app.py

This is an app for Segmenting the picture of greek coins in regions using Spectral Clustering, using one of the following algorithms: kmeans, discrete and QR factorization.

app.py

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+
+# load the coins as a numpy array
+orig_coins = coins()
+
+# Resize it to 20% of the original size to speed up the processing
+# Applying a Gaussian filter for smoothing prior to down-scaling
+# reduces aliasing artifacts.
+smoothened_coins = gaussian_filter(orig_coins, sigma=2)
+rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", anti_aliasing=False)
+
+# Convert the image into a graph with the value of the gradient on the
+# edges.
+graph = image.img_to_graph(rescaled_coins)
+
+# Take a decreasing function of the gradient: an exponential
+# The smaller beta is, the more independent the segmentation is of the
+# actual image. For beta=1, the segmentation is close to a voronoi
+beta = 10
+eps = 1e-6
+graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
+
+# The number of segmented regions to display needs to be chosen manually.
+# The current version of 'spectral_clustering' does not support determining
+# the number of good quality clusters automatically.
+n_regions = 26
+
+# Computing a few extra eigenvectors may speed up the eigen_solver.
+# The spectral clustering quality may also benetif from requesting
+# extra regions for segmentation.
+n_regions_plus = 3
+
+#Function for retrieving the plot
+def getClusteringPlot(algorithm):
+    t0 = time.time()
+    labels = spectral_clustering(
+        graph,
+        n_clusters=(n_regions + n_regions_plus),
+        eigen_tol=1e-7,
+        assign_labels=algorithm,
+        random_state=42,
+    )
+
+    t1 = time.time()
+    labels = labels.reshape(rescaled_coins.shape)
+    plt.figure(figsize=(5, 5))
+    plt.imshow(rescaled_coins, cmap=plt.cm.gray)
+
+    plt.xticks(())
+    plt.yticks(())
+    title = "Spectral clustering: %s, %.2fs" % (algorithm, (t1 - t0))
+    print(title)
+    plt.title(title)
+    for l in range(n_regions):
+        colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))]
+        plt.contour(labels == l, colors=colors)
+        # To view individual segments as appear comment in plt.pause(0.5)
+    return plt
+
+import gradio as gr
+
+def welcome(name):
+    return f"Welcome to Gradio, {name}!"
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # Segmenting the picture of greek coins in regions 🪙
+    An application of spectral clustering.
+    ![Image of coins](coins.jpeg "a title")
+
+    """)
+    inp = gr.Radio(["kmeans", "discretize", "cluster_qr"], label="Solver", info="Choose a clustering algorithm", value="kmeans")
+    plot = gr.Plot(label="Plot")
+    inp.change(getClusteringPlot, inputs=inp, outputs=[plot])
+    demo.load(getClusteringPlot, inputs=[inp], outputs=[plot])
+
+if __name__ = "main":
+    demo.launch()