Create one.py

one.py

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+import os
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import ResNet50
+
+# Set the path to the dataset
+data_path = '/content/lfw/'
+
+# Load the dataset
+images = []
+labels = []
+
+for folder_name in os.listdir(data_path):
+    folder_path = os.path.join(data_path, folder_name)
+    if not os.path.isdir(folder_path):
+        continue
+    for file_name in os.listdir(folder_path):
+        file_path = os.path.join(folder_path, file_name)
+        if not file_path.endswith('.jpg'):
+            continue
+        image = np.array(Image.open(file_path).convert('RGB'))
+        label = folder_name
+        images.append(image)
+        labels.append(label)
+
+# Convert to numpy arrays
+images = np.array(images)
+labels = np.array(labels)
+
+# Perform necessary preprocessing on the images
+preprocessed_images = tf.keras.applications.resnet50.preprocess_input(images)
+
+# Obtain a ResNet50 model pre-trained on ImageNet
+model = ResNet50(include_top=False, pooling='avg')
+
+# Extract features from the penultimate layer of the network
+features = model.predict(preprocessed_images)
+
+# Store the features in a dictionary
+features_dict = {}
+for i in range(len(labels)):
+    features_dict[labels[i]] = features[i]
+
+# Use a nearest neighbor algorithm to obtain the 10 most similar images to each query image
+from sklearn.neighbors import NearestNeighbors
+
+# Initialize the nearest neighbor algorithm with cosine distance
+nn = NearestNeighbors(n_neighbors=10, metric='cosine')
+
+# Fit the algorithm to the features
+nn.fit(list(features_dict.values()))
+
+# Define a function to retrieve the most similar images to a query image
+def retrieve_similar_images(query_image_path):
+    # Load the query image
+    query_image = np.array(Image.open(query_image_path).convert('RGB'))
+
+    # Perform necessary preprocessing on the query image
+    preprocessed_query_image = tf.keras.applications.resnet50.preprocess_input(np.array([query_image]))
+
+    # Extract features from the query image
+    query_features = model.predict(preprocessed_query_image)
+
+    # Use the nearest neighbor algorithm to retrieve the most similar images
+    distances, indices = nn.kneighbors(query_features)
+
+    # Display the most similar images
+    for i in range(len(indices[0])):
+        image_path = list(features_dict.keys())[list(features_dict.values()).index(features[indices[0][i]])]
+        image = Image.open(os.path.join(data_path, image_path)).convert('RGB')
+        image.show()