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import gradio as gr |
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import time |
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import numpy as np |
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from scipy.ndimage import gaussian_filter |
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import matplotlib.pyplot as plt |
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from skimage.data import coins |
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from skimage.transform import rescale |
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from sklearn.feature_extraction import image |
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from sklearn.cluster import spectral_clustering |
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orig_coins = coins() |
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smoothened_coins = gaussian_filter(orig_coins, sigma=2) |
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rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect", anti_aliasing=False) |
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graph = image.img_to_graph(rescaled_coins) |
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beta = 10 |
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eps = 1e-6 |
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graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps |
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n_regions = 26 |
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n_regions_plus = 3 |
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def getClusteringPlot(algorithm): |
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t0 = time.time() |
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labels = spectral_clustering( |
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graph, |
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n_clusters=(n_regions + n_regions_plus), |
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eigen_tol=1e-7, |
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assign_labels=algorithm, |
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random_state=42, |
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) |
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t1 = time.time() |
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labels = labels.reshape(rescaled_coins.shape) |
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plt.figure(figsize=(5, 5)) |
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plt.imshow(rescaled_coins, cmap=plt.cm.gray) |
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plt.xticks(()) |
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plt.yticks(()) |
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title = "Spectral clustering: %s, %.2fs" % (algorithm, (t1 - t0)) |
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print(title) |
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plt.title(title) |
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for l in range(n_regions): |
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colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))] |
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plt.contour(labels == l, colors=colors) |
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return plt |
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import gradio as gr |
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def welcome(name): |
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return f"Welcome to Gradio, {name}!" |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# Segmenting the picture of greek coins in regions πͺ |
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An application of spectral clustering. |
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![Image of coins](coins.jpeg "a title") |
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""") |
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inp = gr.Radio(["kmeans", "discretize", "cluster_qr"], label="Solver", info="Choose a clustering algorithm", value="kmeans") |
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plot = gr.Plot(label="Plot") |
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inp.change(getClusteringPlot, inputs=inp, outputs=[plot]) |
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demo.load(getClusteringPlot, inputs=[inp], outputs=[plot]) |
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if __name__ == "main": |
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demo.launch() |