def __init__(self, image_paths, loaded_images, aligner, verbose=1, eval_local=False):
self.image_paths = image_paths
self.verbose = verbose
self.aligner = aligner
self.org_faces = []
self.cropped_faces = []
self.cropped_faces_shape = []
self.cropped_index = []
self.callback_idx = []
for i in range(0, len(loaded_images)):
cur_img = loaded_images[i]
p = image_paths[i]
self.org_faces.append(cur_img)
if eval_local:
margin = 0
else:
margin = 0.7
align_img = align(cur_img, self.aligner, margin=margin)
if align_img is None:
print("Find 0 face(s)".format(p.split("/")[-1]))
continue
cur_faces = align_img[0]
cur_shapes = [f.shape[:-1] for f in cur_faces]
cur_faces_square = []
if verbose:
print("Find {} face(s) in {}".format(len(cur_faces), p.split("/")[-1]))
if eval_local:
cur_faces = cur_faces[:1]
for img in cur_faces:
if eval_local:
base = resize(img, (224, 224))
else:
long_size = max([img.shape[1], img.shape[0]])
base = np.zeros((long_size, long_size, 3))
base[0:img.shape[0], 0:img.shape[1], :] = img
cur_faces_square.append(base)
cur_index = align_img[1]
cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square]
self.cropped_faces_shape.extend(cur_shapes)
self.cropped_faces.extend(cur_faces_square)
self.cropped_index.extend(cur_index)
self.callback_idx.extend([i] * len(cur_faces_square))
if len(self.cropped_faces) == 0:
return
self.cropped_faces = np.array(self.cropped_faces)
self.cropped_faces = preprocess(self.cropped_faces, 'imagenet')
self.cloaked_cropped_faces = None
self.cloaked_faces = np.copy(self.org_faces)
def __init__(self, image_paths, loaded_images, aligner, verbose=1, eval_local=False, preprocessing=True,
no_align=False):
self.image_paths = image_paths
self.verbose = verbose
self.no_align = no_align
self.aligner = aligner
self.org_faces = []
self.cropped_faces = []
self.cropped_faces_shape = []
self.cropped_index = []
self.start_end_ls = []
self.callback_idx = []
self.images_without_face = []
for i in range(0, len(loaded_images)):
cur_img = loaded_images[i]
p = image_paths[i]
self.org_faces.append(cur_img)
if not no_align:
align_img = align(cur_img, self.aligner)
if align_img is None:
print("Find 0 face(s) in {}".format(p.split("/")[-1]))
self.images_without_face.append(i)
continue
cur_faces = align_img[0]
else:
cur_faces = [cur_img]
cur_faces = [face for face in cur_faces if face.shape[0] != 0 and face.shape[1] != 0]
cur_shapes = [f.shape[:-1] for f in cur_faces]
cur_faces_square = []
if verbose and not no_align:
print("Find {} face(s) in {}".format(len(cur_faces), p.split("/")[-1]))
if eval_local:
cur_faces = cur_faces[:1]
for img in cur_faces:
if eval_local:
base = resize(img, (IMG_SIZE, IMG_SIZE))
else:
long_size = max([img.shape[1], img.shape[0]])
base = np.ones((long_size, long_size, 3)) * np.mean(img, axis=(0, 1))
start1, end1 = get_ends(long_size, img.shape[0])
start2, end2 = get_ends(long_size, img.shape[1])
base[start1:end1, start2:end2, :] = img
cur_start_end = (start1, end1, start2, end2)
self.start_end_ls.append(cur_start_end)
cur_faces_square.append(base)
cur_faces_square = [resize(f, (IMG_SIZE, IMG_SIZE)) for f in cur_faces_square]
self.cropped_faces.extend(cur_faces_square)
if not self.no_align:
cur_index = align_img[1]
self.cropped_faces_shape.extend(cur_shapes)
self.cropped_index.extend(cur_index[:len(cur_faces_square)])
self.callback_idx.extend([i] * len(cur_faces_square))
if len(self.cropped_faces) == 0:
return
self.cropped_faces = np.array(self.cropped_faces)
if preprocessing:
self.cropped_faces = preprocess(self.cropped_faces, PREPROCESS)
self.cloaked_cropped_faces = None
self.cloaked_faces = np.copy(self.org_faces)
def __init__(self, image_paths, aligner, verbose=1, eval_local=False):
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://sandlab.cs.uchicago.edu/fawkes/files/mtcnn.p.gz",
cache_dir=model_dir,
cache_subdir='')
self.verbose = verbose
self.aligner = aligner
self.org_faces = []
self.cropped_faces = []
self.cropped_faces_shape = []
self.cropped_index = []
self.callback_idx = []
if verbose:
print("Identify {} images".format(len(image_paths)))
for i, p in enumerate(image_paths):
cur_img = load_image(p)
if cur_img is None:
continue
self.org_faces.append(cur_img)
if eval_local:
margin = 0
else:
margin = 0.7
align_img = align(cur_img, self.aligner, margin=margin)
if align_img is None:
print("Find 0 face(s) in {}".format(p.split("/")[-1]))
continue
cur_faces = align_img[0]
cur_shapes = [f.shape[:-1] for f in cur_faces]
cur_faces_square = []
if verbose:
print("Find {} face(s) in {}".format(len(cur_faces),
p.split("/")[-1]))
for img in cur_faces:
if eval_local:
base = resize(img, (224, 224))
else:
long_size = max([img.shape[1], img.shape[0]])
base = np.zeros((long_size, long_size, 3))
base[0:img.shape[0], 0:img.shape[1], :] = img
cur_faces_square.append(base)
cur_index = align_img[1]
cur_faces_square = [
resize(f, (224, 224)) for f in cur_faces_square
]
self.cropped_faces_shape.extend(cur_shapes)
self.cropped_faces.extend(cur_faces_square)
self.cropped_index.extend(cur_index)
self.callback_idx.extend([i] * len(cur_faces_square))
if not self.cropped_faces:
print("No faces detected")
exit(1)
self.cropped_faces = np.array(self.cropped_faces)
self.cropped_faces = preprocess(self.cropped_faces, 'imagenet')
self.cloaked_cropped_faces = None
self.cloaked_faces = np.copy(self.org_faces)
