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import gradio as gr
import glob
import os
from PIL import Image
import numpy as np
from fawkes.protection import Fawkes # Make sure the import path is correct
def run_protection_interface(uploaded_image, gpu='0', mode='low', feature_extractor="arcface_extractor_0",
th=0.01, max_step=1000, sd=1e6, lr=2, batch_size=1, format='png',
separate_target=False, no_align=False, debug=False):
"""
Gradio compatible function for running protection.
"""
if uploaded_image is None:
return None, "No image uploaded."
# Save the uploaded image to a temporary directory
temp_dir = "temp_imgs"
os.makedirs(temp_dir, exist_ok=True)
img_path = os.path.join(temp_dir, "uploaded_image.png")
uploaded_image.save(img_path)
# Run the protection process
protector = Fawkes(feature_extractor, gpu, batch_size, mode=mode)
image_paths = [img_path]
protector.run_protection(image_paths, th=th, sd=sd, lr=lr,
max_step=max_step, batch_size=batch_size,
format=format, separate_target=separate_target,
debug=debug, no_align=no_align)
# Load and return the processed image
processed_img_path = img_path.replace(".png", "_cloaked.png")
if os.path.exists(processed_img_path):
processed_image = Image.open(processed_img_path)
return processed_image, "Protection process completed."
else:
return None, "Protection process failed or no cloaked image generated."
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.Markdown("### Upload Image")
uploaded_image = gr.Image(type="pil", label="Upload Image")
with gr.Column():
gr.Markdown("### Configuration Options")
gpu = gr.Textbox(label="GPU", value='0')
mode = gr.Dropdown(label="Mode", choices=['low', 'mid', 'high'], value='low')
feature_extractor = gr.Textbox(label="Feature Extractor", value="arcface_extractor_0")
th = gr.Slider(label="Threshold", minimum=0.001, maximum=0.05, value=0.01)
max_step = gr.Slider(label="Max Steps", minimum=500, maximum=2000, value=1000)
sd = gr.Slider(label="Penalty Number (SD)", minimum=1e5, maximum=1e7, value=1e6)
lr = gr.Slider(label="Learning Rate", minimum=1, maximum=25, value=2)
batch_size = gr.Slider(label="Batch Size", minimum=1, maximum=10, value=1)
format = gr.Radio(label="Output Format", choices=['png', 'jpg', 'jpeg'])
separate_target = gr.Checkbox(label="Separate Target")
no_align = gr.Checkbox(label="No Align")
debug = gr.Checkbox(label="Debug")
run_button = gr.Button("Run Protection")
output_image = gr.Image(label="Processed Image")
output_text = gr.Textbox(label="Output Message")
run_button.click(
fn=run_protection_interface,
inputs=[uploaded_image, gpu, mode, feature_extractor, th, max_step, sd, lr, batch_size, format,
separate_target, no_align, debug],
outputs=[output_image, output_text]
)
if __name__ == "__main__":
demo.launch()
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