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