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import gradio as gr
import tensorflow as tf
import numpy as np
from scipy.spatial.distance import cosine
import cv2
import os
RECOGNITION_THRESHOLD = 0.1
# Load the embedding model
embedding_model = tf.keras.models.load_model('embedding_model.h5')
# Database to store embeddings and user IDs
user_embeddings = {}
# Preprocess the image
def preprocess_image(image):
image = cv2.resize(image, (200, 200)) # Resize image to 200x200
image = tf.keras.applications.resnet50.preprocess_input(image)
return np.expand_dims(image, axis=0)
# Generate embedding
def generate_embedding(image):
preprocessed_image = preprocess_image(image)
return embedding_model.predict(preprocessed_image)[0]
# Register new user
def register_user(image, user_id):
try:
embedding = generate_embedding(image)
user_embeddings[user_id] = embedding
return f"User {user_id} registered successfully."
except Exception as e:
return f"Error during registration: {str(e)}"
# Recognize user
def recognize_user(image):
try:
new_embedding = generate_embedding(image)
min_distance = float('inf')
recognized_user_id = "Unknown"
for user_id, embedding in user_embeddings.items():
distance = cosine(new_embedding, embedding)
print(f"Distance for {user_id}: {distance}") # Debug: Print distances
if distance < min_distance:
min_distance = distance
recognized_user_id = user_id
print(f"Min distance: {min_distance}") # Debug: Print minimum distance
if min_distance > RECOGNITION_THRESHOLD:
return "User not recognized."
else:
return f"Recognized User: {recognized_user_id}"
except Exception as e:
return f"Error during recognition: {str(e)}"
def main():
with gr.Blocks() as demo:
gr.Markdown("Facial Recognition System")
with gr.Tab("Register"):
with gr.Row():
img_register = gr.Image()
user_id = gr.Textbox(label="User ID")
register_button = gr.Button("Register")
register_output = gr.Textbox()
register_button.click(register_user, inputs=[img_register, user_id], outputs=register_output)
with gr.Tab("Recognize"):
with gr.Row():
img_recognize = gr.Image()
recognize_button = gr.Button("Recognize")
recognize_output = gr.Textbox()
recognize_button.click(recognize_user, inputs=[img_recognize], outputs=recognize_output)
demo.launch(share=True)
if __name__ == "__main__":
main() |