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- app.py +74 -0
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app.py
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import gradio as gr
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import glob
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import os
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from PIL import Image
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import numpy as np
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from fawkes.protection import Fawkes # Make sure the import path is correct
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def run_protection_interface(uploaded_image, gpu='0', mode='low', feature_extractor="arcface_extractor_0",
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th=0.01, max_step=1000, sd=1e6, lr=2, batch_size=1, format='png',
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separate_target=False, no_align=False, debug=False):
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"""
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Gradio compatible function for running protection.
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"""
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if uploaded_image is None:
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return None, "No image uploaded."
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# Save the uploaded image to a temporary directory
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temp_dir = "temp_imgs"
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os.makedirs(temp_dir, exist_ok=True)
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img_path = os.path.join(temp_dir, "uploaded_image.png")
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uploaded_image.save(img_path)
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# Run the protection process
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protector = Fawkes(feature_extractor, gpu, batch_size, mode=mode)
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image_paths = [img_path]
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protector.run_protection(image_paths, th=th, sd=sd, lr=lr,
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max_step=max_step, batch_size=batch_size,
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format=format, separate_target=separate_target,
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debug=debug, no_align=no_align)
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# Load and return the processed image
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processed_img_path = img_path.replace(".png", "_cloaked.png")
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if os.path.exists(processed_img_path):
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processed_image = Image.open(processed_img_path)
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return processed_image, "Protection process completed."
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else:
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return None, "Protection process failed or no cloaked image generated."
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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gr.Markdown("### Upload Image")
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uploaded_image = gr.Image(type="pil", label="Upload Image")
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with gr.Column():
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gr.Markdown("### Configuration Options")
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gpu = gr.Textbox(label="GPU", value='0')
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mode = gr.Dropdown(label="Mode", choices=['low', 'mid', 'high'], value='low')
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feature_extractor = gr.Textbox(label="Feature Extractor", value="arcface_extractor_0")
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th = gr.Slider(label="Threshold", minimum=0.001, maximum=0.05, value=0.01)
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max_step = gr.Slider(label="Max Steps", minimum=500, maximum=2000, value=1000)
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sd = gr.Slider(label="Penalty Number (SD)", minimum=1e5, maximum=1e7, value=1e6)
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lr = gr.Slider(label="Learning Rate", minimum=1, maximum=25, value=2)
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batch_size = gr.Slider(label="Batch Size", minimum=1, maximum=10, value=1)
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format = gr.Radio(label="Output Format", choices=['png', 'jpg', 'jpeg'])
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separate_target = gr.Checkbox(label="Separate Target")
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no_align = gr.Checkbox(label="No Align")
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debug = gr.Checkbox(label="Debug")
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run_button = gr.Button("Run Protection")
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output_image = gr.Image(label="Processed Image")
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output_text = gr.Textbox(label="Output Message")
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run_button.click(
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fn=run_protection_interface,
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inputs=[uploaded_image, gpu, mode, feature_extractor, th, max_step, sd, lr, batch_size, format,
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separate_target, no_align, debug],
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outputs=[output_image, output_text]
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)
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if __name__ == "__main__":
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demo.launch()
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