Create app.py

app.py

@@ -0,0 +1,77 @@
+import gradio as gr
+import time
+import numpy as np
+from scipy.ndimage import gaussian_filter
+import matplotlib.pyplot as plt
+from skimage.data import coins
+from skimage.transform import rescale
+from sklearn.feature_extraction import image
+from sklearn.cluster import spectral_clustering
+import gradio as gr
+
+
+# function for making the clustering plot.
+# input: one of the following algorithms: "kmeans", "discretize", "cluster_qr"
+def getClusteringPlot(algorithm):
+    # load the coins as a numpy array
+    orig_coins = coins()
+
+    # Pre-processing the image
+    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 
+    graph = image.img_to_graph(rescaled_coins)
+
+    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
+    n_regions = 26
+
+    # The spectral clustering quality may also benetif from requesting
+    # extra regions for segmentation.
+    n_regions_plus = 3
+    
+    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, "%.3fs" % (t1 - t0))
+
+
+# building the gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Segmenting the picture of Greek coins in regions 🪙")
+    gr.Markdown("This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py).")
+    gr.Markdown("In this demo, we compare three strategies for performing segmentation-clustering and breaking the below image of Greek coins into multiple partly-homogeneous regions.")
+    gr.Image(coins(), label="An image of 24 Greek coins")
+    gr.Markdown("The image is retrieved from scikit-image's data [gallery](https://scikit-image.org/docs/stable/auto_examples/).")
+    inp = gr.Radio(["kmeans", "discretize", "cluster_qr"], label="Solver", info="Choose a clustering algorithm", value="kmeans")
+    with gr.Row():
+        plot = gr.Plot(label="Plot")
+        num = gr.Textbox(label="Running Time")
+    inp.change(getClusteringPlot, inputs=[inp], outputs=[plot, num])
+    demo.load(getClusteringPlot, inputs=[inp], outputs=[plot, num])
+    
+demo.launch()