app.py

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


# 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()