AMLGroup4/supervised_model

1import gradio as gr2import tensorflow as tf3import numpy as np4from scipy.spatial.distance import cosine5import cv26import os7 8RECOGNITION_THRESHOLD = 0.39 10# Load the embedding model11embedding_model = tf.keras.models.load_model('v3_embedding_model.h5')12 13# Database to store embeddings and user IDs14user_embeddings = {}15 16# Preprocess the image17def preprocess_image(image):18    image = cv2.resize(image, (375, 375))  # Resize image19    image = tf.keras.applications.resnet50.preprocess_input(image)20    return np.expand_dims(image, axis=0)21 22# Generate embedding23def generate_embedding(image):24    preprocessed_image = preprocess_image(image)25    return embedding_model.predict(preprocessed_image)[0]26 27# Register new user28def register_user(image, user_id):29    try:30        embedding = generate_embedding(image)31        user_embeddings[user_id] = embedding32        return f"User {user_id} registered successfully."33    except Exception as e:34        return f"Error during registration: {str(e)}"35 36# Recognize user37def recognize_user(image):38    try:39        new_embedding = generate_embedding(image)40        min_distance = float('inf')41        recognized_user_id = "Unknown"42        43        for user_id, embedding in user_embeddings.items():44            distance = cosine(new_embedding, embedding)45            print(f"Distance for {user_id}: {distance}")  # Debug: Print distances46            if distance < min_distance:47                min_distance = distance48                recognized_user_id = user_id49        50        print(f"Min distance: {min_distance}")  # Debug: Print minimum distance51        if min_distance > RECOGNITION_THRESHOLD:52            return "User not recognized."53        else:54            return f"Recognized User: {recognized_user_id}"55    except Exception as e:56        return f"Error during recognition: {str(e)}"57        58def main():59    with gr.Blocks() as demo:60        gr.Markdown("Facial Recognition System")61        62        with gr.Tab("Register"):63            with gr.Row():64                img_register = gr.Image()65                user_id = gr.Textbox(label="User ID")66                register_button = gr.Button("Register")67            register_output = gr.Textbox()68            register_button.click(register_user, inputs=[img_register, user_id], outputs=register_output)69 70        with gr.Tab("Recognize"):71            with gr.Row():72                img_recognize = gr.Image()73                recognize_button = gr.Button("Recognize")74            recognize_output = gr.Textbox()75            recognize_button.click(recognize_user, inputs=[img_recognize], outputs=recognize_output)76 77    demo.launch(share=True)78 79if __name__ == "__main__":80    main()