def __init__(self, feature_extractor, gpu, batch_size):
self.feature_extractor = feature_extractor
self.gpu = gpu
self.batch_size = batch_size
global sess
sess = init_gpu(gpu)
global graph
graph = tf.get_default_graph()
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
if not os.path.exists(os.path.join(model_dir, "mtcnn.p.gz")):
os.makedirs(model_dir, exist_ok=True)
get_file("mtcnn.p.gz",
"http://mirror.cs.uchicago.edu/fawkes/files/mtcnn.p.gz",
cache_dir=model_dir,
cache_subdir='')
self.fs_names = [feature_extractor]
if isinstance(feature_extractor, list):
self.fs_names = feature_extractor
self.aligner = aligner(sess)
self.feature_extractors_ls = [
load_extractor(name) for name in self.fs_names
]
self.protector = None
self.protector_param = None
def __init__(self, original_images, protect_images):
l = int(len(original_images) * 0.7)
self.original_images_test = original_images[l:]
self.protect_images_train = protect_images[:l]
other_classes = range(0, 20946)
selected_classes = random.sample(other_classes, args.num_other_classes)
print("Downloading additional data...")
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
self.id2label = {-1: 0}
self.id2path = {}
self.id2pathtest = {}
idx = 1
for target_data_id in selected_classes:
image_dir = os.path.join(model_dir,
"target_data/{}".format(target_data_id))
os.makedirs(os.path.join(model_dir, "target_data"), exist_ok=True)
os.makedirs(image_dir, exist_ok=True)
self.id2label[target_data_id] = idx
idx += 1
for i in range(10):
if os.path.exists(
os.path.join(
model_dir,
"target_data/{}/{}.jpg".format(target_data_id,
i))):
continue
try:
get_file(
"{}.jpg".format(i),
"http://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg"
.format(target_data_id, i),
cache_dir=model_dir,
cache_subdir='target_data/{}/'.format(target_data_id))
except Exception:
print(
"error getting http://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg"
.format(target_data_id, i))
pass
all_pathes = glob.glob(
os.path.join(model_dir,
'target_data/{}/*.jpg'.format(target_data_id)))
test_path = random.sample(all_pathes, 2)
train_path = [p for p in all_pathes if p not in test_path]
self.id2path[target_data_id] = train_path
self.id2pathtest[target_data_id] = test_path
self.num_classes = 1 + len(self.id2path)
np.random.seed(12345)
self.all_id = selected_classes + [-1]
