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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
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))
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.")
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])
gr.HTML("<hr>")
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/).")
if __name__ == "__main__":
demo.launch() |