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import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)


image_directory = r"Image_dataset"

images = []
for filename in os.listdir(image_directory):
    if filename.endswith(".jpg") or filename.endswith(".png"):
        img = cv2.imread(os.path.join(image_directory, filename))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB format
        img = cv2.resize(img, (200, 200))  # Resize to a common size
        images.append(img)

images = np.array(images)

# Flatten the images
n_samples, height, width, n_colors = images.shape
images = images.reshape((n_samples, -1))

# Number of clusters
clusters = 5

# Perform K-means clustering
kmeans = KMeans(clusters,random_state=0)
kmeans.fit(images)

# Get the labels and cluster centroids
labels = kmeans.labels_
cluster_centers = kmeans.cluster_centers_

# Reshape cluster centers into images
cluster_centers_images = cluster_centers.reshape((clusters, height, width, n_colors))

# Plot the cluster centers
plt.figure(figsize=(10, 4))
plt.subplots_adjust(top=1, bottom=0, hspace=0.05, wspace=0.05)
for i in range(clusters):
    plt.subplot(1, clusters, i + 1)
    plt.imshow(cluster_centers_images[i].astype(np.uint8))
    plt.title(f'Cluster {i}')
    plt.axis('off')

plt.show()
warnings.resetwarnings()