Create model_optimization.py

model_optimization.py

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+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+import numpy as np
+
+def load_optimized_model():
+    model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    print("Loaded MobileNetV2 model for optimized face detection.")
+    return model
+
+def preprocess_image(image_path):
+    image = load_img(image_path, target_size=(224, 224))
+    image = img_to_array(image)
+    image = image / 255.0  # Normalize image
+    image = np.expand_dims(image, axis=0)
+    return image
+
+# Example usage
+if __name__ == "__main__":
+    model = load_optimized_model()
+    preprocessed_image = preprocess_image("path/to/sample_image.jpg")
+    features = model.predict(preprocessed_image)
+    print("Extracted features:", features)