if len(sys.argv) != 3:
print(
"Call this program like this:\n"
" ./face_alignment.py shape_predictor_5_face_landmarks.dat ../examples/faces/bald_guys.jpg\n"
"You can download a trained facial shape predictor from:\n"
" http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n")
exit()
# Load all the models we need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)
# Load the image using Dlib
img = dlib.load_rgb_image(face_file_path)
# Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(img, 1)
num_faces = len(dets)
if num_faces == 0:
print("Sorry, there were no faces found in '{}'".format(face_file_path))
exit()
# Find the 5 face landmarks we need to do the alignment.
faces = dlib.full_object_detections()
for detection in dets:
faces.append(sp(img, detection))
window = dlib.image_window()
# Get the aligned face images
# Optionally:
# images = dlib.get_face_chips(img, faces, size=160, padding=0.25)
images = dlib.get_face_chips(img, faces, size=320)
for image in images:
window.set_image(image)
dlib.hit_enter_to_continue()
# It is also possible to get a single chip
image = dlib.get_face_chip(img, faces[0])
window.set_image(image)
dlib.hit_enter_to_continue()
