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from fawkes.protection import Fawkes |
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from fawkes.utils import Faces, reverse_process_cloaked |
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from fawkes.differentiator import FawkesMaskGeneration |
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import numpy as np |
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import gradio as gr |
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IMG_SIZE = 112 |
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PREPROCESS = 'raw' |
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def generate_cloak_images(protector, image_X, target_emb=None): |
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cloaked_image_X = protector.compute(image_X, target_emb) |
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return cloaked_image_X |
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def predict(img, level, th=0.04, sd=1e7, lr=10, max_step=500, batch_size=1, format='png', |
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separate_target=True, debug=False, no_align=False, exp="", maximize=True, |
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save_last_on_failed=True): |
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print(img.ndim) |
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fwks = Fawkes("extractor_2", '0', 1, mode=level) |
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current_param = "-".join([str(x) for x in [fwks.th, sd, fwks.lr, fwks.max_step, batch_size, format, |
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separate_target, debug]]) |
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faces = Faces(['./Current Face'], [img], fwks.aligner, verbose=1, no_align=False) |
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original_images = faces.cropped_faces |
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if len(original_images) == 0: |
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print("No face detected. ") |
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return 2 |
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original_images = np.array(original_images) |
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if current_param != fwks.protector_param: |
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fwks.protector_param = current_param |
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if fwks.protector is not None: |
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del fwks.protector |
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if batch_size == -1: |
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batch_size = len(original_images) |
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fwks.protector = FawkesMaskGeneration(fwks.feature_extractors_ls, |
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batch_size=batch_size, |
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mimic_img=True, |
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intensity_range=PREPROCESS, |
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initial_const=sd, |
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learning_rate=fwks.lr, |
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max_iterations=fwks.max_step, |
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l_threshold=fwks.th, |
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verbose=debug, |
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maximize=maximize, |
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keep_final=False, |
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image_shape=(IMG_SIZE, IMG_SIZE, 3), |
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loss_method='features', |
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tanh_process=True, |
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save_last_on_failed=save_last_on_failed, |
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) |
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protected_images = generate_cloak_images(fwks.protector, original_images) |
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faces.cloaked_cropped_faces = protected_images |
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final_images, images_without_face = faces.merge_faces( |
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reverse_process_cloaked(protected_images, preprocess=PREPROCESS), |
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reverse_process_cloaked(original_images, preprocess=PREPROCESS)) |
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return final_images[-1] |
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print("Done!") |
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fwks.run_protection([img], format='jpeg') |
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splt = img.split(".") |
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return splt[0] + "_cloaked.jpeg" |
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gr.Interface(fn=predict, inputs=[gr.components.Image(type='numpy'), |
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gr.components.Radio(["low", "mid", "high"], label="``Protection Level")], |
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outputs=gr.components.Image(type="pil"), allow_flagging="never").launch(show_error=True, quiet=False) |
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