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kmean.py
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kmean.py
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import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
NUM_TESTS = 1000
# Load pre-trained VGG16 model
vgg16 = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Initialize features and labels for initialization
init_features = []
init_labels = []
# Add 10 real and 10 fake images for initialization
for i in range(100):
real_path = f'Dataset/Test/Real/real_{i}.jpg'
fake_path = f'Dataset/Test/Fake/fake_{i}.jpg'
real_image = cv2.imread(real_path)
real_image = cv2.resize(real_image, (224, 224))
real_image = np.expand_dims(real_image, axis=0)
real_feature = vgg16.predict(real_image)
init_features.append(real_feature.flatten())
init_labels.append(0) # 0 for real
fake_image = cv2.imread(fake_path)
fake_image = cv2.resize(fake_image, (224, 224))
fake_image = np.expand_dims(fake_image, axis=0)
fake_feature = vgg16.predict(fake_image)
init_features.append(fake_feature.flatten())
init_labels.append(1) # 1 - fake
# Calculating initial cluster centers
init_features = np.stack(init_features)
real_init_center = np.mean(init_features[np.array(init_labels) == 0], axis=0)
fake_init_center = np.mean(init_features[np.array(init_labels) == 1], axis=0)
init_centers = np.stack([real_init_center, fake_init_center])
# Initialize k-means with the initial centers
kmeans = KMeans(n_clusters=2, init=init_centers, n_init=1, random_state=42)
kmeans.fit(init_features)
# Plot the initial clusters
real_cluster = init_features[np.array(init_labels) == 0]
fake_cluster = init_features[np.array(init_labels) == 1]
plt.scatter(real_cluster[:, 0], real_cluster[:, 1], c='r', label='Real')
plt.scatter(fake_cluster[:, 0], fake_cluster[:, 1], c='b', label='Fake')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='k', marker='x', s=100, label='Centroids')
plt.legend()
plt.title('Initial Clusters')
plt.show()
# Classify remaining images based on distance from centroids
features = []
labels = []
for i in range(100, NUM_TESTS):
real_path = f'Dataset/Test/Real/real_{i}.jpg'
fake_path = f'Dataset/Test/Fake/fake_{i}.jpg'
real_image = cv2.imread(real_path)
real_image = cv2.resize(real_image, (224, 224))
real_image = np.expand_dims(real_image, axis=0)
real_feature = vgg16.predict(real_image)
features.append(real_feature.flatten())
labels.append(0) # 0 for real
fake_image = cv2.imread(fake_path)
fake_image = cv2.resize(fake_image, (224, 224))
fake_image = np.expand_dims(fake_image, axis=0)
fake_feature = vgg16.predict(fake_image)
features.append(fake_feature.flatten())
labels.append(1) # 1 for fake
features = np.stack(features)
labels = np.array(labels)
# Classify based on distance from centroids
predictions = kmeans.predict(features)
# Evaluate performance
real_correct = np.sum((predictions == 0) & (labels == 0))
fake_correct = np.sum((predictions == 1) & (labels == 1))
print(f"Correctly classified {real_correct} out of {NUM_TESTS - 10} real images.")
print(f"Correctly classified {fake_correct} out of {NUM_TESTS - 10} fake images.")
# Plot the final clusters
real_cluster = features[np.array(labels) == 0]
fake_cluster = features[np.array(labels) == 1]
plt.figure()
plt.scatter(real_cluster[:, 0], real_cluster[:, 1], c='r', label='Real')
plt.scatter(fake_cluster[:, 0], fake_cluster[:, 1], c='b', label='Fake')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='k', marker='x', s=100, label='Centroids')
plt.legend()
plt.title('Final Clusters')
plt.show()