reduced))
+    return compressed_image, U, S, V
+
+def svd_compression_rgb(image, num_singular_values):
+    channels = []
+    for i in range(image.shape[2]):  # R, G, B channels
+        compressed_channel, _, _, _ = svd_compression(image[:, :, i], num_singular_values)
+        channels.append(compressed_channel)
+    return np.clip(np.stack(channels, axis=2), 0, 1)
+
+def compute_metrics(original, compressed):
+    mse = np.mean((original - compressed) ** 2)
+    ssim_value = sum(ssim(original[:, :, i], compressed[:, :, i], data_range=compressed[:, :, i].max() - compressed[:, :, i].min()) for i in range(3)) / 3
+    return mse, ssim_value
+
+def visualize_results(original, compressed, num_singular_values, mse, ssim_value):
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6))
+    ax[0].imshow(original)
+    ax[0].set_title("Original Image")
+    ax[0].axis("off")
+    
+    ax[1].imshow(compressed)
+    ax[1].set_title(f"Compressed (k={num_singular_values})\nMSE: {mse:.2f}, SSIM: {ssim_value:.2f}")
+    ax[1].axis("off")
+    
+    plt.tight_layout()
+    plt.show()
+
+# Main script
+# image = load_image('cow.jpg', gray=False) 
+# num_singular_values = 50
+
+# compressed_image = svd_compression_rgb(image, num_singular_values)
+
+# mse, ssim_value = compute_metrics(image, compressed_image)
+# print(f"MSE: {mse:.2f}, SSIM: {ssim_value:.2f}")
+
+# visualize_results(image, compressed_image, num_singular_values, mse, ssim_value)
+
+
+
+def save_image_as_bytes(image):
+    buf = BytesIO()
+    plt.imsave(buf, image, format="jpg", cmap='gray')
+    buf.seek(0)
+    return buf
+
+# Streamlit app code
+st.title("Image Compression using SVD")
+
+# Upload Image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Load the image
+    image = load_image(uploaded_file, gray=False)
+    
+    # Show original image
+    st.image(image, caption="Original Image", use_container_width=True)
+
+    # User input for the number of singular values
+    num_singular_values = st.slider("Select number of singular values (k)", min_value=1, max_value=100, value=50)
+
+    # Perform SVD compression
+    compressed_image = svd_compression_rgb(image, num_singular_values)
+
+    # Compute metrics
+    mse, ssim_value = compute_metrics(image, compressed_image)
+    
+    # Show compressed image
+    st.image(compressed_image, caption=f"Compressed Image (k={num_singular_values})", use_container_width=True)
+    
+    # Display metrics
+    st.write(f"MSE: {mse:.2f}")
+    st.write(f"SSIM: {ssim_value:.2f}")
+
+    # Save compressed image as bytes for download
+    compressed_image_bytes = save_image_as_bytes(compressed_image)
+
+    # Download button for the compressed image
+    st.download_button(
+        label="Download Compressed Image",
+        data=compressed_image_bytes,
+        file_name="compressed_image.png",
+        mime="image/png"
+    )