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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
# K-mean Clustering
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Threshold
RECOGNITION_THRESHOLD = 0.1 # Adjust as needed
n_clusters = 5 # You can adjust this based on your data
kmeans = KMeans(n_clusters=n_clusters)
# Load the embedding model
embedding_model = tf.keras.models.load_model('base.h5')
# Database to store embeddings and user IDs
user_embeddings = []
user_ids = []
# 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.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)
# Update the KMeans model
kmeans.fit(user_embeddings)
cluster_label = kmeans.predict([new_embedding])[0]
distances = kmeans.transform([new_embedding])[0]
min_distance = np.min(distances)
if min_distance > RECOGNITION_THRESHOLD:
return "User not recognized."
# Find the user ID(s) in the closest cluster
recognized_user_ids = [user_ids[i] for i, label in enumerate(kmeans.labels_) if label == cluster_label]
return f"Recognized User(s): {', '.join(recognized_user_ids)}"
except Exception as e:
return f"Error during recognition: {str(e)}"
def plot_clusters():
# Assuming embeddings are 2-dimensional
plt.figure(figsize=(8, 6))
plt.scatter(*zip(*user_embeddings), c=kmeans.labels_)
plt.title('User Embeddings Clustered by K-Means')
plt.xlabel('Embedding Dimension 1')
plt.ylabel('Embedding Dimension 2')
plt.show()
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() |