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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.
""")
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() |