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import gradio as gr
import tensorflow as tf
import numpy as np
import cv2
import os
from scipy.spatial.distance import cosine
from keras_facenet import FaceNet
# Load the FaceNet model
def load_facenet_model():
facenet = FaceNet()
model = facenet.model # Access the Keras model in FaceNet
return model
embedding_model = load_facenet_model()
embedding_model.load_weights('facenet_embedding.h5')
# Database to store embeddings and user IDs
user_embeddings = []
user_ids = []
# Threshold
RECOGNITION_THRESHOLD = 0.1 # Adjust as needed
# Preprocess the image for FaceNet
def preprocess_image(image):
image = cv2.resize(image, (160, 160)) # Resize image to 160x160 for FaceNet
image = image.astype('float32')
mean, std = image.mean(), image.std()
image = (image - mean) / std
return np.expand_dims(image, axis=0)
# Generate embedding using FaceNet
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.append(embedding)
user_ids.append(user_id)
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)
closest_user_id = None
closest_distance = float('inf')
for user_id, embedding in zip(user_ids, user_embeddings):
distance = cosine(new_embedding, embedding)
print(f"Distance for {user_id}: {distance}") # Debug: Print distances for each user
if distance < closest_distance:
closest_distance = distance
closest_user_id = user_id
print(f"Min distance: {closest_distance}") # Debug: Print minimum distance
if closest_distance <= RECOGNITION_THRESHOLD:
return f"Recognized User: {closest_user_id}"
else:
return f"User not recognized. Closest Distance: {closest_distance}"
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() |