Upload app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+import matplotlib.ticker as ticker
+from io import BytesIO
+from PIL import Image
+
+# -----------------------------
+# Streamlit App
+# -----------------------------
+st.title("🖼️ Image Segmentation using K-Means Clustering")
+
+# Sidebar options
+st.sidebar.header("Controls")
+uploaded_file = st.sidebar.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+max_k = st.sidebar.slider("Max K for Elbow Curve", 5, 15, 10)
+chosen_k = st.sidebar.slider("Choose K (Clusters)", 2, 10, 4)
+
+if uploaded_file is not None:
+    # -----------------------------
+    # Step 1: Load Image
+    # -----------------------------
+    image = Image.open(uploaded_file)
+    image = np.array(image)
+
+    st.subheader("Original Image")
+    st.image(image, use_container_width=True)
+
+    # -----------------------------
+    # Step 2: Preprocess Image
+    # -----------------------------
+    pixels = image.reshape((-1, 3))
+    pixels = np.float32(pixels)
+
+    # -----------------------------
+    # Step 3: Elbow Curve
+    # -----------------------------
+    wcss = []
+    K = range(1, max_k+1)
+    for k in K:
+        kmeans = KMeans(n_clusters=k, random_state=42)
+        kmeans.fit(pixels)
+        wcss.append(kmeans.inertia_)
+
+    # Plot elbow curve
+    fig, ax = plt.subplots(figsize=(6, 4))
+    ax.plot(K, wcss, 'bo-')
+    ax.set_xlabel("Number of clusters (k)")
+    ax.set_ylabel("WCSS (Inertia)")
+    ax.set_title("Elbow Curve")
+
+    # Format y-axis in millions
+    ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{int(x/1e6)}M'))
+
+    # Add vertical dotted line for chosen k
+    ax.axvline(x=chosen_k, color='r', linestyle='--', label=f'k={chosen_k}')
+    ax.legend()
+
+    st.subheader("Elbow Curve")
+    st.pyplot(fig)
+
+    # -----------------------------
+    # Step 4: Apply KMeans with chosen k
+    # -----------------------------
+    kmeans = KMeans(n_clusters=chosen_k, random_state=42)
+    labels = kmeans.fit_predict(pixels)
+
+    segmented_img = kmeans.cluster_centers_[labels]
+    segmented_img = segmented_img.reshape(image.shape)
+    segmented_img = np.uint8(segmented_img)
+
+    # -----------------------------
+    # Step 5: Show Results
+    # -----------------------------
+    st.subheader(f"Segmented Image (k={chosen_k})")
+    st.image(segmented_img, use_container_width=True)
+
+else:
+    st.info("👈 Upload an image from the sidebar to start.")