Add: basic similar image classification

Python/similar-images-classification/main.py

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+import os
+import numpy as np
+from tensorflow.keras.applications import vgg19
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.vgg19 import preprocess_input
+import faiss
+import cv2
+
+model = vgg19.VGG19(weights="imagenet", include_top=False, pooling="avg")
+
+
+def extract_features(img_path, model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = preprocess_input(img)
+    feature = model.predict(img)
+    return feature.flatten()
+
+
+image_dir = "images/"
+image_paths = [
+    os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".jpg")
+]
+features = []
+
+for image_path in image_paths:
+    img_feature = extract_features(image_path, model)
+    features.append(img_feature)
+
+features = np.array(features)
+
+d = features.shape[1]
+index = faiss.IndexFlatL2(d)
+index.add(features)
+
+
+def find_similar_images(query_image_path, index, k=6):
+    query_feature = extract_features(query_image_path, model).reshape(1, -1)
+    _distance, indices = index.search(query_feature, k)
+    return indices.flatten()
+
+
+query_image_path = "query_image.jpg"
+similar_image_indices = find_similar_images(query_image_path, index)
+
+# display the results
+for idx in similar_image_indices:
+    similar_image_path = image_paths[idx]
+    img = cv2.imread(similar_image_path)
+    cv2.imshow("Similar Image", img)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()