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import gradio as gr
import cv2
import numpy as np
from face_recognition_system import FaceRecognitionSystem
import os
import tempfile
# Initialize the face recognition system
face_system = FaceRecognitionSystem()
def process_image(image, confidence_threshold=0.5, similarity_threshold=2.0):
"""Process a single image and return the annotated result"""
# Convert from RGB (Gradio) to BGR (OpenCV)
image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Update thresholds
face_system.confidence_threshold = confidence_threshold
face_system.similarity_threshold = similarity_threshold
# Process the frame
processed_frame = face_system.process_frame(image_bgr)
# Convert back to RGB for display
return cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)
def add_face(image, name):
"""Add a new face to the database"""
if not name.strip():
return "Error: Please enter a name"
# Convert from RGB (Gradio) to BGR (OpenCV)
image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if face_system.add_face_to_database(name, image_bgr):
return f"Successfully added {name} to database"
return "Failed to add face to database"
# Create the Gradio interface
with gr.Blocks(title="Face Recognition System") as demo:
gr.Markdown("# Face Recognition System")
gr.Markdown("Upload an image to detect and recognize faces, or add new faces to the database.")
with gr.Tab("Recognize Faces"):
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="numpy")
confidence_slider = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.5,
step=0.1,
label="Confidence Threshold"
)
similarity_slider = gr.Slider(
minimum=0.5,
maximum=5.0,
value=2.0,
step=0.1,
label="Similarity Threshold"
)
detect_btn = gr.Button("Detect Faces")
with gr.Column():
output_image = gr.Image(label="Output Image")
with gr.Tab("Add New Face"):
with gr.Row():
with gr.Column():
new_face_image = gr.Image(label="Face Image", type="numpy")
name_input = gr.Textbox(label="Name")
add_btn = gr.Button("Add Face to Database")
with gr.Column():
result_text = gr.Textbox(label="Result")
# Set up event handlers
detect_btn.click(
fn=process_image,
inputs=[input_image, confidence_slider, similarity_slider],
outputs=output_image
)
add_btn.click(
fn=add_face,
inputs=[new_face_image, name_input],
outputs=result_text
)
# Launch the app
if __name__ == "__main__":
demo.launch() |