def setup(opts):
shutil.move(
opts['face_detector'],
'deep_privacy/detection/dsfd/weights/WIDERFace_DSFD_RES152.pth')
config = config_parser.load_config('models/default/config.yml')
ckpt = utils.load_checkpoint(opts['checkpoint_dir'])
generator = infer.init_generator(config, ckpt)
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(
generator, 128, use_static_z=True)
return anonymizer
def run_gt(imgf):
# run original DeepPrivacy modified to save results locally
config = load_config("test/models/default/config.yml")
checkpoint = torch.load("test/models/default/checkpoints/step_40000000.ckpt")
generator = infer.init_generator(config, checkpoint)
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(generator,
batch_size=1,
use_static_z=True,
keypoint_threshold=.1,
face_threshold=.6)
anonymizer.anonymize_image_paths([imgf], ["example_anonymized.jpg"])
def anon_and_write_imgs(list_of_path_to_imgs, list_of_path_to_saveimgs):
print('---> anony \t- loading config')
config = load_config("models/large/config.yml")
print('---> anony \t- initializating generator')
generator = infer.init_generator(config, torch.load(path_to_ckpt))
print('---> anony \t- build anonymizer')
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(
generator,
batch_size=32,
use_static_z=True,
keypoint_threshold=.1,
face_threshold=.6)
print('---> anony \t- anonymize')
anonymizer.anonymize_image_paths(list_of_path_to_imgs,
list_of_path_to_saveimgs)
from deep_privacy.inference import deep_privacy_anonymizer, infer
if __name__ == "__main__":
generator, _, source_path, _, target_path, config = infer.read_args(
[{
"name": "anonymize_source",
"default": False
}, {
"name": "max_face_size",
"default": 1.0
}, {
"name": "without_source",
"default": False
}], )
a = deep_privacy_anonymizer.DeepPrivacyAnonymizer(generator,
32,
use_static_z=True,
keypoint_threshold=.1,
face_threshold=.6,
replace_tight_bbox=True)
a.anonymize_video(source_path,
target_path,
start_frame=0,
end_frame=None,
with_keypoints=True,
anonymize_source=config.anonymize_source,
max_face_size=float(config.max_face_size),
without_source=config.without_source)
from deep_privacy.inference import deep_privacy_anonymizer, infer
if __name__ == "__main__":
generator, imsize, source_path, image_paths, save_path = infer.read_args()
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(
generator, 128, use_static_z=True, replace_tight_bbox=True)
anonymizer.anonymize_folder(source_path, save_path)
from deep_privacy.config_parser import load_config
from deep_privacy.inference import infer, deep_privacy_anonymizer
import torch
config = load_config("models/large/config.yml")
checkpoint = torch.load("models/large/checkpoints/step_40000000.ckpt")
generator = infer.init_generator(config, checkpoint)
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(
generator,
batch_size=32,
use_static_z=True,
keypoint_threshold=.1,
face_threshold=.6)
#anonymizer.anonymize_image_paths(["images/demo.jpg"], ["images/demo_anonymized.jpg"])
anonymizer.anonymize_video(["images/portrait.mp4"],
["images/portrait_anon.mp4"])
import numpy as np
import os
import matplotlib.pyplot as plt
from deep_privacy import torch_utils
from deep_privacy.inference import infer, deep_privacy_anonymizer
from deep_privacy.data_tools.dataloaders import load_dataset_files, cut_bounding_box
from deep_privacy.visualization import utils as vis_utils
if __name__ == "__main__":
generator, _, _, _, _ = infer.read_args()
imsize = generator.current_imsize
images, bounding_boxes, landmarks = load_dataset_files("data/fdf_png",
imsize,
load_fraction=True)
batch_size = 128
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(
generator, batch_size, use_static_z=True)
savedir = os.path.join(".debug", "test_examples", "inference_check")
os.makedirs(savedir, exist_ok=True)
num_iterations = 1
ims_to_save = []
percentages = [0]
z = generator.generate_latent_variable(1, "cuda", torch.float32).zero_()
for idx in range(-20, -1):
orig = images[idx]
orig = np.array(orig)
pose = landmarks[idx:idx + 1]
assert orig.dtype == np.uint8
to_save = orig.copy()
to_save = vis_utils.draw_faces_with_keypoints(
from deep_privacy.inference import deep_privacy_anonymizer, infer
if __name__ == "__main__":
generator, imsize, source_path, image_paths, save_path = infer.read_args()
anonymizer = deep_privacy_anonymizer.DeepPrivacyAnonymizer(
generator, 128, use_static_z=True)
anonymizer.anonymize_folder(source_path, save_path)