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import gradio as gr
import face_recognition
from PIL import Image, ImageDraw
import os
import random
# Path to the data folder containing sample images
DATA_FOLDER = "data/"
# Load sample images from the data folder
sample_images = [os.path.join(DATA_FOLDER, img) for img in os.listdir(DATA_FOLDER) if img.lower().endswith(('png', 'jpg', 'jpeg'))]
def recognize_faces(image_option, uploaded_image):
"""
Perform face recognition on the selected image.
"""
if image_option == "Upload My Image" and uploaded_image is not None:
img = uploaded_image
status = "Processed the uploaded image."
else:
img_path = random.choice(sample_images)
img = Image.open(img_path)
status = f"Processed a sample image: {os.path.basename(img_path)}"
# Convert PIL image to numpy array
img_array = face_recognition.load_image_file(img)
# Find all face locations and face encodings in the image
face_locations = face_recognition.face_locations(img_array)
face_encodings = face_recognition.face_encodings(img_array, face_locations)
# Convert back to PIL image for drawing
pil_image = Image.fromarray(img_array)
draw = ImageDraw.Draw(pil_image)
# Iterate over each face found in the image
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# Draw a box around the face
draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255), width=2)
# Clean up the drawing library
del draw
return pil_image, status
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## Face Recognition Test Run")
with gr.Row():
image_option = gr.Radio(["Upload My Image", "Use Sample Image"], label="Select an Option")
uploaded_image = gr.Image(label="Upload Image (if selected)", type="pil", interactive=True)
submit = gr.Button("Process Image")
output_image = gr.Image(label="Processed Image")
status = gr.Textbox(label="Status")
submit.click(recognize_faces, inputs=[image_option, uploaded_image], outputs=[output_image, status])
if __name__ == "__main__":
demo.launch()
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